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msmarco_v2.1_doc_01_1668500655#10_2446916682 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| Here is a list of some of the most popular of these types of data analysis methods: 7. Artificial Neural Networks
No doubt that this is one of the most popular new and modern types of data analysis methods out there. According to http://neuralnetworksanddeeplearning.com ,”Neutral Networks are a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data”
Artificial Neural Networks (ANN), often just called a “neural network”, present a brain metaphor for information processing. These models are biologically inspired computational models. They consist of an interconnected group of artificial neurons and process information using a computation approach. The advanced ANN software solutions are adaptive systems that easily changes its structure based on information that flows through the network. The application of neural networks in data mining is very broad. They have a high acceptance ability for noisy data and high accuracy. Data mining based on neural networks is researched in detail. | 4,945 | 5,996 |
msmarco_v2.1_doc_01_1668500655#11_2446918154 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| They consist of an interconnected group of artificial neurons and process information using a computation approach. The advanced ANN software solutions are adaptive systems that easily changes its structure based on information that flows through the network. The application of neural networks in data mining is very broad. They have a high acceptance ability for noisy data and high accuracy. Data mining based on neural networks is researched in detail. Neural networks have been shown to be very promising systems in many forecasting and business classification applications. 8. Decision Trees
This is another very popular and modern classification algorithm in data mining and machine learning. The decision tree is a tree-shaped diagram that represents a classification or regression model. It divides a data set into smaller and smaller sub-datasets (that contain instances with similar values) while at the same time a related decision tree is continuously developed. | 5,540 | 6,515 |
msmarco_v2.1_doc_01_1668500655#12_2446919530 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| Neural networks have been shown to be very promising systems in many forecasting and business classification applications. 8. Decision Trees
This is another very popular and modern classification algorithm in data mining and machine learning. The decision tree is a tree-shaped diagram that represents a classification or regression model. It divides a data set into smaller and smaller sub-datasets (that contain instances with similar values) while at the same time a related decision tree is continuously developed. The tree is built to show how and why one choice might lead to the next, with the help of the branches. Among the benefits of using decision trees are: domain knowledge is not required; they are easy to comprehend; the classification steps of a decision tree are very simple and fast. | 5,997 | 6,800 |
msmarco_v2.1_doc_01_1668500655#13_2446920734 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| The tree is built to show how and why one choice might lead to the next, with the help of the branches. Among the benefits of using decision trees are: domain knowledge is not required; they are easy to comprehend; the classification steps of a decision tree are very simple and fast. 9. Evolutionary Programming
Evolutionary programming in data mining is a common concept that combines many different types of data analysis using evolutionary algorithms. Most popular of them are: genetic algorithms, genetic programming, and co-evolutionary algorithms. In fact, many data management agencies apply evolutionary algorithms to deal with some of the world’s biggest big-data challenges. | 6,516 | 7,201 |
msmarco_v2.1_doc_01_1668500655#14_2446921825 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| 9. Evolutionary Programming
Evolutionary programming in data mining is a common concept that combines many different types of data analysis using evolutionary algorithms. Most popular of them are: genetic algorithms, genetic programming, and co-evolutionary algorithms. In fact, many data management agencies apply evolutionary algorithms to deal with some of the world’s biggest big-data challenges. Among the benefits of evolutionary methods are: they are a domain independent techniques
they have the ability to explore large search spaces discovering good solutions
they are relatively insensitive to noise
can manage attribute interaction in a great way. 10. Fuzzy Logic
Fuzzy logic is applied to cope with the uncertainty in data mining problems. Fuzzy logic modeling is one of the probability-based data analysis methods and techniques. | 6,800 | 7,644 |
msmarco_v2.1_doc_01_1668500655#15_2446923078 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| Among the benefits of evolutionary methods are: they are a domain independent techniques
they have the ability to explore large search spaces discovering good solutions
they are relatively insensitive to noise
can manage attribute interaction in a great way. 10. Fuzzy Logic
Fuzzy logic is applied to cope with the uncertainty in data mining problems. Fuzzy logic modeling is one of the probability-based data analysis methods and techniques. It is a relatively new field but has great potential for extracting valuable information from different data sets. Fuzzy logic is an innovative type of many-valued logic in which the truth values of variables are a real number between 0 and 1. In this term, the truth value can range between completely true and completely false. Fuzzy logic is applicable when the model contains parameters whose values can not be precisely determined or these values contain too high a level of noise. Download the above infographic in PDF for FREE
Conclusion
The types of data analysis methods are just a part of the whole data management picture that also includes data architecture and modeling, data collection tools, data collection methods, warehousing, data visualization types, data security, data quality metrics and management, data mapping and integration, business intelligence, etc. | 7,201 | 8,525 |
msmarco_v2.1_doc_01_1668500655#16_2446924807 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| It is a relatively new field but has great potential for extracting valuable information from different data sets. Fuzzy logic is an innovative type of many-valued logic in which the truth values of variables are a real number between 0 and 1. In this term, the truth value can range between completely true and completely false. Fuzzy logic is applicable when the model contains parameters whose values can not be precisely determined or these values contain too high a level of noise. Download the above infographic in PDF for FREE
Conclusion
The types of data analysis methods are just a part of the whole data management picture that also includes data architecture and modeling, data collection tools, data collection methods, warehousing, data visualization types, data security, data quality metrics and management, data mapping and integration, business intelligence, etc. What type of data analysis to use? No single data analysis method or technique can be defined as the best technique for data mining. All of them have their role, meaning, advantages, and disadvantages. The selection of methods depends on the particular problem and your data set. Data may be your most valuable tool. | 7,644 | 8,842 |
msmarco_v2.1_doc_01_1668500655#17_2446926406 | http://intellspot.com/types-data-analysis/ | 10 Top Types of Data Analysis Methods and Techniques | 10 Key Types of Data Analysis Methods and Techniques
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| What type of data analysis to use? No single data analysis method or technique can be defined as the best technique for data mining. All of them have their role, meaning, advantages, and disadvantages. The selection of methods depends on the particular problem and your data set. Data may be your most valuable tool. So, choosing the right methods of data analysis might be a crucial point for your overall business development. About The Author
Silvia Valcheva
Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc. Leave a Reply
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msmarco_v2.1_doc_01_1668510492#0_2446927667 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| Types of Graphs and Charts and Their Uses: with Examples and Pics
Types of Graphs and Charts And Their Uses
If you are wondering what are the different types of graphs and charts, their uses and names, this page summarizes them with examples and pictures. As the different kinds of graphs aim to represent data, they are used in many areas such as: in statistics, in data science, in math, in economics, in business and etc. Every type of graph is a visual representation of data on diagram plots (ex. bar, pie, line chart) that show different types of graph trends and relationships between variables. Although it is hard to tell what are all the types of graphs, this page consists all of the common types of statistical graphs and charts (and their meanings) widely used in any science. 1. Line Graphs
A line chart graphically displays data that changes continuously over time. Each line graph consists of points that connect data to show a trend (continuous change). | 0 | 970 |
msmarco_v2.1_doc_01_1668510492#1_2446929026 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| bar, pie, line chart) that show different types of graph trends and relationships between variables. Although it is hard to tell what are all the types of graphs, this page consists all of the common types of statistical graphs and charts (and their meanings) widely used in any science. 1. Line Graphs
A line chart graphically displays data that changes continuously over time. Each line graph consists of points that connect data to show a trend (continuous change). Line graphs have an x-axis and a y-axis. In the most cases, time is distributed on the horizontal axis. Uses of line graphs: When you want to show trends. For example, how house prices have increased over time. | 502 | 1,181 |
msmarco_v2.1_doc_01_1668510492#2_2446930095 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| Line graphs have an x-axis and a y-axis. In the most cases, time is distributed on the horizontal axis. Uses of line graphs: When you want to show trends. For example, how house prices have increased over time. When you want to make predictions based on a data history over time. When comparing two or more different variables, situations, and information over a given period of time. Example: The following line graph shows annual sales of a particular business company for the period of six consecutive years: Note: | 971 | 1,488 |
msmarco_v2.1_doc_01_1668510492#3_2446931001 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| When you want to make predictions based on a data history over time. When comparing two or more different variables, situations, and information over a given period of time. Example: The following line graph shows annual sales of a particular business company for the period of six consecutive years: Note: the above example is with 1 line. However, one line chart can compare multiple trends by several distributing lines. 2. Bar Charts
Bar charts represent categorical data with rectangular bars (to understand what is categorical data see categorical data examples ). Bar graphs are among the most popular types of graphs and charts in economics, statistics, marketing, and visualization in digital customer experience. | 1,181 | 1,904 |
msmarco_v2.1_doc_01_1668510492#4_2446932114 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| the above example is with 1 line. However, one line chart can compare multiple trends by several distributing lines. 2. Bar Charts
Bar charts represent categorical data with rectangular bars (to understand what is categorical data see categorical data examples ). Bar graphs are among the most popular types of graphs and charts in economics, statistics, marketing, and visualization in digital customer experience. They are commonly used to compare several categories of data. Each rectangular bar has length and height proportional to the values that they represent. One axis of the bar chart presents the categories being compared. The other axis shows a measured value. Bar Charts Uses: | 1,489 | 2,179 |
msmarco_v2.1_doc_01_1668510492#5_2446933195 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| They are commonly used to compare several categories of data. Each rectangular bar has length and height proportional to the values that they represent. One axis of the bar chart presents the categories being compared. The other axis shows a measured value. Bar Charts Uses: When you want to display data that are grouped into nominal or ordinal categories (see nominal vs ordinal data ). To compare data among different categories. Bar charts can also show large data changes over time. Bar charts are ideal for visualizing the distribution of data when we have more than three categories. Example: | 1,905 | 2,504 |
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| When you want to display data that are grouped into nominal or ordinal categories (see nominal vs ordinal data ). To compare data among different categories. Bar charts can also show large data changes over time. Bar charts are ideal for visualizing the distribution of data when we have more than three categories. Example: The bar chart below represents the total sum of sales for Product A and Product B over three years. The bars are 2 types: vertical or horizontal. It doesn’t matter which kind you will use. The above one is a vertical type. | 2,179 | 2,727 |
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| The bar chart below represents the total sum of sales for Product A and Product B over three years. The bars are 2 types: vertical or horizontal. It doesn’t matter which kind you will use. The above one is a vertical type. 3. Pie Charts
When it comes to statistical types of graphs and charts, the pie chart (or the circle chart) has a crucial place and meaning. It displays data and statistics in an easy-to-understand ‘pie-slice’ format and illustrates numerical proportion. Each pie slice is relative to the size of a particular category in a given group as a whole. To say it in another way, the pie chart brakes down a group into smaller pieces. | 2,504 | 3,155 |
msmarco_v2.1_doc_01_1668510492#8_2446936182 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| 3. Pie Charts
When it comes to statistical types of graphs and charts, the pie chart (or the circle chart) has a crucial place and meaning. It displays data and statistics in an easy-to-understand ‘pie-slice’ format and illustrates numerical proportion. Each pie slice is relative to the size of a particular category in a given group as a whole. To say it in another way, the pie chart brakes down a group into smaller pieces. It shows part-whole relationships. To make a pie chart, you need a list of categorical variables and numerical variables. Pie Chart Uses: When you want to create and represent the composition of something. It is very useful for displaying nominal or ordinal categories of data. | 2,727 | 3,433 |
msmarco_v2.1_doc_01_1668510492#9_2446937288 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| It shows part-whole relationships. To make a pie chart, you need a list of categorical variables and numerical variables. Pie Chart Uses: When you want to create and represent the composition of something. It is very useful for displaying nominal or ordinal categories of data. To show percentage or proportional data. When comparing areas of growth within a business such as profit. Pie charts work best for displaying data for 3 to 7 categories. Example: The pie chart below represents the proportion of types of transportation used by 1000 students to go to their school. | 3,156 | 3,730 |
msmarco_v2.1_doc_01_1668510492#10_2446938252 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| To show percentage or proportional data. When comparing areas of growth within a business such as profit. Pie charts work best for displaying data for 3 to 7 categories. Example: The pie chart below represents the proportion of types of transportation used by 1000 students to go to their school. Pie charts are widely used by data-driven marketers for displaying marketing data. 4. Histogram
A histogram shows continuous data in ordered rectangular columns (to understand what is continuous data see our post discrete vs continuous data ). Usually, there are no gaps between the columns. The histogram displays a frequency distribution (shape) of a data set. | 3,433 | 4,093 |
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| Pie charts are widely used by data-driven marketers for displaying marketing data. 4. Histogram
A histogram shows continuous data in ordered rectangular columns (to understand what is continuous data see our post discrete vs continuous data ). Usually, there are no gaps between the columns. The histogram displays a frequency distribution (shape) of a data set. At first glance, histograms look alike to bar graphs. However, there is a key difference between them. Bar Chart represents categorical data and histogram represent continuous data. Histogram Uses: When the data is continuous. | 3,730 | 4,320 |
msmarco_v2.1_doc_01_1668510492#12_2446940284 | http://intellspot.com/types-graphs-charts/ | Types of Graphs and Charts and Their Uses: with Examples and Pics | Types of Graphs and Charts And Their Uses
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| At first glance, histograms look alike to bar graphs. However, there is a key difference between them. Bar Chart represents categorical data and histogram represent continuous data. Histogram Uses: When the data is continuous. When you want to represent the shape of the data’s distribution. When you want to see whether the outputs of two or more processes are different. To summarize large data sets graphically. To communicate the data distribution quickly to others. Example: | 4,094 | 4,573 |
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| When you want to represent the shape of the data’s distribution. When you want to see whether the outputs of two or more processes are different. To summarize large data sets graphically. To communicate the data distribution quickly to others. Example: The histogram below represents per capita income for five age groups. Histograms are very widely used in statistics, business, and economics. 5. Scatter plot
The scatter plot is an X-Y diagram that shows a relationship between two variables. It is used to plot data points on a vertical and a horizontal axis. | 4,320 | 4,883 |
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| The histogram below represents per capita income for five age groups. Histograms are very widely used in statistics, business, and economics. 5. Scatter plot
The scatter plot is an X-Y diagram that shows a relationship between two variables. It is used to plot data points on a vertical and a horizontal axis. The purpose is to show how much one variable affects another. Usually, when there is a relationship between 2 variables, the first one is called independent. The second variable is called dependent because its values depend on the first variable. Scatter plots also help you predict the behavior of one variable (dependent) based on the measure of the other variable (independent). Scatter plot uses: | 4,573 | 5,284 |
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| The purpose is to show how much one variable affects another. Usually, when there is a relationship between 2 variables, the first one is called independent. The second variable is called dependent because its values depend on the first variable. Scatter plots also help you predict the behavior of one variable (dependent) based on the measure of the other variable (independent). Scatter plot uses: When trying to find out whether there is a relationship between 2 variables. To predict the behavior of dependent variable based on the measure of the independent variable. When having paired numerical data. When working with root cause analysis tools to identify the potential for problems. When you just want to visualize the correlation between 2 large datasets without regard to time. | 4,884 | 5,673 |
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| When trying to find out whether there is a relationship between 2 variables. To predict the behavior of dependent variable based on the measure of the independent variable. When having paired numerical data. When working with root cause analysis tools to identify the potential for problems. When you just want to visualize the correlation between 2 large datasets without regard to time. Example: The below Scatter plot presents data for 7 online stores, their monthly e-commerce sales, and online advertising costs for the last year. The orange line you see in the plot is called “line of best fit” or a “trend line”. This line is used to help us make predictions that are based on past data. The Scatter plots are used widely in data science and statistics. | 5,284 | 6,045 |
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| Example: The below Scatter plot presents data for 7 online stores, their monthly e-commerce sales, and online advertising costs for the last year. The orange line you see in the plot is called “line of best fit” or a “trend line”. This line is used to help us make predictions that are based on past data. The Scatter plots are used widely in data science and statistics. They are a great tool for visualizing linear regression models. More examples and explanation for scatter plots you can see in our post what does a scatter plot show and simple linear regression examples. 6. Venn Chart
Venn Diagram (also called primary diagram, set diagram or logic diagrams) uses overlapping circles to visualize the logical relationships between two or more group of items. Venn Diagram is one of the types of graphs and charts used in scientific and engineering presentations, in computer applications, in maths, and in statistics. | 5,673 | 6,597 |
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| They are a great tool for visualizing linear regression models. More examples and explanation for scatter plots you can see in our post what does a scatter plot show and simple linear regression examples. 6. Venn Chart
Venn Diagram (also called primary diagram, set diagram or logic diagrams) uses overlapping circles to visualize the logical relationships between two or more group of items. Venn Diagram is one of the types of graphs and charts used in scientific and engineering presentations, in computer applications, in maths, and in statistics. The basic structure of the Venn diagram is usually overlapping circles. The items in the overlapping section have specific common characteristics. Items in the outer portions of the circles do not have common traits. Venn Chart Uses: When you want to compare and contrast groups of things. | 6,046 | 6,887 |
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| The basic structure of the Venn diagram is usually overlapping circles. The items in the overlapping section have specific common characteristics. Items in the outer portions of the circles do not have common traits. Venn Chart Uses: When you want to compare and contrast groups of things. To categorize or group items. To illustrate logical relationships from various datasets. To identify all the possible relationships between collections of datasets. Example: The following science example of Venn diagram compares the features of birds and bats. | 6,597 | 7,148 |
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| To categorize or group items. To illustrate logical relationships from various datasets. To identify all the possible relationships between collections of datasets. Example: The following science example of Venn diagram compares the features of birds and bats. 7. Area Charts
Area charts show the change in one or several quantities over time. They are very similar to the line chart. However, the area between axis and line are usually filled with colors. Despite line and area charts support the same type of analysis, they cannot be always used interchangeably. | 6,887 | 7,452 |
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| 7. Area Charts
Area charts show the change in one or several quantities over time. They are very similar to the line chart. However, the area between axis and line are usually filled with colors. Despite line and area charts support the same type of analysis, they cannot be always used interchangeably. Line charts are often used to represent multiple data sets. Area charts cannot show multiple data sets clearly because area charts show a filled area below the line. Area Chart Uses: When you want to show trends, rather than express specific values. To show a simple comparison of the trend of data sets over the period of time. | 7,148 | 7,781 |
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| Line charts are often used to represent multiple data sets. Area charts cannot show multiple data sets clearly because area charts show a filled area below the line. Area Chart Uses: When you want to show trends, rather than express specific values. To show a simple comparison of the trend of data sets over the period of time. To display the magnitude of a change. To compare a small number of categories. The area chart has 2 variants: a variant with data plots overlapping each other and a variant with data plots stacked on top of each other (known as stacked area chart – as the shown in the following example). Example: | 7,453 | 8,079 |
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| To display the magnitude of a change. To compare a small number of categories. The area chart has 2 variants: a variant with data plots overlapping each other and a variant with data plots stacked on top of each other (known as stacked area chart – as the shown in the following example). Example: The area chart below shows quarterly sales for product categories A and B for the last year. This area chart shows you a quick comparison of the trend in the quarterly sales of Product A and Product B over the period of the last year. 8. Spline Chart
The Spline Chart is one of the most widespread types of graphs and charts used in statistics. It is a form of the line chart that represent smooth curves through the different data points. | 7,781 | 8,519 |
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| The area chart below shows quarterly sales for product categories A and B for the last year. This area chart shows you a quick comparison of the trend in the quarterly sales of Product A and Product B over the period of the last year. 8. Spline Chart
The Spline Chart is one of the most widespread types of graphs and charts used in statistics. It is a form of the line chart that represent smooth curves through the different data points. Spline charts possess all the characteristics of a line chart except that spline charts have a fitted curved line to join the data points. In comparison, line charts connect data points with straight lines. Spline Chart Uses: When you want to plot data that requires the usage of curve-fitting such as a product lifecycle chart or an impulse-response chart. Spline charts are often used in designing Pareto charts. | 8,079 | 8,934 |
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| Spline charts possess all the characteristics of a line chart except that spline charts have a fitted curved line to join the data points. In comparison, line charts connect data points with straight lines. Spline Chart Uses: When you want to plot data that requires the usage of curve-fitting such as a product lifecycle chart or an impulse-response chart. Spline charts are often used in designing Pareto charts. Spline chart also is often used for data modeling by when you have limited number of data points and estimating the intervening values. Example: The following spline chart example shows sales of a company through several months of a year: 9. Box and Whisker Chart
A box and whisker chart is a statistical graph for displaying sets of numerical data through their quartiles. | 8,519 | 9,308 |
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| Spline chart also is often used for data modeling by when you have limited number of data points and estimating the intervening values. Example: The following spline chart example shows sales of a company through several months of a year: 9. Box and Whisker Chart
A box and whisker chart is a statistical graph for displaying sets of numerical data through their quartiles. It displays a frequency distribution of the data. The box and whisker chart helps you to display the spread and skewness for a given set of data using the five number summary principle: minimum, maximum, median, lower and upper quartiles. The ‘five-number summary’ principle allows providing a statistical summary for a particular set of numbers. It shows you the range (minimum and maximum numbers), the spread (upper and lower quartiles), and the center (median) for the set of data numbers. | 8,934 | 9,802 |
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| It displays a frequency distribution of the data. The box and whisker chart helps you to display the spread and skewness for a given set of data using the five number summary principle: minimum, maximum, median, lower and upper quartiles. The ‘five-number summary’ principle allows providing a statistical summary for a particular set of numbers. It shows you the range (minimum and maximum numbers), the spread (upper and lower quartiles), and the center (median) for the set of data numbers. A very simple figure of a box and whisker plot you can see below: Box and Whisker Chart Uses: When you want to observe the upper, lower quartiles, mean, median, deviations, etc. for a large set of | 9,309 | 9,999 |
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| Types of Sampling Methods in Research: Briefly Explained
Types of Sampling Methods and Techniques in Research
The main goal of any marketing or statistical research is to provide quality results that are a reliable basis for decision-making. That is why the different types of sampling methods and techniques have a crucial role in research methodology and statistics. Your sample is one of the key factors that determine if your findings are accurate. Making the research with the wrong sample designs, you will almost surely get various misleading results. On this page you will learn: What is sampling? The various types of sampling methods: briefly explained. Probability and non-probability sampling. | 0 | 705 |
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| On this page you will learn: What is sampling? The various types of sampling methods: briefly explained. Probability and non-probability sampling. Infographic in PDF. What is sampling? Dy definition, sampling is a statistical process whereby researchers choose the type of the sample. The crucial point here is to choose a good sample. What is a population? | 558 | 916 |
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| Infographic in PDF. What is sampling? Dy definition, sampling is a statistical process whereby researchers choose the type of the sample. The crucial point here is to choose a good sample. What is a population? In sampling meaning, a population is a set of units that we are interested in studying. These units should have at least one common characteristic. The units could be people, cases (organizations, institutions), and pieces of data (for example – customer transactions). What is a sample? A sample is a part of the population that is subject to research and used to represent the entire population as a whole. | 705 | 1,325 |
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| In sampling meaning, a population is a set of units that we are interested in studying. These units should have at least one common characteristic. The units could be people, cases (organizations, institutions), and pieces of data (for example – customer transactions). What is a sample? A sample is a part of the population that is subject to research and used to represent the entire population as a whole. What is crucial here is to study a sample that provides a true picture of the whole group. Often, it’s not possible to contact every member of the population. So, only a sample is studied when conducting statistical or marketing research. There are two basic types of sampling methods: Probability sampling
Non-probability sampling
Probability Sampling
What is probability sampling? | 916 | 1,708 |
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| What is crucial here is to study a sample that provides a true picture of the whole group. Often, it’s not possible to contact every member of the population. So, only a sample is studied when conducting statistical or marketing research. There are two basic types of sampling methods: Probability sampling
Non-probability sampling
Probability Sampling
What is probability sampling? In simple words, probability sampling (also known as random sampling or chance sampling) utilizes random sampling techniques and principles to create a sample. This type of sampling method gives all the members of a population equal chances of being selected. For example, if we have a population of 100 people, each one of the persons has a chance of 1 out of 100 of being chosen for the sample. Advantages of probability sampling: A comparatively easier method of sampling
Lesser degree of judgment
High level of reliability of research findings
High accuracy of sampling error estimation
Can be done even by non-technical individuals
The absence of both systematic and sampling bias. | 1,326 | 2,395 |
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| In simple words, probability sampling (also known as random sampling or chance sampling) utilizes random sampling techniques and principles to create a sample. This type of sampling method gives all the members of a population equal chances of being selected. For example, if we have a population of 100 people, each one of the persons has a chance of 1 out of 100 of being chosen for the sample. Advantages of probability sampling: A comparatively easier method of sampling
Lesser degree of judgment
High level of reliability of research findings
High accuracy of sampling error estimation
Can be done even by non-technical individuals
The absence of both systematic and sampling bias. Disadvantages: Monotonous work
Chances of selecting specific class of samples only
Higher complexity
Can be more expensive and time-consuming. Types of Probability Sampling Methods
Simple Random Sampling
This is the purest and the clearest probability sampling design and strategy. It is also the most popular way of a selecting a sample because it creates samples that are very highly representative of the population. Simple random is a fully random technique of selecting subjects. | 1,708 | 2,880 |
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| Disadvantages: Monotonous work
Chances of selecting specific class of samples only
Higher complexity
Can be more expensive and time-consuming. Types of Probability Sampling Methods
Simple Random Sampling
This is the purest and the clearest probability sampling design and strategy. It is also the most popular way of a selecting a sample because it creates samples that are very highly representative of the population. Simple random is a fully random technique of selecting subjects. All you need to do as a researcher is ensure that all the individuals of the population are on the list and after that randomly select the needed number of subjects. This process provides very reasonable judgment as you exclude the units coming consecutively. Simple random sampling avoids the issue of consecutive data to occur simultaneously. Stratified Random Sampling
A stratified random sample is a population sample that involves the division of a population into smaller groups, called ‘strata’. Then the researcher randomly selects the final items proportionally from the different strata. | 2,395 | 3,478 |
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| All you need to do as a researcher is ensure that all the individuals of the population are on the list and after that randomly select the needed number of subjects. This process provides very reasonable judgment as you exclude the units coming consecutively. Simple random sampling avoids the issue of consecutive data to occur simultaneously. Stratified Random Sampling
A stratified random sample is a population sample that involves the division of a population into smaller groups, called ‘strata’. Then the researcher randomly selects the final items proportionally from the different strata. It means the stratified sampling method is very appropriate when the population is heterogeneous. Stratified sampling is a valuable type of sampling methods because it captures key population characteristics in the sample. In addition, stratified sampling design leads to increased statistical efficiency. Each stratа (group) is highly homogeneous, but all the strata-s are heterogeneous (different) which reduces the internal dispersion. Thus, with the same size of the sample, greater accuracy can be obtained. | 2,881 | 3,991 |
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| It means the stratified sampling method is very appropriate when the population is heterogeneous. Stratified sampling is a valuable type of sampling methods because it captures key population characteristics in the sample. In addition, stratified sampling design leads to increased statistical efficiency. Each stratа (group) is highly homogeneous, but all the strata-s are heterogeneous (different) which reduces the internal dispersion. Thus, with the same size of the sample, greater accuracy can be obtained. Systematic Sampling
This method is appropriate if we have a complete list of sampling subjects arranged in some systematic order such as geographical and alphabetical order. The process of systematic sampling design generally includes first selecting a starting point in the population and then performing subsequent observations by using a constant interval between samples taken. This interval, known as the sampling interval, is calculated by dividing the entire population size by the desired sample size. For example, if you as a researcher want to create a systematic sample of 1000 workers at a corporation with a population of 10000, you would choose every 10th individual from the list of all workers. Cluster Random Sampling
This is one of the popular types of sampling methods that randomly select members from a list which is too large. | 3,478 | 4,840 |
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| Systematic Sampling
This method is appropriate if we have a complete list of sampling subjects arranged in some systematic order such as geographical and alphabetical order. The process of systematic sampling design generally includes first selecting a starting point in the population and then performing subsequent observations by using a constant interval between samples taken. This interval, known as the sampling interval, is calculated by dividing the entire population size by the desired sample size. For example, if you as a researcher want to create a systematic sample of 1000 workers at a corporation with a population of 10000, you would choose every 10th individual from the list of all workers. Cluster Random Sampling
This is one of the popular types of sampling methods that randomly select members from a list which is too large. A typical example is when a researcher wants to choose 1000 individuals from the entire population of the U.S. It is impossible to get a complete list of every individual. So, the researcher randomly selects areas (such as cities) and randomly selects from within those boundaries. Cluster sampling design is used when natural groups occur in a population. The entire population is subdivided into clusters (groups) and random samples are then gathered from each group. Cluster sampling is a very typical method for market research. | 3,991 | 5,373 |
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| A typical example is when a researcher wants to choose 1000 individuals from the entire population of the U.S. It is impossible to get a complete list of every individual. So, the researcher randomly selects areas (such as cities) and randomly selects from within those boundaries. Cluster sampling design is used when natural groups occur in a population. The entire population is subdivided into clusters (groups) and random samples are then gathered from each group. Cluster sampling is a very typical method for market research. It’s used when you can’t get information about the whole population, but you can get information about the clusters. The cluster sampling requires heterogeneity in the clusters and homogeneity between them. Each cluster must be a small representation of the whole population. Non-probability Sampling
The key difference between non-probability and probability sampling is that the first one does not include random selection. So, let’s see the definition. | 4,840 | 5,829 |
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| It’s used when you can’t get information about the whole population, but you can get information about the clusters. The cluster sampling requires heterogeneity in the clusters and homogeneity between them. Each cluster must be a small representation of the whole population. Non-probability Sampling
The key difference between non-probability and probability sampling is that the first one does not include random selection. So, let’s see the definition. What is non-probability sampling? Non-probability sampling is a group of sampling techniques where the samples are collected in a way that does not give all the units in the population equal chances of being selected. Probability sampling does not involve random selection at all. For example, one member of a population could have a 10% chance of being picked. Another member could have a 50% chance of being picked. | 5,374 | 6,247 |
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| What is non-probability sampling? Non-probability sampling is a group of sampling techniques where the samples are collected in a way that does not give all the units in the population equal chances of being selected. Probability sampling does not involve random selection at all. For example, one member of a population could have a 10% chance of being picked. Another member could have a 50% chance of being picked. Most commonly, the units in a non-probability sample are selected on the basis of their accessibility. They can be also selected by the purposive personal judgment of you as a researcher. Advantages of non-probability sampling: When a respondent refuses to participate, he may be replaced by another individual who wants to give information. Less expensive
Very cost and time effective. | 5,829 | 6,634 |
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| Most commonly, the units in a non-probability sample are selected on the basis of their accessibility. They can be also selected by the purposive personal judgment of you as a researcher. Advantages of non-probability sampling: When a respondent refuses to participate, he may be replaced by another individual who wants to give information. Less expensive
Very cost and time effective. Easy to use types of sampling methods. Disadvantages of non-probability sampling: The researcher interviews individuals who are easily accessible and available. It means the possibility of gathering valuable data is reduced. Impossible to estimate how well the researcher representing the population. | 6,247 | 6,935 |
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| Easy to use types of sampling methods. Disadvantages of non-probability sampling: The researcher interviews individuals who are easily accessible and available. It means the possibility of gathering valuable data is reduced. Impossible to estimate how well the researcher representing the population. Excessive dependence on judgment. The researchers can’t calculate margins of error. Bias arises when selecting sample units. The correctness of data is less certain. It focuses on simplicity instead of effectiveness. | 6,634 | 7,152 |
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| Excessive dependence on judgment. The researchers can’t calculate margins of error. Bias arises when selecting sample units. The correctness of data is less certain. It focuses on simplicity instead of effectiveness. Types of Non-Probability Sampling Methods
There are many types of non-probability sampling techniques and designs, but here we will list some of the most popular. Convenience Sampling
As the name suggests, this method involves collecting units that are the easiest to access: your local school, the mall, your nearest church and etc. It forms an accidental sample. It is generally known as an unsystematic and careless sampling method. | 6,935 | 7,588 |
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| Types of Non-Probability Sampling Methods
There are many types of non-probability sampling techniques and designs, but here we will list some of the most popular. Convenience Sampling
As the name suggests, this method involves collecting units that are the easiest to access: your local school, the mall, your nearest church and etc. It forms an accidental sample. It is generally known as an unsystematic and careless sampling method. Respondents are those “who are very easily available for interview”. For example, people intercepted on the street, Facebook fans of a brand and etc. This technique is known as one of the easiest, cheapest, and least time-consuming types of sampling methods. Quota Sampling
Quota sampling methodology aims to create a sample where the groups (e.g. males vs. females workers) are proportional to the population. The population is divided into groups (also called strata) and the samples are gathered from each group to meet a quota. | 7,152 | 8,120 |
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| Respondents are those “who are very easily available for interview”. For example, people intercepted on the street, Facebook fans of a brand and etc. This technique is known as one of the easiest, cheapest, and least time-consuming types of sampling methods. Quota Sampling
Quota sampling methodology aims to create a sample where the groups (e.g. males vs. females workers) are proportional to the population. The population is divided into groups (also called strata) and the samples are gathered from each group to meet a quota. For example, if your population has 40% female and 60% males, your sample should consist those percentages. Quota sampling is typically done to ensure the presence of a specific segment of the population. Judgment Sampling
Judgmental sampling is a sampling methodology where the researcher selects the units of the sample based on their knowledge . This type of sampling methods is also famous as purposive sampling or authoritative sampling. In this method, units are selected for the sample on the basis of a professional judgment that the units have the required characteristics to be representatives of the population. | 7,588 | 8,743 |
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| For example, if your population has 40% female and 60% males, your sample should consist those percentages. Quota sampling is typically done to ensure the presence of a specific segment of the population. Judgment Sampling
Judgmental sampling is a sampling methodology where the researcher selects the units of the sample based on their knowledge . This type of sampling methods is also famous as purposive sampling or authoritative sampling. In this method, units are selected for the sample on the basis of a professional judgment that the units have the required characteristics to be representatives of the population. According to https://explorable.com/ “The process involves nothing but purposely handpicking individuals from the population based on the authority’s or the researcher’s knowledge and judgment.” Judgmental sampling design is used mainly when a restricted number of people possess the characteristics of interest. It is a common method of gathering information from a very specific group of individuals. Snowball Sampling
Snowball sampling isn’t one of the common types of sampling methods but still valuable in certain cases. It is a methodology where researcher recruits other individuals for the study. | 8,120 | 9,348 |
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| According to https://explorable.com/ “The process involves nothing but purposely handpicking individuals from the population based on the authority’s or the researcher’s knowledge and judgment.” Judgmental sampling design is used mainly when a restricted number of people possess the characteristics of interest. It is a common method of gathering information from a very specific group of individuals. Snowball Sampling
Snowball sampling isn’t one of the common types of sampling methods but still valuable in certain cases. It is a methodology where researcher recruits other individuals for the study. This method is used only when the population is very hard-to-reach. For example, these include populations such as working prostitutes, current heroin users, people with drug addicts, and etc. The key downside of a snowball sample is that it is not very representative of the population. Conclusion
Sampling can be a confusing activity for marketing managers carrying out research projects. By knowing and understanding some basic information about the different types of sampling methods and designs, you can be aware of their advantages and disadvantages. | 8,743 | 9,906 |
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| This method is used only when the population is very hard-to-reach. For example, these include populations such as working prostitutes, current heroin users, people with drug addicts, and etc. The key downside of a snowball sample is that it is not very representative of the population. Conclusion
Sampling can be a confusing activity for marketing managers carrying out research projects. By knowing and understanding some basic information about the different types of sampling methods and designs, you can be aware of their advantages and disadvantages. The two main sampling methods (probability sampling and non-probability sampling) has their s | 9,349 | 10,000 |
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What is Statistical Analysis?
The Two Main Types of Statistical Analysis
Other Types of Statistics
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| 7 Types of Statistical Analysis: Definition and Explanation
The Key Types of Statistical Analysis
Statistics science is used widely in so many areas such as market research, business intelligence, financial and data analysis and many other areas. Why? Simply because statistics is a core basis for millions of business decisions made every day. The two main types of statistical analysis and methodologies are descriptive and inferential. However, there are other types that also deal with many aspects of data including data collection, prediction, and planning. On this page: What is statistical analysis? Definition and explanation. What are the different types of statistics? | 0 | 679 |
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| However, there are other types that also deal with many aspects of data including data collection, prediction, and planning. On this page: What is statistical analysis? Definition and explanation. What are the different types of statistics? (descriptive, inferential, predictive, prescriptive, exploratory data analysis and mechanistic analysis explained)
An infographic in PDF for free download. What is Statistical Analysis? First, let’s clarify that “statistical analysis” is just the second way of saying “statistics.” Now, the official definition: Statistical analysis is a study, a science of collecting, organizing, exploring, interpreting, and presenting data and uncovering patterns and trends. | 439 | 1,142 |
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| (descriptive, inferential, predictive, prescriptive, exploratory data analysis and mechanistic analysis explained)
An infographic in PDF for free download. What is Statistical Analysis? First, let’s clarify that “statistical analysis” is just the second way of saying “statistics.” Now, the official definition: Statistical analysis is a study, a science of collecting, organizing, exploring, interpreting, and presenting data and uncovering patterns and trends. Many businesses rely on statistical analysis and it is becoming more and more important. One of the main reasons is that statistical data is used to predict future trends and to minimize risks. Furthermore, if you look around you, you will see a huge number of products (your mobile phone for example) that have been improved thanks to the results of the statistical research and analysis. Here are some of the fields where statistics play an important role: Market research, data collection methods , and analysis
Business intelligence
Data analysis
SEO and optimization for user search intent
Financial analysis and many others. | 679 | 1,773 |
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What is Statistical Analysis?
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Other Types of Statistics
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| Many businesses rely on statistical analysis and it is becoming more and more important. One of the main reasons is that statistical data is used to predict future trends and to minimize risks. Furthermore, if you look around you, you will see a huge number of products (your mobile phone for example) that have been improved thanks to the results of the statistical research and analysis. Here are some of the fields where statistics play an important role: Market research, data collection methods , and analysis
Business intelligence
Data analysis
SEO and optimization for user search intent
Financial analysis and many others. Statistics allows businesses to dig deeper into specific information to see the current situations, the future trends and to make the most appropriate decisions. There are two key types of statistical analysis: descriptive and inference. The Two Main Types of Statistical Analysis
In the real world of analysis, when analyzing information, it is normal to use both descriptive and inferential types of statistics. Commonly, in many research run on groups of people (such as marketing research for defining market segments ), are used both descriptive and inferential statistics to analyze results and come up with conclusions. | 1,142 | 2,400 |
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| Statistics allows businesses to dig deeper into specific information to see the current situations, the future trends and to make the most appropriate decisions. There are two key types of statistical analysis: descriptive and inference. The Two Main Types of Statistical Analysis
In the real world of analysis, when analyzing information, it is normal to use both descriptive and inferential types of statistics. Commonly, in many research run on groups of people (such as marketing research for defining market segments ), are used both descriptive and inferential statistics to analyze results and come up with conclusions. What is descriptive and inferential statistics? What is the difference between them? Descriptive Type of Statistical Analysis
As the name suggests, the descriptive statistic is used to describe! It describes the basic features of information and shows or summarizes data in a rational way. Descriptive statistics is a study of quantitatively describing. | 1,773 | 2,754 |
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| What is descriptive and inferential statistics? What is the difference between them? Descriptive Type of Statistical Analysis
As the name suggests, the descriptive statistic is used to describe! It describes the basic features of information and shows or summarizes data in a rational way. Descriptive statistics is a study of quantitatively describing. This type of statistics draws in all of the data from a certain population ( a population is a whole group, it is every member of this group) or a sample of it. Descriptive statistics can include numbers, charts, tables, graphs, or other data visualization types to present raw data. However, descriptive statistics do not allow making conclusions. You can not get conclusions and make generalizations that extend beyond the data at hand. With descriptive statistics, you can simply describe what is and what the data present. | 2,400 | 3,281 |
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| This type of statistics draws in all of the data from a certain population ( a population is a whole group, it is every member of this group) or a sample of it. Descriptive statistics can include numbers, charts, tables, graphs, or other data visualization types to present raw data. However, descriptive statistics do not allow making conclusions. You can not get conclusions and make generalizations that extend beyond the data at hand. With descriptive statistics, you can simply describe what is and what the data present. For example, if you have a data population that includes 30 workers in a business department, you can find the average of that data set for those 30 workers. However, you can’t discover what the eventual average is for all the workers in the whole company using just that data. Imagine, this company has 10 000 workers. Despite that, this type of statistics is very important because it allows us to show data in a meaningful way. It also can give us the ability to make a simple interpretation of the data. | 2,754 | 3,789 |
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| For example, if you have a data population that includes 30 workers in a business department, you can find the average of that data set for those 30 workers. However, you can’t discover what the eventual average is for all the workers in the whole company using just that data. Imagine, this company has 10 000 workers. Despite that, this type of statistics is very important because it allows us to show data in a meaningful way. It also can give us the ability to make a simple interpretation of the data. In addition, it helps us to simplify large amounts of data in a reasonable way. Inferential Type of Statistical Analysis
As you see above, the main limitation of the descriptive statistics is that it only allows you to make summations about the objects or people that you have measured. It is a serious limitation. This is where inferential statistics come. Inferential statistics is a result of more complicated mathematical estimations, and allow us to infer trends about a larger population based on samples of “subjects” taken from it. | 3,281 | 4,329 |
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| In addition, it helps us to simplify large amounts of data in a reasonable way. Inferential Type of Statistical Analysis
As you see above, the main limitation of the descriptive statistics is that it only allows you to make summations about the objects or people that you have measured. It is a serious limitation. This is where inferential statistics come. Inferential statistics is a result of more complicated mathematical estimations, and allow us to infer trends about a larger population based on samples of “subjects” taken from it. This type of statistical analysis is used to study the relationships between variables within a sample, and you can make conclusions, generalizations or predictions about a bigger population. In other words, the sample accurately represents the population. Moreover, inference statistics allows businesses and other organizations to test a hypothesis and come up with conclusions about the data. One of the key reasons for the existing of inferential statistics is because it is usually too costly to study an entire population of people or objects. To sums up the above two main types of statistical analysis, we can say that descriptive statistics are used to describe data. | 3,789 | 5,006 |
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| This type of statistical analysis is used to study the relationships between variables within a sample, and you can make conclusions, generalizations or predictions about a bigger population. In other words, the sample accurately represents the population. Moreover, inference statistics allows businesses and other organizations to test a hypothesis and come up with conclusions about the data. One of the key reasons for the existing of inferential statistics is because it is usually too costly to study an entire population of people or objects. To sums up the above two main types of statistical analysis, we can say that descriptive statistics are used to describe data. Inferential statistics go further and it is used to infer conclusions and hypotheses. Other Types of Statistics
While the above two types of statistical analysis are the main, there are also other important types every scientist who works with data should know. Predictive Analytics
If you want to make predictions about future events, predictive analysis is what you need. This analysis is based on current and historical facts. Predictive analytics uses statistical algorithms and machine learning techniques to define the likelihood of future results, behavior, and trends based on both new and historical data. | 4,329 | 5,621 |
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| Inferential statistics go further and it is used to infer conclusions and hypotheses. Other Types of Statistics
While the above two types of statistical analysis are the main, there are also other important types every scientist who works with data should know. Predictive Analytics
If you want to make predictions about future events, predictive analysis is what you need. This analysis is based on current and historical facts. Predictive analytics uses statistical algorithms and machine learning techniques to define the likelihood of future results, behavior, and trends based on both new and historical data. Data-driven marketing, financial services, online services providers, and insurance companies are among the main users of predictive analytics. More and more businesses are starting to implement predictive analytics to increase competitive advantage and to minimize the risk associated with an unpredictable future. Predictive analytics can use a variety of techniques such as data mining, modeling, artificial intelligence, machine learning and etc. to make important predictions about the future. It is important to note that no statistical method can “predict” the future with 100% surety. | 5,007 | 6,214 |
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| Data-driven marketing, financial services, online services providers, and insurance companies are among the main users of predictive analytics. More and more businesses are starting to implement predictive analytics to increase competitive advantage and to minimize the risk associated with an unpredictable future. Predictive analytics can use a variety of techniques such as data mining, modeling, artificial intelligence, machine learning and etc. to make important predictions about the future. It is important to note that no statistical method can “predict” the future with 100% surety. Businesses use these statistics to answer the question “ What might happen? “. Remember the basis of predictive analytics is based on probabilities. Prescriptive Analytics
Prescriptive analytics is a study that examines data to answer the question “ What should be done? ” It is a common area of business analysis dedicated to identifying the best movie or action for a specific situation. Prescriptive analytics aims to find the optimal recommendations for a decision making process. | 5,621 | 6,699 |
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| Businesses use these statistics to answer the question “ What might happen? “. Remember the basis of predictive analytics is based on probabilities. Prescriptive Analytics
Prescriptive analytics is a study that examines data to answer the question “ What should be done? ” It is a common area of business analysis dedicated to identifying the best movie or action for a specific situation. Prescriptive analytics aims to find the optimal recommendations for a decision making process. It is all about providing advice. Prescriptive analytics is related to descriptive and predictive analytics. While descriptive analytics describe what has happened and predictive analytics helps to predict what might happen, prescriptive statistics aims to find the best options among available choices. Prescriptive analytics uses techniques such as simulation, graph analysis, business rules, algorithms, complex event processing, recommendation engines, and machine learning. Causal Analysis
When you would like to understand and identify the reasons why things are as they are, causal analysis comes to help. | 6,215 | 7,312 |
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| It is all about providing advice. Prescriptive analytics is related to descriptive and predictive analytics. While descriptive analytics describe what has happened and predictive analytics helps to predict what might happen, prescriptive statistics aims to find the best options among available choices. Prescriptive analytics uses techniques such as simulation, graph analysis, business rules, algorithms, complex event processing, recommendation engines, and machine learning. Causal Analysis
When you would like to understand and identify the reasons why things are as they are, causal analysis comes to help. This type of analysis answer the question “Why?” The business world is full of events that lead to failure. The causal seeks to identify the reasons why? It is better to find causes and to treat them instead of treating symptoms. Causal analysis searches for the root cause – the basic reason why something happens. | 6,700 | 7,628 |
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| This type of analysis answer the question “Why?” The business world is full of events that lead to failure. The causal seeks to identify the reasons why? It is better to find causes and to treat them instead of treating symptoms. Causal analysis searches for the root cause – the basic reason why something happens. Causal analysis is a common practice in industries that address major disasters. However, it is becoming more popular in the business, especially in IT field. For example, the causal analysis is a common practice in quality assurance in the software industry. So, let’s sum the goals of casual analysis: To identify key problem areas. | 7,313 | 7,963 |
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| Causal analysis is a common practice in industries that address major disasters. However, it is becoming more popular in the business, especially in IT field. For example, the causal analysis is a common practice in quality assurance in the software industry. So, let’s sum the goals of casual analysis: To identify key problem areas. To investigate and determine the root cause. To understand what happens to a given variable if you change another. Exploratory Data Analysis (EDA)
Exploratory data analysis (EDA) is a complement to inferential statistics. It is used mostly by data scientists. EDA is an analysis approach that focuses on identifying general patterns in the data and to find previously unknown relationships. | 7,628 | 8,354 |
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| To investigate and determine the root cause. To understand what happens to a given variable if you change another. Exploratory Data Analysis (EDA)
Exploratory data analysis (EDA) is a complement to inferential statistics. It is used mostly by data scientists. EDA is an analysis approach that focuses on identifying general patterns in the data and to find previously unknown relationships. The purpose of exploratory data analysis is: Check mistakes or missing data. Discover new connections. Collect maximum insight into the data set. Check assumptions and hypotheses. | 7,963 | 8,534 |
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| The purpose of exploratory data analysis is: Check mistakes or missing data. Discover new connections. Collect maximum insight into the data set. Check assumptions and hypotheses. EDA alone should not be used for generalizing or predicting. EDA is used for taking a bird’s eye view of the data and trying to make some feeling or sense of it. Commonly, it is the first step in data analysis, performed before other formal statistical techniques. Mechanistic Analysis
Mechanistic Analysis is not a common type of statistical analysis. However it worth mentioning here because, in some industries such as big data analysis, it has an important role. | 8,354 | 9,001 |
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| EDA alone should not be used for generalizing or predicting. EDA is used for taking a bird’s eye view of the data and trying to make some feeling or sense of it. Commonly, it is the first step in data analysis, performed before other formal statistical techniques. Mechanistic Analysis
Mechanistic Analysis is not a common type of statistical analysis. However it worth mentioning here because, in some industries such as big data analysis, it has an important role. The mechanistic analysis is about understanding the exact changes in given variables that lead to changes in other variables. However, mechanistic does not consider external influences. The assumption is that a given system is affected by the interaction of its own components. It is useful on those systems for which there are very clear definitions. Biological science, for example, can make use of. | 8,534 | 9,403 |
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| The mechanistic analysis is about understanding the exact changes in given variables that lead to changes in other variables. However, mechanistic does not consider external influences. The assumption is that a given system is affected by the interaction of its own components. It is useful on those systems for which there are very clear definitions. Biological science, for example, can make use of. Download the following infographic in PDF: 7 Key Types of Statistical Analysis: About The Author
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| Download the following infographic in PDF: 7 Key Types of Statistical Analysis: About The Author
Silvia Valcheva
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| Supervised vs Unsupervised Learning: algorithms, example, difference
Supervised vs Unsupervised Learning: Algorithms and Examples
When it comes to fundamentals of data science, we should know what is the difference between supervised and unsupervised learning in machine learning and in data mining as a whole. It is not only about to know when to use the one or the other. Unsupervised and supervised learning algorithms, techniques, and models give us a better understanding of the entire data mining world. We will compare and explain the contrast between the two learning methods. On this page: Unsupervised vs supervised learning: examples, comparison, similarities, differences. Supervised learning algorithms: | 0 | 716 |
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| We will compare and explain the contrast between the two learning methods. On this page: Unsupervised vs supervised learning: examples, comparison, similarities, differences. Supervised learning algorithms: list, definition, examples, advantages, and disadvantages. Unsupervised learning algorithms: list, definition, examples, pros, and cons. Which is better? Infographic in PDF (with comparison chart). | 509 | 914 |
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| list, definition, examples, advantages, and disadvantages. Unsupervised learning algorithms: list, definition, examples, pros, and cons. Which is better? Infographic in PDF (with comparison chart). What is Supervised learning? Supervised and unsupervised learning represent the two key methods in which the machines (algorithms) can automatically learn and improve from experience. This process of learning starts with some kind of observations or data (such as examples or instructions) with the purpose to seek for patterns. We use those patterns to make better decisions or forecast based on the examples/ instruction that we provide. The goal is to let the computers (machines) learn automatically without people assistance and adjust actions suitably. | 717 | 1,473 |
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| What is Supervised learning? Supervised and unsupervised learning represent the two key methods in which the machines (algorithms) can automatically learn and improve from experience. This process of learning starts with some kind of observations or data (such as examples or instructions) with the purpose to seek for patterns. We use those patterns to make better decisions or forecast based on the examples/ instruction that we provide. The goal is to let the computers (machines) learn automatically without people assistance and adjust actions suitably. So, what is supervised learning? We have supervised learning when a computer uses given labels as examples to take and sort series of data and thus to predict future events. Essentially, in supervised learning people teach or train the machine using labeled data. Labeled data means it is already tagged with the right answer. That’s why it is called supervised – because there is a teacher or supervisor. | 914 | 1,879 |
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| So, what is supervised learning? We have supervised learning when a computer uses given labels as examples to take and sort series of data and thus to predict future events. Essentially, in supervised learning people teach or train the machine using labeled data. Labeled data means it is already tagged with the right answer. That’s why it is called supervised – because there is a teacher or supervisor. In other words, the machine algorithm starts from the analysis of a well-known training dataset (also called input data) and then model a function to make predictions about future outcomes. Example: Let’s give an example to make things clearer: Suppose you have а bunch of different kinds of flowers. First, you need to train the machine on how to classify all different flowers: | 1,473 | 2,259 |
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| In other words, the machine algorithm starts from the analysis of a well-known training dataset (also called input data) and then model a function to make predictions about future outcomes. Example: Let’s give an example to make things clearer: Suppose you have а bunch of different kinds of flowers. First, you need to train the machine on how to classify all different flowers: You can train it like this: If there are thorns and the head has color Red then it will be labeled as Rose. If there aren’t thorns and the head has color White then it will be labeled as Daisy. Now, let’s say that after training the data, there is a new separate flower (say Rose) from the bunch and you need to ask the machine to identify it. Since your machine has already learned things, it needs to use that knowledge. | 1,879 | 2,682 |
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| You can train it like this: If there are thorns and the head has color Red then it will be labeled as Rose. If there aren’t thorns and the head has color White then it will be labeled as Daisy. Now, let’s say that after training the data, there is a new separate flower (say Rose) from the bunch and you need to ask the machine to identify it. Since your machine has already learned things, it needs to use that knowledge. The machine will classify the flower regarding the presence (or absence of thorns) and color and would label the flower name like Rose. This is how machines learn from training data (the bunch of flowers in our case) and then use the knowledge to label data. That is why the process is widely known as machine learning. Nowadays, supervised machine learning is the more common method that has applications in a wide variety of industries where data mining is used. Types of supervised learning algorithms: | 2,260 | 3,188 |
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| The machine will classify the flower regarding the presence (or absence of thorns) and color and would label the flower name like Rose. This is how machines learn from training data (the bunch of flowers in our case) and then use the knowledge to label data. That is why the process is widely known as machine learning. Nowadays, supervised machine learning is the more common method that has applications in a wide variety of industries where data mining is used. Types of supervised learning algorithms: Supervised learning techniques can be grouped into 2 types: Regression – we have regression problem when the output variables are continuous (to know what they mean see our post discrete vs continuous data ). The output variable is a real value, such as “euros” or “height”. Classification – machine learning classification algorithms are at the heart of a vast number of data mining problems and tasks. Classification can be used only for simple data such as nominal data, categorical data, and some numerical variables (see our posts nominal vs ordinal data and categorical data examples ). | 2,682 | 3,781 |
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| Supervised learning techniques can be grouped into 2 types: Regression – we have regression problem when the output variables are continuous (to know what they mean see our post discrete vs continuous data ). The output variable is a real value, such as “euros” or “height”. Classification – machine learning classification algorithms are at the heart of a vast number of data mining problems and tasks. Classification can be used only for simple data such as nominal data, categorical data, and some numerical variables (see our posts nominal vs ordinal data and categorical data examples ). There are 2 types of classification: binary and multi-classification. Binary classification is when there are only two possible outcomes (such as Yes/ No). For example, binary problem is to predict if a student will pass or fail. There are only two possible answers. | 3,188 | 4,048 |
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| There are 2 types of classification: binary and multi-classification. Binary classification is when there are only two possible outcomes (such as Yes/ No). For example, binary problem is to predict if a student will pass or fail. There are only two possible answers. Multi-classification is when there are more possible results such as “red”, “green”, “yellow”,and “white”. Here is a list of common supervised machine learning algorithms: Decision Trees
K Nearest Neighbors
Linear SVC (Support vector Classifier)
Logistic Regression
Naive Bayes
Neural Networks
Linear Regression
Support Vector Regression (SVR)
Regression Trees (e.g. Random Forest)
Gradient boosting
Fisher linear discriminant. Advantages and disadvantages of supervised learning
Advantages: It allows you to be very specific about the definition of the labels. | 3,782 | 4,610 |
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| Multi-classification is when there are more possible results such as “red”, “green”, “yellow”,and “white”. Here is a list of common supervised machine learning algorithms: Decision Trees
K Nearest Neighbors
Linear SVC (Support vector Classifier)
Logistic Regression
Naive Bayes
Neural Networks
Linear Regression
Support Vector Regression (SVR)
Regression Trees (e.g. Random Forest)
Gradient boosting
Fisher linear discriminant. Advantages and disadvantages of supervised learning
Advantages: It allows you to be very specific about the definition of the labels. In other words, you can train the algorithm to distinguish different classes where you can set an ideal decision boundary. You are able to determine the number of classes you want to have. The input data is very well known and is labeled. The results produced by the supervised method are more accurate and reliable in comparison to the results produced by the unsupervised techniques of machine learning. This is mainly because the input data in the supervised algorithm is well known and labeled. | 4,049 | 5,109 |
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Supervised vs Unsupervised Learning: Algorithms and Examples
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| In other words, you can train the algorithm to distinguish different classes where you can set an ideal decision boundary. You are able to determine the number of classes you want to have. The input data is very well known and is labeled. The results produced by the supervised method are more accurate and reliable in comparison to the results produced by the unsupervised techniques of machine learning. This is mainly because the input data in the supervised algorithm is well known and labeled. This is a key difference between supervised and unsupervised learning. The answers in the analysis and the output of your algorithm are likely to be known due to that all the classes used are known. Disadvantages: Supervised learning can be a complex method in comparison with the unsupervised method. The key reason is that you have to understand very well and label the inputs in supervised learning. | 4,611 | 5,512 |
msmarco_v2.1_doc_01_1668549522#12_2447031495 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| This is a key difference between supervised and unsupervised learning. The answers in the analysis and the output of your algorithm are likely to be known due to that all the classes used are known. Disadvantages: Supervised learning can be a complex method in comparison with the unsupervised method. The key reason is that you have to understand very well and label the inputs in supervised learning. It doesn’ take place in real time while the unsupervised learning is about the real time. This is also a major difference between supervised and unsupervised learning. Supervised machine learning uses of-line analysis. It is needed a lot of computation time for training. If you have a dynamic big and growing data, you are not sure of the labels to predefine the rules. | 5,110 | 5,883 |
msmarco_v2.1_doc_01_1668549522#13_2447032721 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| It doesn’ take place in real time while the unsupervised learning is about the real time. This is also a major difference between supervised and unsupervised learning. Supervised machine learning uses of-line analysis. It is needed a lot of computation time for training. If you have a dynamic big and growing data, you are not sure of the labels to predefine the rules. This can be a real challenge. In addition, the pros and or cons of supervised machine learning highly depend on what exactly supervised learning algorithm you use. Some examples of supervised learning applications include: In finance and banking for credit card fraud detection (fraud, not fraud). In fact, supervised learning provides some of the greatest anomaly detection algorithms. | 5,512 | 6,270 |
msmarco_v2.1_doc_01_1668549522#14_2447033931 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| This can be a real challenge. In addition, the pros and or cons of supervised machine learning highly depend on what exactly supervised learning algorithm you use. Some examples of supervised learning applications include: In finance and banking for credit card fraud detection (fraud, not fraud). In fact, supervised learning provides some of the greatest anomaly detection algorithms. Email spam detection (spam, not spam). In marketing area – a range of text mining algorithms are used for text sentiment analysis (happy, not happy). In medicine, for predicting patient risk (such as high-risk patient, low-risk patient) or for predicting the probability of congestive heart failure. What is unsupervised learning? As you already might guess, unsupervised learning works things out without using predefined labels. | 5,884 | 6,701 |
msmarco_v2.1_doc_01_1668549522#15_2447035201 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| Email spam detection (spam, not spam). In marketing area – a range of text mining algorithms are used for text sentiment analysis (happy, not happy). In medicine, for predicting patient risk (such as high-risk patient, low-risk patient) or for predicting the probability of congestive heart failure. What is unsupervised learning? As you already might guess, unsupervised learning works things out without using predefined labels. The unsupervised machine learning algorithms act without human guidance. The task of the machine is to sort ungrouped information according to some similarities and differences without any previous training of data. In other words, the machine is expected to find the hidden patterns and structure in unlabeled data by their own. That’s why it is called unsupervised – there is no supervisor to teach the machine what is right and what is wrong. The machine not always know what it is searching for, but can independently sort data and find compelling patterns. | 6,270 | 7,263 |
msmarco_v2.1_doc_01_1668549522#16_2447036656 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| The unsupervised machine learning algorithms act without human guidance. The task of the machine is to sort ungrouped information according to some similarities and differences without any previous training of data. In other words, the machine is expected to find the hidden patterns and structure in unlabeled data by their own. That’s why it is called unsupervised – there is no supervisor to teach the machine what is right and what is wrong. The machine not always know what it is searching for, but can independently sort data and find compelling patterns. To explain and compare better the difference between supervised and unsupervised learning, let’s see the types of unsupervised method. Unsupervised learning has two categories of algorithms: Clustering. Clustering is the assignment of a set of objects into subsets (also called clusters) so that objects in the same cluster have similar characteristics in some sense. The goal of clustering is to segregate groups with similar characteristics and then assign them into clusters. | 6,702 | 7,742 |
msmarco_v2.1_doc_01_1668549522#17_2447038159 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| To explain and compare better the difference between supervised and unsupervised learning, let’s see the types of unsupervised method. Unsupervised learning has two categories of algorithms: Clustering. Clustering is the assignment of a set of objects into subsets (also called clusters) so that objects in the same cluster have similar characteristics in some sense. The goal of clustering is to segregate groups with similar characteristics and then assign them into clusters. A good example here is when you want to group customers by their purchasing behavior. Analytics Vidhya has a great introduction to clustering that will help you to understand better the whole idea. Association . Here we have association rules that aim to find associations amongst data objects within large databases. Association is about discovering some interesting relationships between variables in large databases. | 7,263 | 8,162 |
msmarco_v2.1_doc_01_1668549522#18_2447039510 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| A good example here is when you want to group customers by their purchasing behavior. Analytics Vidhya has a great introduction to clustering that will help you to understand better the whole idea. Association . Here we have association rules that aim to find associations amongst data objects within large databases. Association is about discovering some interesting relationships between variables in large databases. For example, people that buy a new house also tend to buy new furniture. So, Clustering is about grouping data points according to their similarities while Association is about discovering some relationships between the attributes of those data points. List of key unsupervised machine learning algorithms and techniques: K-means clustering
K-NN (k nearest neighbors)
Dimensionality Reduction
Neural networks / Deep Learning
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis
Distribution models
Hierarchical clustering
Mixture models
Advantages and disadvantages of unsupervised learning
Again here, the pros and or cons of unsupervised machine learning depend on what exactly unsupervised learning algorithms you need to use. Despite that, there are some common benefits and advantages for the whole group of unsupervised machine learning algorithms. | 7,743 | 9,053 |
msmarco_v2.1_doc_01_1668549522#19_2447041279 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| For example, people that buy a new house also tend to buy new furniture. So, Clustering is about grouping data points according to their similarities while Association is about discovering some relationships between the attributes of those data points. List of key unsupervised machine learning algorithms and techniques: K-means clustering
K-NN (k nearest neighbors)
Dimensionality Reduction
Neural networks / Deep Learning
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis
Distribution models
Hierarchical clustering
Mixture models
Advantages and disadvantages of unsupervised learning
Again here, the pros and or cons of unsupervised machine learning depend on what exactly unsupervised learning algorithms you need to use. Despite that, there are some common benefits and advantages for the whole group of unsupervised machine learning algorithms. Advantages: Less complexity in comparison with supervised learning. Unlike in supervised algorithms, in unsupervised learning, no one is required to understand and then to label the data inputs. This makes unsupervised learning less complex and explains why many people prefer unsupervised techniques. Takes place in real time such that all the input data to be analyzed and labeled in the presence of learners. | 8,163 | 9,465 |
msmarco_v2.1_doc_01_1668549522#20_2447043040 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| Advantages: Less complexity in comparison with supervised learning. Unlike in supervised algorithms, in unsupervised learning, no one is required to understand and then to label the data inputs. This makes unsupervised learning less complex and explains why many people prefer unsupervised techniques. Takes place in real time such that all the input data to be analyzed and labeled in the presence of learners. This helps them to understand very well different models of learning and sorting of raw data. It is often easier to get unlabeled data — from a computer than labeled data, which need person intervention. This is also a key difference between supervised and unsupervised learning. Disadvantages: You cannot get very specific about the definition of the data sorting and the output. | 9,053 | 9,846 |
msmarco_v2.1_doc_01_1668549522#21_2447044285 | http://intellspot.com/unsupervised-vs-supervised-learning/ | Supervised vs Unsupervised Learning: algorithms, example, difference | Supervised vs Unsupervised Learning: Algorithms and Examples
Supervised vs Unsupervised Learning: Algorithms and Examples
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| This helps them to understand very well different models of learning and sorting of raw data. It is often easier to get unlabeled data — from a computer than labeled data, which need person intervention. This is also a key difference between supervised and unsupervised learning. Disadvantages: You cannot get very specific about the definition of the data sorting and the output. This is because the data used in unsupervised learning is labeled and not known. It is a job of the machine to label and group the raw data before determ | 9,466 | 10,000 |
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