" "Data Science is the practice of using data to acquire insights, solve issues, and make decisions. It combines math, statistics, programming, and domain expertise to analyze data and extract meaningful information. " "It is a multidisciplinary field concerned with collecting knowledge and insights from structured and unstructured data using scientific methods, procedures, algorithms, and systems. Here's a detailed look at the key components of data science." "
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"Data Cleaning: Preparing raw data by correcting errors, filling in missing values, and addressing formatting issues.
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"Data Modeling: Creating predictive or descriptive models using machine learning techniques.
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"Decision Making: Leveraging insights to solve business problems, optimize processes, or develop new products."
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"Programming Skills: Proficiency in Python, R, SQL, and other programming languages.
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"Mathematics and Statistics: Knowledge of probability, linear algebra, and hypothesis testing.
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"Machine Learning: Expertise in supervised and unsupervised learning techniques, including regression, classification, and clustering.
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"Data Wrangling and ETL: Skills in extracting, manipulating, and loading data for analysis.
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"Visualization Tools: Proficiency in tools like Tableau, Power BI, Matplotlib, and Seaborn."
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"Data Science can be applied across various industries, including business, healthcare, finance, retail, and social media."
"Real World Examples: Real-world Examples Spotify and Netflix utilise user behaviour and preferences to propose music and films.
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"Forecast demand, optimize supply chains, and improve customer experiences in Retail Industry."
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" "In Artificial intellect, we will guide machines to effortlessly replicate or mimic natural intellect in order to construct AI." "To replicate or copy the AI, we employ three tools. Machine learning techniques include deep learning and generative AI" "These are the instruments that allow us to mimic natural intelligence and construct AI" "Because of NI, humans have two capabilities: learning and generating.We can learn from and produce data." "The computer seeks to replicate NI's learning abilities.When a machine attempts to replicate the creating capacity of NI, a generative AI tool is deployed." "
", unsafe_allow_html=True) st.markdown("" "AI is used to recognise speech and deliver appropriate replies. For example, when you ask Siri, What's the weather today? it utilises natural language processing (NLP) to interpret your inquiry and respond." "Self-Driving Cars,companies like Tesla employ artificial intelligence to allow automobiles to drive themselves by analysing real-time data from cameras, sensors, and maps. AI systems in self-driving cars can identify objects, forecast traffic patterns, and make driving judgements." "
", unsafe_allow_html=True) st.markdown("" "Machine Learning (ML) is a subset of Artificial Intelligence (AI) in which computers are trained to learn from data, recognise patterns, and make judgements or predictions without being specifically programmed to do so. The key assumption is that systems may learn and improve via experience." "Machine learning algorithms are used to analyse data, identify patterns, and make conclusions. As more data is collected, these algorithms improve and become more accurate at making predictions or conclusions." "
", unsafe_allow_html=True) st.markdown("" "Recommendation Systems (Netflix, YouTube, and Amazon):How It Works: Machine learning models use your previous behaviour (e.g., what you viewed or purchased) to propose new content or goods that you might enjoy.For example, Netflix recommends episodes based on what you've viewed, while Amazon sells things based on your browsing history." "Fraud detection (banking and credit cards),How it works: Machine learning algorithms examine transaction data for unexpected patterns that might signal fraud.For example, credit card firms utilise machine learning to detect potentially fraudulent transactions in real time, such as a card being used in two different places within minutes." "
", unsafe_allow_html=True) st.markdown("" "Deep learning models are made up of several layers of artificial neurones, each of which gradually modifies the data. These layers are organised into a neural network, and the network's internal parameters (weights) are adjusted during training to reduce prediction error. Backpropagation is a common way for accomplishing this." "
", unsafe_allow_html=True) st.markdown("" "IMage Recognition (Computer Vision):Convolutional Neural Networks (CNNs) are utilised in face recognition systems, self-driving cars, and even medical imaging (tumour identification in X-rays or MRI scans).Real-World Applications: Instagram and Facebook utilise deep learning to automatically tag individuals in images." "
", unsafe_allow_html=True) st.markdown("" "Generative AI is a class of artificial intelligence systems capable of creating new material, such as text, photos, music, or even video, that resembles or replicates real-world data. Unlike classical AI, which is frequently used for tasks like as classification or prediction (e.g., determining if a picture contains a cat or a dog), Generative AI generates new data by learning the patterns and structures of current data." "
", unsafe_allow_html=True) st.markdown("" "Deepfake (Video Generation):How It Works: GANs are often used to generate deepfakes, which are synthetic media (mainly videos) in which a person's image is transformed to make it look as if they are talking or doing something they never did. These algorithms learn from real-world footage to produce believable changes.Real-World Applications: Deepfakes have been utilised for amusement (e.g., movies, impersonations), but they also raise questions regarding misrepresentation." "
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