" "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("" "Supervised Learning" "Unsupervised Learning" "Reinforcement Learning" "Semi-Supervised Learning") 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." "
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