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Data Science Course Material

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Published in: Data Science
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This course provides an in-depth knowledge and practical skills, covering key points such as Python for Data Science, Probability and Statistics, Artificial Intelligence and Machine Learning.

Sadhvi A / Coimbatore

year of teaching experience

Qualification:

Teaches: Advanced Excel, Basic Computer, MS Office, Business Analytics, Content Writing, Data Analysis Certification, Machine Learning, Power BI Certification, Cloud Computing, Data Structures, DBMS & RDBMS, MongoDB, PL/SQL, Tableau, JELET, NATA Exam, Software Engineering, C / C++, Python Programming, Coding & Programming, Artificial Intelligence, Data Science, ACS, Digital Marketing, Node JS, React JS

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  1. INTRODUCTION TO DATA SCIENCE
  2. INTRODUCTION TO PYTHON FOR DATA SCIENCE What is Data Science? Data Science revolves around uncovering valuable information from data, influencing future decisions based on these discoveries. It includes predictive analytics through Machine Learning and related areas to foresee outcomes and enhance decision- making processes.
  3. Data Science Skills Python Programming and SQL Exploratory Data Analysis Machine Learning Deep Learning Natural Language Processing Computer Vision Information Retrieval Python libraries for Machine Learning like scikit-learn, Tensor flow, keras, and PyTorch.
  4. What is Data Analytics? Data analysis focuses on extracting valuable information from data through cleaning and transforming processes, with commonalities in presenting insights and creating visualizations as in Data Science. However, data analysis diverges from Data Science by not incorporating the machine learning aspect.
  5. Data Analytics Skills Any open source tool which is provided with complete documentation (E.g.: MS Office) Microsoft Excel SQL Python Tableau PowerBl
  6. What is Data Engineering? Data Engineering focuses on aiding data scientists and analysts by creating efficient data pipelines, developing architectures, and optimizing data storage to make vast amounts of data easily accessible. It also involves using tools to reveal hidden insights within the data, enhancing the overall effectiveness of data analysis processes.
  7. Data Engineering Skills Great Tools for Data Engineers Data Platforms Data Storage Streaming Languages Bl Tools databr icks kåfka. SOL Spar Streaming snowflake Flink ySpor QIik@
  8. DATA SCIENCE & ANALYTICS LIBRARIES FOR PYTHON DATA ANALYTICS LIBRARIES NumPy pandas DATA VISUALIZATION LIBRARIES matpl\tlib •seaborn MACHINE LEARNING LIBRARIES Keras TensorFIow
  9. DATA SCIENCE & ANALYSIS LIBRARIES FOR PYTHON DATA ANALYTICS LIBRARIES NumPy A powerful numerical computing library for Python, providing support for large, multi-dimensional arrays and a collection of mathematical functions. Pandas A versatile Data Manipulation and Analytics library, offering data types and tools for efficient handling and processing of structured data. scikit-learn A comprehensive machine learning library in Python, encompassing various algorithms and tools for tasks such as classification, regression, clustering, and model evaluation.
  10. DATA SCIENCE & ANALYSIS LIBRARIES FOR PYTHON DATA VISUALIZATION LIBRARIES Matplotlib A widely-used data visualization library in Python, allowing the creation of high-quality plots, charts, and graphs for effective data representation. Seaborn A statistical data visualization library built on top of Matplotlib, providing additional functionalities and a higher- level interface for creating visually appealing and informative statistical graphics.
  11. DATA SCIENCE & ANALYSIS LIBRARIES FOR PYTHON MACHINE LEARNING LIBRARIES FOR DATA SCIENCE A user-friendly deep learning library that provides a high-level API for building and training neural networks, making it easier to implement complex machine learning models. Tensor Flow A powerful open-source framework for machine learning, known for its extensive support for deep learning algorithms and efficient computation on GPUs, allowing for scalable and production-ready machine learning deployments.
  12. Dynamic pricing predicting flight delay Upselling Cross selling Predicting lifetime valueof customer Sentiment analysis Digital marketing 04 02 MARKETING Churn 03 TRAVEL HEALTHCARE Claims prediction 07 CREDIT & INSURANCE Fraud & risk detection Disease prediction Medication effectiveness Discount offering Demand forecasting SALES Self driving cars SOCIAL MEDIA 06 pitotless aircrafts. drones
  13. TYPES OF DATA ANALYTICS Data Analytics is the process of examining, interpreting, and transforming raw data into meaningful insights, patterns, and trends to facilitate decision-making and gain a deeper understanding of various aspects of a business or problem. There are several types of data analytics techniques, each serving different purposes. Descriptive Analytics Inferential Analytics Predictive Analytics Perspective Analytics
  14. TYPES OF DATA ANALYTICS Prescriptive Predictive Descriptive What happened? Diagnostic Why did it happen? What will happen? 11 How can we make it happen? COMPLEXITY
  15. Descriptive Analytics: Descriptive Analytics focuses on summarizing and presenting historical data to gain an understanding of past events. It helps answer the question, "What happened?" Examples include: Sales Analytics: What are the total sales figures for each product category in the last quarter? Customer Demographics: Can you provide a summary of customer demographics based on our recent survey responses?
  16. Diagnostic Analytics: Diagnostic Analytics involves analyzing data to identify the reasons behind certain outcomes or trends. It aims to answer the question, "Why did it happen?" Examples include: Web Analytics: Why did website traffic decrease last month compared to the previous month? Customer Satisfaction: What factors contributed to the decline in customer satisfaction scores in a specific region?
  17. Predictive Analytics: Predictive analytics uses historical data to make predictions about future events or outcomes. It helps answer the question, "What is likelyto happen?" Examples include: Financial Forecasting: What is the expected sales forecast for the upcoming quarter based on historical trends? Customer Churn Prediction: Can we predict which customers are most likely to churn based on their recent behavior and interactions?
  18. Prescriptive Analytics: Prescriptive analytics goes beyond predictive analytics and recommends actions to achieve a desired outcome. It helps answer the question, "What should we do about it?" Examples include: Market Strategy: Given the current market trends, what actions should we take to improve product performance? Supply Chain Optimization: How can we optimize our supply chain to reduce delivery times and operational costs?
  19. Data Visualization Model Evaluation Machine Learning Business Probem Understanding Data Science Life Cycle Feature Engineering Data Collection Data Cleaning and preparatiion Exploratory Data Analysis
  20. Data Science life Cycle Explanation Problem Understanding: Initiate the Data Science life cycle by comprehending the problem, the questions to be addressed, and the desired outcomes from the dataset. Data Acquisition: Retrieve necessary data with the assistance of Data Engineers, consolidating all required information to solve the identified problem. Data Wrangling: Apply knowledge to preprocess data, addressing missing values and shaping the dataset for visualizations and subsequent life cycle stages.
  21. Data Exploration: Employ visualizations and statistical measures in data exploration to verify if initial questions are being answered, marking the completion of the data analyst's role. Feature Engineering and Selection: Preprocess data for modeling, incorporating algorithms for feature selection and transformation, essential for both Machine Learning and Deep Learning. Modeling: Uncover underlying data trends, capturing behavior to create a model for predictive analytics, revealing insights and patterns.
  22. Deployment: Efficiently deploy the developed model, making it accessible to real-world users through various platforms such as mobile and web applications. Monitoring: Continuously monitor the deployed model, adapting to new datasets, tracking usage, and making adjustments as necessary, ensuring ongoing optimization and effectiveness.
  23. Structured Vs. Unstructured Data Structured Data: Structured data is characterized by a predetermined format and organization, commonly stored in relational databases. Its systematic arrangement simplifies handling within the field of Data Science. Example: Consider a straightforward table featuring columns of varying types. This type of structured data finds storage in relational databases, Excel files, and similar formats. J ennings Schneider Matt Matt
  24. Structured Vs. Unstructured Data Unstructured Data: Unformatted and lacking structure, unstructured data poses challenges in the field of Data Science. Examples: Customer Reviews Emails Handwritten Documents Videos Audios
  25. Applications of Data science Across various Industries Drug Discovery: Data Science is revolutionizing drug discovery by analysing digitalized results of drug compositions against bacteria, leading to the emergence of new drugs. Predictive Medicine: Healthcare now leverages wearables to monitor real-time patient data, allowing for proactive alerts based on current health status, potentially saving lives. Pe r s on a ise d predictive rnedicine
  26. Applications of Data science Across various Industries Medical Imaging: Data Science, particularly computer vision tools, enhances medical imaging like X-rays and MRI scans, outperforming in disease detection and addressing doctor shortages. Genomics: In genomics, Data Science deciphers genome sequences, unveiling hidden patterns crucial for understanding various functions within the human body.
  27. Applications of Data science Across various Industries Recommender Systems: Data Science drives recommender systems, predicting user preferences based on purchasing history. This boosts site traffic and enhances product sales on platforms like Amazon. Customers who viewed this item also viewed Spigen Air Armor for Oneplus B case (2020) (NOT Compatible with Verizon LJW - Matte alack 673 JNR926.48 Spigen Hybrid fM Oneplu5 8 case (2020) (NOT with LJW Version] - Crystal Cleat SAMSUNG A 71 56 Android Cell Ptu;ne US version AMOLEO screen Long.. 2,021 rap4S,048.27 oe.eP1us S - Model 77. 177 —1,049.34 Ruwed (2020) - Matte
  28. Applications of Data science Across various Industries Image Applications: 1 - Image Classification: Assigning images to specific categories using models inspired by brain function, contributing to extensive research in this field. 2 - Object Detection: Identifying and outlining objects within an image by drawing bounding boxes, along with displaying their corresponding classes. 3 - Object Segmentation: Pixel-level classification of images, marking each pixel to belong to a specific object, advancing the understanding of image content.
  29. Applications of Data science Across various Industries Image Applications: CAT CAT, DOG, DUCK Instance CAT, DOG, DUCK
  30. Applications of Data science Across various Industries 4 - Object Tracking: Continuous monitoring of an object's movement in a video, displaying a bounding box around it for comprehensive tracking. 5 - Self-driving Cars: Utilizing Data Science algorithms to process images captured by self-driving cars, making real-time decisions based on the analyzed visual data.
  31. Applications of Data science Across various Industries 6 - Image Translation: Applying Data Science algorithms for image translation, transforming images from one species of animals to another, showcasing the versatility of these models. Video Stream extraction original iroage bac kg rou d elirnination and edge detection, cropping and resizing to ptive statistical

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