Data Science and Business Analytics Course by IITM Pravartak

Introduction to Python and IDEs – The basics of the Python programming language, and how you can use various IDEs for Python development like Jupyter, Pycharm, etc.

Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, etc.
Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.

Data Manipulation with Numpy, Pandas, and Visualization – Using large datasets, you will learn about various techniques and processes that will convert raw unstructured data into actionable insights for further computations i.e. machine learning models, etc.

Case Study – The culmination of all the above concepts with real-world problem statements for better understanding.

Business Domains – Learn about various business domains and understand how one differs from the other.

1. Finance
2. Marketing
3. Retail
4. Supply Chain

Understanding the business problems and formulating hypotheses – Learn about formulating hypotheses for various business problems on samples and populations.

Exploratory Data Analysis to Gather Insights – Learn about exploratory data analysis and how it enables a foolproof producer of actionable insights.

Data Storytelling: Narrate stories in a memorable way – Learn to narrate business problems and solutions in a simple relatable format that makes it easier to understand and recall.

Case Study
This case study will cover the following concepts:

1. Create actionable insights from raw unstructured data to solve real-world business problems.

Feature Selection – Feature selection techniques in Python that include recursive feature elimination, Recursive feature elimination using cross-validation, variance threshold, etc.

Feature Engineering – Feature engineering techniques that help in reducing the best features to use for data modeling.

Model Tuning – Optimization techniques like hyperparameter tuning to increases the efficiency of the machine learning models.

Problem Statement and Project Objectives – You will learn how to formulate various problem statements and understand the business objective of any problem statement that comes as a requirement.

Approach for the Solution – Creating various statistical insights-based solutions to approach the problem will guide your learnings to finish a project from scratch.

Optimum Solutions – Formulating actionable insights backed by statistical evidence will help you find the most effective solution for your problem statements.

Evaluation Metrics – You will be able to apply various evaluation metrics to your project/solution. It will validate your approach and point towards shortcomings backed by insights, if any.

Gathering Actionable insights – You will learn about how a problem’s solution isn’t just creating a machine learning model, the insights that were gained from your analysis should be presentable in the form of actionable insights to capitalize on the solutions formulated for the problem statement.

Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same.

Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next time period(weeks, months, years, etc.)

Census – After studying the population data, we will gather insights and through predictive modeling try to create actionable insights on the same, it could be the average income of an individual, or most likely profession, etc.

Predictive Modeling – Various case studies on categorical and continuous data, to create predictive models that will predict specific outcomes based on the business problems.

HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing.

Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems.

Housing – A case study that will give you insight into how real estate firms can narrow down on pricing, customer choices, etc. using various predictive modeling techniques.

Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.

Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques.

Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in the early detection of various diseases.

Introduction to Data Warehouse – Introducing Data Warehouse and Business Intelligence, understanding the difference between database and data warehouse, working with ETL tools, and SQL parsing.

Architecture of Data Warehouse – Understanding the Data Warehousing Architecture, system used for Reporting and Business Intelligence, understanding OLAP vs. OLTP, and introduction to Cubes.

Data Modeling Concepts – The various stages from Conceptual Model, Logical Model to Physical Schema, Understanding the Cubes, benefits of Cube, working with OLAP multidimensional Cube, creating Report using a Cube.

Data Normalization – Understanding the process of Data Normalization, rules of normalization for first, second, and third normal, BCNF, deploying Erwin for generating SQL scripts.

Dimension and Fact Table – The main components of Business Intelligence – Dimensions and Fact Tables, understanding the difference between Fact Tables & Dimensions, and understanding Slowly Changing Dimensions in Data Warehousing.

Cubes, and OLAP – Compilation and optimization, understanding types and scope of cubes, Data Warehousing Vs. Cubes, limitations of Cubes and evolution of in-memory analytics.

• Job Search Strategy
• Resume Building
• Linkedin Profile Creation