Data Analytics

Module 1

Basic to Advanced Excel

• Excel Interface
• Basic Formating
• Arithmetic Functions
• Logical Functions
• Basic To Advanced Charts
• Lookup Functions
• Data Cleaning and Validation
• Text Functions
• Pivots
• Exploratory Data Analysis (EDA)
• Interactive Dashboards
• Project Building – Ecommerce Industry

Module 2

Basic to Advanced Sql

• Mysql Workbench Interface
• Relational Database Management Systems(RDBMS)
• Normalisation
• Constraints and Keys
• Data Types And Creating Tables
• DML commands & Basic Stuctures of SQL Query
• Aggregate Functions and Advanced Operators
• Joins
• Union and Nested Queries
• CTEs and Window Functions
• Project Building – Digital Marketing Industry

Problem Solving Curriculum

with Module 1 and Module 2

• Mysql Workbench Interface
• Relational Database Management Systems(RDBMS)
• Normalisation
• Constraints and Keys
• Data Types And Creating Tables
• DML commands & Basic Stuctures of SQL Query
• Aggregate Functions and Advanced Operators
• Joins
• Union and Nested Queries
• CTEs and Window Functions
• Project Building – Digital Marketing Industry

Module 3

Basic to Advanced PowerBI

• PowerBI interface and Business Intelligence
• Data Connections and Power Query
• Data Modelling
• Basic to Advanced Visualisations
• DAX Functions
• Advanced Dashboard Creation
• Layout of PowerBI Tools
• Data Siurces/Data Modeling
• Dashboard
• Project Building – Healthcare Industry

Module 4

Basic to Advanced Python

• Basic and Jupyter Notebook Interfaces
• Control Statements-“Strings,List,Set,Tuples,Dictionary,Arrays”
• Functions
• Lambda Functions
• Mappings/Filters
• File Handling
• Reading and Writing files
• Advanced Python Library
• Basic to Advanced Numpy
• Basic to Advanced Pandas
• Basic to Advanced Matplotlib
• Project Building – Banking (BFSI) Industry

Statistics Curriculum

with Module 3 and Module 4

• Basic Statistics
• Descriptive Statistics
• Probability
• Permutation and Combination
• Discreate and Continuous Probability Distributions
• Inferential Statistics
• Parametric and Non-parametric Tests
• Degrees of Freedom and Bias
• CTE
• Project Building – Time Series Data Analysis

Module 5

Machine Learning

• Basics – Supervised and Unsupervised ML
• Linear, Logistics Regression and Applications
• Classification and Clustering Basics
• Principal Component Analysis and Applications
• Decision Trees
• Random Forest technique and Applications
• Project Building – Time Series Data Analysis

Module 6

Capstone Projects (After Module 3 and Module 4)

• Ecommerce Analytics – Uncover trends, and identify low performers based on demographics, product and more
• Digital Marketing Analytics – Explore funnel volume and ideate through data how funnel drops can be improved
• Healthcare Analytics – Identify performance payers and providers, and how this impacts the patients for the US market
• Banking Analytics – Credit Worthiness and Froud Detection
• Sales Analytics – Gain insights into customer demographics and business patterns

Module 7

Capstone Projects (After Module 3 and Module 4)

• Sports Analytics – Uncovering insights, patterns, and statistics, from ancient beginnings to modern spectacles in the Olympics History
• Retail Analytics – Streamline office management and enabling better decision-making for the organization
• Author Analytics – Informed decisions and insights within the dynamic world of book publishing
• Movie Rental Analytics – Explore customer preferences, rental patterns, and more