Data Analytics

Module 1: Introduction to Data Analytics

• What is Data Analytics: Concepts, Types & Scope
• Structured vs. Unstructured Data
• Data Collection Methods and Sources
• Career Roles: Analyst, BI Analyst, Data Engineer
• Data-Driven Decision Making in Business

Module 2: Statistics & Probability for Analysts

• Descriptive Statistics: Mean, Median, Mode, Standard Deviation
• Probability Basics and Distributions
• Hypothesis Testing and Confidence Intervals
• Correlation and Regression Fundamentals
• A/B Testing for Business Decisions

Module 3: Excel for Data Analysis

• Core Formulas, Functions, Filtering & Sorting
• Pivot Tables and Pivot Charts
• Data Cleaning and Preprocessing in Excel
• Power Query for Data Transformation
• Building Dynamic Dashboards and Reports

Module 4: SQL & Database Management

• Relational Databases: Tables, Keys, Schemas
• Core Queries: SELECT, INSERT, UPDATE, DELETE
• Joins, Subqueries and CTEs
• Aggregations, Window Functions & GROUP BY
• Query Optimization and Indexing Basics

Module 5: Python for Data Analysis

• Python Basics: Variables, Loops, Functions
• NumPy and Pandas for Data Manipulation
• Data Cleaning, Wrangling and Preprocessing
• Exploratory Data Analysis (EDA)
• Matplotlib and Seaborn for Visualization

Module 6: Power BI & Data Visualization

• Power BI Interface: Reports, Data & Model Views
• Connecting and Transforming Data (Power Query)
• DAX Fundamentals: Measures and Calculated Columns
• Building Interactive Dashboards and Visuals
• Publishing, Sharing and Collaboration in Power BI

Module 7: Advanced SQL & Python

• Advanced SQL: Stored Procedures, Views, Triggers
• Connecting Python to SQL Databases
• Automating Data Workflows with Python
• Statistical Analysis with SciPy and Statsmodels
• Building End-to-End Analysis Pipelines

Module 8: Data Storytelling & Reporting

• Principles of Effective Data Visualization
• Choosing the Right Chart for the Right Data
• Structuring Business Reports and Presentations
• Dashboards for Stakeholders and Executives
• Communicating Insights Clearly and Concisely

Module 9: Intro to Machine Learning for Analysts

• What Analysts Need to Know About ML
• Linear and Logistic Regression with Python
• Clustering for Customer Segmentation
• Basic Time Series Analysis and Forecasting
• Model Evaluation and Interpreting Results

Module 10: Capstone Projects & Portfolio

• End-to-End Real-World Business Projects
• Sales, Marketing and Operations Case Studies
• Building a GitHub / Portfolio of Work
• Interview Prep: SQL Challenges and Case Studies
• Resume, LinkedIn and Job Application Guidance

Experience in the Industry Gain knowledge from experts with practical Data Analyst project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture  and cognizant  employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.