Introduction to Data Analytics Duration: 3 Month
- What is Data Analytics?
- Importance and Applications of Data Analytics
- Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
- Data Analytics Workflow
- Roles in the Data Domain: Analyst, Data Scientist, Engineer
- Overview of Tools (Excel, SQL, Python, Power BI, Tableau)
Excel for Data Analysis
- Excel Basics: Cells, Formulas, and Functions
- Data Cleaning and Validation
- Sorting, Filtering, Conditional Formatting
- Pivot Tables and Pivot Charts
- Advanced Excel Functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
- What-If Analysis & Dashboards
- Excel Charts and Data Visualization
- Introduction to Excel Add-ins (Power Query, Power Pivot)
Statistics & Mathematics for Data Analytics
- Descriptive Statistics (Mean, Median, Mode, Variance, SD)
- Probability Basics
- Distributions (Normal, Binomial)
- Correlation & Covariance
- Hypothesis Testing
- Sampling Techniques
- Inferential Statistics & Confidence Intervals
- Introduction to Regression Analysis
SQL for Data Analysis
- Introduction to Databases & RDBMS Concepts
- SQL Basics: SELECT, WHERE, ORDER BY
- Filtering & Sorting Data
- JOINS (INNER, LEFT, RIGHT, FULL)
- GROUP BY & Aggregations
- Subqueries & Common Table Expressions (CTEs)
- Window Functions (RANK, ROW_NUMBER, etc.)
- Data Cleaning with SQL
- Real-world Query Optimization Techniques
Python for Data Analytics
- Python Basics (Variables, Loops, Conditions, Functions)
- Working with Libraries: NumPy – Numerical operations
- Pandas – DataFrames, Cleaning, Merging
- Handling Missing Data & Outliers
- Data Aggregation & Grouping
- Exploratory Data Analysis (EDA) with Python
Data Visualization & Dashboarding
Option A: Power BI
- Introduction to Power BI Interface
- Importing Data (Excel, SQL, Web APIs)
- Data Transformation using Power Query
- Data Modeling & DAX Basics
- Creating Interactive Dashboards
- Power BI Service: Publishing & Sharing Reports
Option B: Tableau
- Tableau Interface & Components
- Connecting to Data Sources
- Building Charts, Maps, and Dashboards
- Calculated Fields & Filters
- Storytelling with Data in Tableau
- Publishing Dashboards Online
Data Cleaning & Preprocessing
- Understanding Raw Data
- Handling Missing, Duplicate, and Inconsistent Data
- Data Formatting & Standardization
- Outlier Detection & Treatment
- Encoding Categorical Variables
- Data Normalization & Scaling
Exploratory Data Analysis (EDA)
- Understanding Data Distribution
- Univariate, Bivariate, and Multivariate Analysis
- Detecting Relationships and Patterns
- Feature Correlation & Visualization
- Case Study: Real-time EDA Project
Business Analytics & Domain Knowledge
- Business Understanding and KPIs
- Marketing Analytics (Customer Segmentation, Campaign Analysis)
- Financial Analytics (Profitability, Risk Analysis)
- Sales Analytics (Sales Trends, Forecasting)
- HR Analytics (Attrition, Performance)
- Supply Chain Analytics (Inventory, Demand Forecasting)
Introduction to Machine Learning for Analysts
- What is Machine Learning?
- Regression Models (Linear & Multiple)
- Classification Basics (Logistic Regression, Decision Trees)
- Clustering (K-Means)
- Feature Selection & Model Evaluation
- Hands-on: Predicting Sales / Customer Churn
Data Analytics with Cloud Platforms (Optional Advanced)
- Overview of Cloud in Analytics
- Using Google BigQuery / AWS Redshift / Azure Synapse
- Cloud-based Dashboards & Reporting
- Data Pipelines and ETL Overview
Projects & Case Studies
Mini Projects:
- Excel Dashboard for Sales Data
- SQL Query Analysis on HR Dataset
- Python EDA on Retail Data
- Power BI Dashboard for Financial KPIs
Capstone Projects:
- End-to-End Sales Data Analysis (SQL + Power BI)
- Customer Churn Prediction (Python + ML)
- E-Commerce Data Insights Dashboard (Tableau)
Career Preparation
- Building a Professional Portfolio (GitHub, LinkedIn)
- Resume Writing for Data Analysts
- Mock Interviews & Case Study Practice
- Interview Questions on Excel, SQL, Python, and BI Tools
- Google Data Analytics Professional Certificate
- Microsoft Power BI Data Analyst
Outcome: After completing this course, learners will be able to:
- Analyze and visualize real-world datasets
- Build dashboards and reports
- Perform EDA and generate business insights
- Use SQL, Excel, Python, and Power BI proficiently
- Prepare for roles like Data Analyst, Business Analyst, or Junior Data Scientist
