Data Analytics
Duration: 3 Months | Level: Beginner to Advanced | Mode: Online/Offline
Week 1: Introduction to Data Analytics
- Day 1: Data Analytics Overview & Career Paths
- Day 2: Types of Analytics – Descriptive, Diagnostic, Predictive & Prescriptive
- Day 3: Data Collection Methods & Sources
- Day 4: Data Types & Measurement Scales
- Day 5: Data Quality & Preparation Fundamentals
- Day 6: Statistical Thinking for Data Analysis
- Day 7: Business Intelligence Concepts
Week 2: Excel for Data Analysis
- Day 8: Excel Fundamentals for Data Analysis
- Day 9: Advanced Functions & Formulas (VLOOKUP, INDEX-MATCH)
- Day 10: Pivot Tables & Data Summarization
- Day 11: Data Visualization in Excel – Charts & Dashboards
- Day 12: What-If Analysis & Scenario Manager
- Day 13: Excel Power Query for Data Transformation
- Day 14: Excel Power Pivot & Data Modeling
Week 3: SQL for Data Analysis
- Day 15: Relational Database Concepts
- Day 16: SQL Fundamentals – SELECT, WHERE, ORDER BY
- Day 17: Aggregations & GROUP BY Operations
- Day 18: JOINS & Relationships Between Tables
- Day 19: Subqueries & Common Table Expressions (CTEs)
- Day 20: Window Functions & Advanced SQL
- Day 21: SQL for Data Manipulation & Transformation
Week 4: Python Fundamentals
- Day 22: Python Basics – Variables, Data Types, Operators
- Day 23: Control Structures – Conditionals, Loops
- Day 24: Functions, Modules & Packages
- Day 25: Python Data Structures – Lists, Dictionaries, Sets
- Day 26: File Handling & Data Import/Export
- Day 27: Python for Data Cleaning
- Day 28: Error Handling & Debugging in Python
Week 5: Data Analysis with Pandas
- Day 29: Introduction to Pandas – Series & DataFrames
- Day 30: Data Indexing, Selection & Filtering
- Day 31: Data Cleaning & Preprocessing with Pandas
- Day 32: Data Transformation & Feature Engineering
- Day 33: Data Aggregation & Group Operations
- Day 34: Merging, Joining & Concatenating DataFrames
- Day 35: Time Series Analysis with Pandas
Week 6: Data Visualization with Python
- Day 36: Data Visualization Principles & Best Practices
- Day 37: Matplotlib Fundamentals
- Day 38: Seaborn for Statistical Visualizations
- Day 39: Interactive Visualizations with Plotly
- Day 40: Geographic Visualizations & Maps
- Day 41: Dashboard Creation with Dash
- Day 42: Storytelling with Data Visualizations
Week 7: Tableau
- Day 43: Tableau Desktop Interface & Data Connections
- Day 44: Creating Basic Visualizations in Tableau
- Day 45: Calculated Fields & Table Calculations
- Day 46: Interactive Dashboards & Stories
- Day 47: Mapping & Geospatial Analysis
- Day 48: Advanced Visualizations & Techniques
- Day 49: Tableau Server & Sharing Reports
Week 8: Power BI
- Day 50: Power BI Desktop Interface & Data Import
- Day 51: Power Query for Data Transformation
- Day 52: Data Modeling & Relationships in Power BI
- Day 53: DAX Fundamentals – Measures & Calculated Columns
- Day 54: Creating Power BI Reports & Visualizations
- Day 55: Power BI Service & Sharing Content
- Day 56: Power BI Administration & Governance
Week 9: Google Data Studio & Advanced BI
- Day 57: Introduction to Google Data Studio
- Day 58: Data Sources & Connections in Data Studio
- Day 59: Creating Reports & Dashboards
- Day 60: Calculated Fields & Data Blending
- Day 61: Comparing BI Tools – Strengths & Use Cases
- Day 62: BI Architecture & Implementation Strategies
- Day 63: Data Governance & Security in BI
Week 10: Statistical Analysis for Business
- Day 64: Descriptive Statistics & Exploratory Data Analysis
- Day 65: Probability Distributions & Sampling
- Day 66: Hypothesis Testing & A/B Testing
- Day 67: Correlation & Regression Analysis
- Day 68: Time Series Forecasting Methods
- Day 69: Cluster Analysis & Segmentation
- Day 70: Statistical Analysis with Python & R
Week 11: Industry-Specific Analytics
- Day 71: Marketing Analytics – Customer Segmentation, Campaign Analysis
- Day 72: Financial Analytics – Financial Statement Analysis, Risk Assessment
- Day 73: HR Analytics – Employee Performance, Attrition Prediction
- Day 74: Supply Chain Analytics – Inventory Optimization, Demand Forecasting