FULL STACK DATA ANALYTICS

Weeks 1-2: Introduction & Fundamentals

Day 1-3: Course Overview, Career Paths, Basics of Data Analysis

• Introduction to data analysis and its importance
• Types of data: Structured, semi-structured, and unstructured

Day 4-7: Data Collection & Data Cleaning

• Sources of data and data collection methods
• Introduction to data cleaning
• Hands-on with common data quality issues (e.g., missing values, duplicates)

Day 8-10: Excel for Data Analysis

• Essential Excel functions for data analysis
• Data sorting, filtering, pivot tables, and basic charts

Weeks 3-4: Database Management & SQL

Day 11-13: Introduction to SQL

• Basics of relational databases
• Writing simple SQL queries for data extraction

Day 14-18: Advanced SQL

• Joins, subqueries, and aggregations
• Data manipulation using SQL (updating, deleting)

Day 19-21: Project: SQL-based Data Extraction

• Real-world scenario: Extracting and analyzing data using SQL

Weeks 5-6: Data Analysis with Python

Day 22-24: Introduction to Python for Data Analysis

• Setting up the environment (Jupyter, libraries)
• Basic Python syntax and data structures

Day 25-28: Data Manipulation with Pandas

• Importing, exporting, and manipulating data
• Data filtering, merging, and grouping with Pandas

Day 29-30: Numpy for Data Processing

• Basics of Numpy for numerical data processing
• Array operations and indexing

Weeks 7-8: Statistics & Data Visualization

Day 31-33: Introduction to Statistics

• Descriptive statistics: mean, median, mode, variance
• Probability basics

Day 34-37: Inferential Statistics

• Hypothesis testing
• Confidence intervals and p-values

Day 38-42: Data Visualization Techniques

• Introduction to data visualization principles
• Visualization tools: Matplotlib and Seaborn in Python

Day 43-45: Advanced Data Visualization with Tableau/Power BI

• Creating dashboards and interactive visualizations
• Hands-on project using a small dataset

Weeks 9-10: Machine Learning Basics (Optional/Introductory)

Day 46-48: Introduction to Machine Learning for Data Analysts

• Overview of machine learning and its use cases
• Types of machine learning: Supervised, unsupervised

Day 49-51: Simple Algorithms for Data Analysts

• Regression analysis and classification basics
• Implementing basic algorithms in Python (e.g., linear regression)

Week 11: Data Storytelling & Final Project

Day 52-53: Data Storytelling

• How to translate data findings into insights
• Tips for creating reports and presentations

Day 54-55: Final Project Presentation

• Students work on a data analysis project from start to finish
• Presenting insights in a report/dashboard format