Program Curriculum
🔹 Module 1: Introduction to Big Data & Analytics
- What is Big Data? (5 Vs: Volume, Variety, Velocity, Veracity, Value)
- Role of a Data Analyst vs Data Scientist vs Data Engineer
- Big Data Analytics Lifecycle & Data-Driven Decision Making
🔹 Module 2: Data Handling with SQL
- Writing SQL Queries: Filtering, Sorting, Aggregating Data
- Advanced SQL: Joins, Subqueries, CTEs, Window Functions
- Data Cleaning using SQL
🔹 Module 3: Python for Data Analysis
- Python Basics & Libraries: Pandas, NumPy
- Working with CSV, Excel, and JSON Data
- Data Wrangling, Transformation, Aggregation and Grouping
🔹 Module 4: Data Visualization & Reporting
- Principles of Visual Storytelling
- Tableau: Dashboards, Charts, Filters, Parameters, Calculated Fields
- Power BI (Intro level)
🔹 Module 5: Statistics for Data Analysis
- Descriptive Statistics: Mean, Median, Mode, Variance
- Data Distributions & Histograms
- Hypothesis Testing and Confidence Intervals
🔹 Module 6: Business Analytics Applications
- KPI Definition and Tracking
- Customer Segmentation
- Sales, Marketing, & Financial Data Analysis
- Case Studies in Retail, Finance, Healthcare
🔹 Module 7: Big Data Tools & Ecosystem Overview
- Introduction to Hadoop & HDFS (High-Level)
- Basics of Apache Spark for Analysts
- Using SparkSQL for querying Big Data
🔹 Module 8: Excel for Analysts (Optional/Bridge Module)
- Advanced Functions (VLOOKUP, INDEX-MATCH)
- Pivot Tables and Dashboards
🔹 Module 9: Capstone Project
- A real-world analytics project to collect, clean, analyze, and visualize data, presenting findings in a business context.
🛠️ Tools & Technologies Covered
Languages:
SQL, Python
Libraries:
Pandas, NumPy, Matplotlib, Seaborn
Visualization:
Tableau, (Power BI – intro)
Databases:
MySQL / PostgreSQL
Big Data (Intro):
Hadoop, SparkSQL
Other:
Excel, Jupyter Notebook