Best Institute for Data Science and Data Analytics Course in Ahmedabad
Data Analytics Course Curriculum (Module-Wise)
Module 1: Introduction to Data Analytics
Topics Covered:
- What is Data Analytics?
- Types of Data Analytics:
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
- Data Analytics vs Data Science vs Business Intelligence
- Data Analytics Lifecycle
- Real-world applications and use cases
Module 2: Tools for Data Analytics
Tools Introduced:
- Excel / Google Sheets
- SQL (Structured Query Language)
- Power BI / Tableau
- Python (Pandas, NumPy, Matplotlib)
- Jupyter Notebook
Module 3: Excel for Data Analytics
Topics:
- Data cleaning and formatting
- Formulas and functions
- VLOOKUP, HLOOKUP, INDEX/MATCH
- IF, AND, OR, COUNTIF, SUMIF
- Pivot Tables and Pivot Charts
- What-If Analysis and Scenario Manager
- Basic dashboards
Example:
Use Excel to analyse monthly sales data and generate a summary dashboard using pivot charts.
Module 4: SQL for Data Analytics
Topics:
- Basics of Relational Databases
- SQL syntax and queries:
- SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- JOINs (INNER, LEFT, RIGHT, FULL)
- Aggregations (SUM, AVG, COUNT)
- Subqueries and CTEs
- Creating views
- Writing queries for business insights
Example:
Write a SQL query to find the top 5 products with the highest sales in the last 6 months.
Module 5: Data Visualization
Topics:
- Principles of effective data visualization
- Types of charts: bar, line, pie, scatter, histogram, box plot
- Data storytelling techniques
- Creating dashboards using:
- Power BI
- Tableau
Example:
Build a dashboard in Power BI that shows KPIs like sales, profit, and customer count segmented by region.
Module 6: Python for Data Analytics
Topics:
- Python Basics (variables, loops, functions)
- NumPy for numerical operations
- Pandas for data manipulation:
- Reading CSV/Excel files
- Filtering, grouping, merging data
- Data visualization:
- Matplotlib
- Seaborn
Example:
Use Pandas to clean customer transaction data and Seaborn to visualize spending patterns.
Module 7: Data Cleaning & Preparation
Topics:
- Handling missing values
- Removing duplicates
- Data type conversions
- Detecting and treating outliers
- Normalization & standardization (basic intro)
Tools: Excel, Pandas (Python), Power Query (Power BI)
Module 8: Basic Statistical Analysis
Topics:
- Mean, Median, Mode
- Variance and Standard Deviation
- Correlation and Covariance
- Frequency distribution and histograms
- Using statistics for insights
Example:
Use descriptive statistics to analyse customer age distribution in Python or Excel.
Module 9: Business Problem Solving with Analytics
Topics:
- Framing a business problem
- Choosing the right data and metrics
- Defining KPIs
- Communicating insights effectively
- Creating a business report or dashboard
Example:
A company wants to reduce customer churn. Use available customer data to analyse churn drivers and present actionable insights.
Module 10: Capstone Project
Project Ideas:
- Sales analysis and forecasting dashboard
- Customer segmentation based on behaviour
- Website traffic analysis
- Marketing campaign ROI analysis
- HR attrition and performance insights
Deliverables:
- Data cleaning & analysis
- Visualization/dashboard
- Insightful report or presentation
Optional Add-on Modules:
Time Series Analysis (Basics)
- Trends, seasonality, moving averages
- Simple forecasting techniques
Web Data & APIs
- Introduction to APIs
- Pulling data from web (optional with Python)
Big Data & Cloud Basics
- Overview of BigQuery, Snowflake, or AWS S3
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