Data Analytics
A course in turning raw data into clear findings. Students learn spreadsheet analysis, SQL, statistics, Python and Power BI, and finish with a capstone project.
- Duration
- 4 months
- Fees
- ₹8,000/month
- Batch Timing
- Afternoon batches
Who This Course Is For
Learners who want practical data analysis skills.
What You’ll Learn
- Data analytics foundations
- Statistics for analysis
- Cleaning data and exploring it
- Building dashboards and reports
- Business analytics
- Presenting findings as a clear story
Tools You’ll Use
- Excel
- SQL
- Python (with NumPy and Pandas)
- Power BI (with DAX)
Course Syllabus
Eight modules, from data foundations through Excel, SQL, statistics, Python and Power BI to business analytics, data storytelling and a capstone project.
- Module 1
Foundations of Data and Data Analytics
- What is data and data analytics
- Types of data
- Types of analytics
- Data analyst, data scientist and data engineer roles
- Real-world analyst workflow
- Business problems, data, insights and decisions
- Structured and unstructured data
- Analytical thinking
Practical work and portfolio outputs: A mini project: basic business data analysis.
- Module 2
Excel for Data Analytics
- Excel fundamentals, interface and data types
- Sorting, filtering and Excel tables
- Data cleaning in Excel
- Text and date/time functions
- Mathematical and logical functions
- Lookups: VLOOKUP, XLOOKUP and INDEX/MATCH
- Conditional formatting and data validation
- Pivot tables and pivot charts
- Basic Excel dashboards
- Power Query and Power Pivot introduction
Practical work and portfolio outputs: A sales analysis dashboard.
- Module 3
SQL for Analytics
- Database and SQL fundamentals
- SELECT, WHERE, ORDER BY, DISTINCT and LIMIT
- Aggregate functions, GROUP BY and HAVING
- CASE WHEN, string and date functions
- NULL handling
- INNER, LEFT, RIGHT and FULL JOINs
- Subqueries
- Common Table Expressions (CTEs)
- Window functions
- Views and query optimisation basics
Practical work and portfolio outputs: An e-commerce customer and sales analysis project.
- Module 4
Statistics
- Mean, median, mode, range, variance and standard deviation
- Percentiles, quartiles and data distribution
- Outliers and correlation
- Probability and sampling
- Population versus sample
- Hypothesis testing
- Confidence intervals
- A/B testing fundamentals
Practical work and portfolio outputs: A customer behaviour and statistical analysis project.
- Module 5
Python for Data Analytics
- Python fundamentals, variables and data types
- Operators, conditional statements and loops
- Functions
- Lists, tuples, dictionaries and sets
- File and exception handling
- NumPy
- Pandas and DataFrames
- Data cleaning and transformation
- GroupBy operations and merging datasets
- Missing-value and outlier handling
- Matplotlib and Seaborn
- Exploratory data analysis (EDA)
Practical work and portfolio outputs: An exploratory data analysis project on a real-world dataset.
- Module 6
Power BI and Business Intelligence
- Business intelligence and Power BI Desktop
- Data import and Power Query
- Data cleaning and transformation
- Data modelling, relationships and star schema
- Measures versus calculated columns
- DAX fundamentals, CALCULATE and FILTER
- Time intelligence and KPIs
- Interactive reports, drill-down and drill-through
- Tooltips, bookmarks and dashboard design
- Power BI Service, workspaces and scheduled refresh
Practical work and portfolio outputs: An executive business dashboard.
- Module 7
Business Analytics and Data Storytelling
- Business requirements, questions and KPI identification
- Sales, marketing, customer, product, finance, HR and operations analytics
- Funnel and cohort analysis
- Retention analysis and customer segmentation
- Root-cause and trend analysis
- Forecasting basics
- Turning insights into recommendations
- Presenting findings to management
- Chart selection and avoiding misleading visualisations
- Dashboard layout, colour, typography and KPI cards
- Interactive dashboards and data storytelling
- Explaining technical findings to non-technical audiences
Practical work and portfolio outputs: A business performance analysis project and an executive dashboard presentation.
- Capstone
Capstone Project
- Business problem, requirements gathering and data collection
- Data cleaning and exploration
- Data analysis
- Visualisation and insight generation
- Business recommendations and decision-making
- Project presentation and evaluation
Practical work and portfolio outputs: An end-to-end capstone project, presented and evaluated.
Practical Projects
- Basic business data analysis (mini project)
- Sales analysis dashboard in Excel
- E-commerce customer and sales analysis in SQL
- Customer behaviour and statistical analysis
- Exploratory data analysis on a real-world dataset
- Executive business dashboard in Power BI
- Business performance analysis and executive dashboard presentation
- An end-to-end capstone project
Learning Approach
Practical, tool-based learning. Students keep a daily notebook of Excel functions, SQL queries, Python snippets and DAX formulas, rebuild examples using fresh datasets, and do weekly practice, review and interview preparation.