Data Analytics (Extended) Course in Greater Noida (Alpha 1)
A deeper diploma in Advanced Excel, SQL, Power BI and Python, with advanced statistics and six live business case studies. 62 modules that end in a two-part capstone for your portfolio.
What is the Data Analytics (Extended) course?
The Data Analytics (Extended) course at Skill Training Institute, Alpha 1, Greater Noida is a 12-month diploma with 62 modules in 10 phases. You learn Advanced Excel, SQL, Power BI, Python and statistics up to advanced modelling, then work through six live business case studies and a two-part capstone.
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What's inside the course
Built around one goal: turning data into decisions.
- Advanced Excel with Power Query, VBA and financial modelling
- SQL with MySQL, from joins to stored procedures and performance
- Power BI: data modelling, DAX, dashboards and deployment concepts
- Statistics from basics to regression, ANOVA, time series and PCA
- Python with NumPy, Pandas, Matplotlib and Seaborn
- AI assistants for formulas, SQL, DAX and Python, with output checking
- Six business case studies across sales, marketing, HR, finance, supply chain and retail
- Two-part capstone, portfolio and interview preparation
What the extended course adds
Compared with the 6-month Data Analytics course.
- 8 advanced statistics modules on regression, ANOVA, time series and multivariate analysis
- 6 advanced tools modules: financial modelling, VBA, advanced SQL, data warehousing and Power BI performance
- 6 live business case studies, each with a dashboard and presentation
- A two-part capstone with more time for review and feedback
The data lifecycle you will work through
Every project follows the same five stages.
Full syllabus: 62 modules, 10 phases
Every module has its own focus. Filter or search.
Phase 1: Foundations & Advanced Excel Modules 1–8, Months 1 to 2
Data Analytics Landscape & Lifecycle Excel
See how analysts work from question to decision.
- Analyst roles and career paths
- The data lifecycle from collection to action
- Types of data and business questions
- Tools overview: Excel, SQL, Power BI, Python
- Learning roadmap for 12 months
Excel Essentials Excel
Work fast and clean in spreadsheets.
- Tables, formatting and shortcuts
- Formulas and cell references
- Sorting, filtering and conditional formatting
- Named ranges
- Printing and sharing workbooks
Excel Functions: Logical, Text & Date Excel
Automate everyday calculations.
- IF, IFS, AND, OR
- Text functions: LEFT, MID, TEXTJOIN
- Date and time functions
- SUMIFS, COUNTIFS, AVERAGEIFS
- Error handling with IFERROR
Lookups: XLOOKUP, INDEX-MATCH & Dynamic Arrays Excel
Pull data from one table into another.
- VLOOKUP and its limits
- XLOOKUP
- INDEX and MATCH
- FILTER, SORT and UNIQUE
- Lookup practice with sales data
Data Cleaning & Preparation in Excel Excel
Turn messy files into analysis-ready data.
- Removing duplicates and blanks
- Text to columns and Flash Fill
- Data validation rules
- Fixing dates and number formats
- Cleaning checklist
Pivot Tables & Pivot Charts Excel
Summarise thousands of rows in seconds.
- Building pivot tables
- Grouping and calculated fields
- Slicers and timelines
- Pivot charts
- Monthly sales summary report
Excel Charts & Dashboards Excel
Present results so people act on them.
- Choosing the right chart
- Combo charts and sparklines
- Interactive dashboard with slicers
- Layout and colour principles
- MIS dashboard project
Power Query, What-If Analysis & Macro Basics Excel
Automate repeat reporting.
- Importing and transforming data with Power Query
- Merge and append queries
- Goal Seek, scenarios and Solver
- Recording simple macros
- One-click report refresh
Phase 2: SQL & Databases Modules 9–16, Months 2 to 3
Database Fundamentals & MySQL Setup SQL
Understand how relational databases work.
- Tables, keys and relationships
- MySQL and Workbench setup
- Data types and constraints
- Creating databases and tables
SELECT, WHERE & Sorting SQL
Retrieve exactly the rows you need.
- SELECT and aliases
- WHERE, AND, OR, IN, LIKE
- ORDER BY and LIMIT
- Handling NULL values
Aggregations, GROUP BY & HAVING SQL
Summarise data inside the database.
- COUNT, SUM, AVG, MIN, MAX
- GROUP BY
- HAVING vs WHERE
- CASE expressions
Joins SQL
Combine tables correctly.
- INNER, LEFT, RIGHT and FULL joins
- Self joins
- Joining three or more tables
- Avoiding duplicate rows
Subqueries & Set Operations SQL
Nest queries to answer harder questions.
- Subqueries in WHERE and FROM
- EXISTS and IN
- UNION and INTERSECT
- Correlated subqueries
Window Functions & CTEs SQL
Write advanced analytical SQL.
- ROW_NUMBER, RANK and DENSE_RANK
- Running totals and moving averages
- LAG and LEAD
- Common table expressions
Data Cleaning, Views & Indexes SQL
Prepare and reuse data in SQL.
- Cleaning text, dates and NULLs
- Creating views
- Indexes overview
- Writing readable, maintainable queries
SQL Case Study & Interview Practice SQL
Apply SQL to a business database.
- Multi-table sales or HR case study
- Reporting queries for managers
- Interview-style SQL problems
- Query review and feedback
Phase 3: Power BI & Data Visualisation Modules 17–24, Months 3 to 4
Power BI Basics & Interface Power BI
Get productive in Power BI Desktop.
- Installing Power BI Desktop
- Report, data and model views
- Connecting to Excel, CSV and SQL
- Your first report
Power Query: Transform & Clean Data Power BI
Prepare data before it reaches the report.
- Applying transformation steps
- Merge and append queries
- Unpivot and pivot
- Parameters and query folding overview
Data Modelling & Star Schema Power BI
Build a model that stays fast and correct.
- Fact and dimension tables
- Relationships and cardinality
- Star schema design
- Date tables
DAX Fundamentals Power BI
Create your own calculations.
- Calculated columns vs measures
- SUM, AVERAGE, COUNT, DIVIDE
- CALCULATE and FILTER
- Variables and readable DAX
DAX: Time Intelligence & Filter Context Power BI
Compare periods and handle context.
- Row and filter context
- Year-to-date and month-to-date
- Previous period and growth %
- Ranking and Top N
Visuals & Report Design Power BI
Design reports business users understand.
- Charts, tables, cards and maps
- Slicers and filters
- Colour, layout and accessibility
- Data storytelling principles
Interactive Dashboards: Drill-through, Bookmarks & RLS Power BI
Make reports explorable and secure.
- Drill-down and drill-through
- Bookmarks and buttons
- Tooltips and parameters
- Row-level security basics
Power BI Service, Sharing & Refresh Power BI
Publish and share your work.
- Publishing to Power BI Service
- Workspaces and sharing
- Scheduled refresh and gateways overview
- Sales or HR dashboard project
Phase 4: Statistics for Analysts Modules 25–28, Months 4 to 5
Descriptive Statistics & Distributions Statistics
Summarise data with numbers.
- Mean, median, mode
- Variance and standard deviation
- Quartiles and outliers
- Normal distribution
Probability & Sampling Statistics
Reason about uncertainty and samples.
- Basic and conditional probability
- Sampling methods and bias
- Central Limit Theorem
- Standard error
Hypothesis Testing & Confidence Intervals Statistics
Test claims with data.
- Null and alternative hypotheses
- t-test and chi-square test
- p-values and their limits
- Confidence intervals
Correlation, Regression & A/B Testing Statistics
Measure relationships and run experiments.
- Correlation vs causation
- Simple linear regression
- Designing an A/B test
- Reporting results to stakeholders
Phase 5: Python for Data Analysis Modules 29–34, Months 5 to 6
Python Basics & Setup Python
Start coding with no prior experience.
- Python, Jupyter and Google Colab setup
- Variables, data types and operators
- Input and output
- Writing clean code
Control Flow, Functions & Data Structures Python
Build the core of Python.
- if, for and while
- Functions and lambda
- Lists, tuples, dictionaries and sets
- Reading and writing CSV files
NumPy & Pandas Fundamentals Python
Load and inspect data with code.
- NumPy arrays
- Series and DataFrames
- Reading CSV and Excel files
- Selecting and filtering with loc and iloc
Data Cleaning & Transformation with Pandas Python
Clean data at scale.
- Missing values and duplicates
- Fixing data types
- Outlier detection
- Creating new columns
GroupBy, Merge & Time Series Python
Summarise and combine datasets.
- groupby and aggregation
- Pivot tables in Pandas
- Merge, join and concat
- Dates and resampling
EDA & Visualisation with Matplotlib and Seaborn Python
Explore data and show what you found.
- EDA framework
- Histograms, scatter and bar charts
- Heatmaps and correlation
- EDA mini project
Phase 6: AI-Powered Analytics & Machine Learning Basics Modules 35–38, Months 6 to 7
AI Assistants for Analysts AI & ML
Use AI to work faster, and check its output.
- Prompting for Excel formulas, SQL, DAX and Python
- Explaining and debugging code with AI
- Copilot features in Excel and Power BI, where available
- Checking AI output for errors
- Data privacy and India's DPDP Act 2023
Machine Learning Basics with scikit-learn AI & ML
Understand how models learn from data.
- Supervised and unsupervised learning
- Train and test sets
- Overfitting and underfitting
- The scikit-learn workflow
Regression & Classification for Business Problems AI & ML
Predict numbers and categories.
- Linear regression for sales prediction
- Logistic regression for customer churn
- Accuracy, precision, recall
- Interpreting model results
Forecasting & Customer Segmentation Basics AI & ML
Look ahead and group customers.
- Trend and seasonality
- Simple forecasting methods
- K-means clustering
- Turning model output into recommendations
Phase 7: Advanced Statistics & Predictive Modelling Modules 39–46, Months 7 to 8
Advanced Probability & Sampling Distributions Advanced statistics
Go beyond the basics of uncertainty.
- Bayes' theorem in practice
- Joint and marginal distributions
- Sampling distributions and bootstrapping
- Simulation with Python
Multiple Regression & Diagnostics Advanced statistics
Build regression models you can trust.
- Multiple linear regression
- Assumptions and residual checks
- Multicollinearity and VIF
- Interpreting coefficients for business
Logistic Regression & Classification Statistics Advanced statistics
Model yes-or-no outcomes.
- Odds and log-odds
- Model fit and significance
- ROC-AUC and threshold choice
- Churn and default examples
ANOVA & Non-Parametric Tests Advanced statistics
Compare many groups correctly.
- One-way and two-way ANOVA
- Post-hoc tests
- Mann-Whitney and Kruskal-Wallis
- Choosing the right test
Experiment Design & Causal Thinking Advanced statistics
Move from correlation towards cause.
- A/B and multivariate tests
- Power and sample size
- Confounders and bias
- Quasi-experiments overview
Time Series Analysis & Forecasting Advanced statistics
Forecast with statistical models.
- Trend, seasonality and stationarity
- Moving averages and exponential smoothing
- ARIMA and SARIMA basics
- Forecast accuracy: MAPE and RMSE
Multivariate Analysis: PCA, Factor Analysis & Segmentation Advanced statistics
Find structure in many variables.
- PCA
- Factor analysis overview
- K-means and hierarchical clustering
- Customer segmentation
Statistical Modelling Workshop Advanced statistics
Apply advanced methods end to end.
- Framing a modelling question
- Model selection and validation
- Communicating uncertainty
- Written model report
Phase 8: Advanced Excel, SQL & Power BI Modules 47–52, Months 8 to 9
Financial Modelling & Forecasting in Excel Advanced tools
Build models decision-makers use.
- Three-statement model basics
- Scenario and sensitivity analysis
- NPV, IRR and loan schedules
- Model auditing and best practice
Excel VBA Automation Advanced tools
Automate repetitive reporting.
- VBA editor and macros
- Variables, loops and conditions
- User forms
- Automated report generation
Advanced SQL: Stored Procedures, Triggers & Performance Advanced tools
Write production-ready SQL.
- Stored procedures and functions
- Triggers
- Query execution plans
- Indexing strategy
Data Warehousing & ETL Concepts Advanced tools
Understand how business data is organised.
- OLTP vs OLAP
- Star and snowflake schemas
- ETL and ELT overview
- Building a small data mart
Advanced DAX & Power BI Performance Advanced tools
Make large reports fast.
- Advanced CALCULATE patterns
- Variables and iterators
- Performance Analyzer
- Aggregations and incremental refresh overview
Power BI in the Organisation Advanced tools
Deploy and govern BI.
- Dataflows overview
- Deployment pipelines overview
- Workspaces, apps and access control
- Governance and documentation
Phase 9: Live Business Case Studies Modules 53–58, Months 9 to 11
Case Study: Sales & Revenue Analytics Case studies
Find what drives revenue and where it leaks.
- Sales KPIs, targets and trends
- Cleaning and analysis with SQL, Excel or Python
- Regional and product performance analysis
- Power BI dashboard and management presentation
Case Study: Marketing & Customer Analytics Case studies
Measure campaigns and understand customers.
- Campaign ROI and funnel analysis
- Cleaning and analysis with SQL, Excel or Python
- Customer segmentation and lifetime value
- Power BI dashboard and management presentation
Case Study: HR & Workforce Analytics Case studies
Understand hiring, attrition and productivity.
- Attrition and hiring metrics
- Cleaning and analysis with SQL, Excel or Python
- Drivers of attrition with regression
- Power BI dashboard and management presentation
Case Study: Finance, Risk & Credit Analytics Case studies
Analyse costs, profit and risk.
- Budget vs actual and margin analysis
- Cleaning and analysis with SQL, Excel or Python
- Default risk modelling with logistic regression
- Power BI dashboard and management presentation
Case Study: Supply Chain & Operations Analytics Case studies
Improve delivery, stock and cost.
- Inventory, lead time and delivery KPIs
- Cleaning and analysis with SQL, Excel or Python
- Demand forecasting
- Power BI dashboard and management presentation
Case Study: E-commerce & Retail Analytics Case studies
Analyse baskets, returns and repeat buyers.
- Cohort and repeat-purchase analysis
- Cleaning and analysis with SQL, Excel or Python
- Basket analysis and product recommendations
- Power BI dashboard and management presentation
Phase 10: Capstone & Career Modules 59–62, Months 11 to 12
Capstone Part 1: Problem Definition & Data Pipeline Projects & career
Scope a real business problem.
- Business brief and success metrics
- Data collection and cleaning
- Data model design
- Project plan and review
Capstone Part 2: Analysis, Dashboard & Presentation Projects & career
Deliver a complete analytics solution.
- Statistical analysis or model
- Power BI dashboard
- Written report
- Final presentation to reviewers
Portfolio, GitHub & Resume Projects & career
Package your work for employers.
- Portfolio and case-study pages
- GitHub basics
- Resume and LinkedIn for analytics roles
- Project storytelling
Interview Preparation & Mock Interviews Projects & career
Get ready for analyst roles.
- Excel, SQL and Power BI interview questions
- Statistics and case-study questions
- Mock technical interviews
- HR round preparation
Tools update often, so we teach current versions. Phase timing is indicative.
Tools you will use
Taught hands-on inside project work.
Spreadsheets
- Excel
- Power Query
- Pivot tables
- VBA
Databases
- MySQL
- MySQL Workbench
- Stored procedures
BI & visualisation
- Power BI Desktop
- Power BI Service
- DAX
- Matplotlib
- Seaborn
Programming
- Python
- Jupyter Notebook
- Google Colab
Statistics & ML
- NumPy
- Pandas
- statsmodels
- scikit-learn
AI assistants
- ChatGPT
- Copilot features
Hands-on projects and case studies
Six case studies and a two-part capstone take business problems from raw data to a decision.
- Excel MIS dashboard
- Financial model in Excel
- SQL analysis on a relational database
- Power BI sales dashboard
- HR and attrition dashboard
- A/B test analysis report
- Time series forecasting project
- Sales and revenue case study
- Marketing and customer case study
- HR and workforce case study
- Finance and credit risk case study
- Supply chain case study
- E-commerce and retail case study
- Two-part capstone project
Who is this course for?
Beginners, career switchers and working professionals.
| If you are | You will |
|---|---|
| Students & freshers | A full diploma-length path with a deep project portfolio. |
| Career switchers | Time to build strong skills step by step alongside other commitments. |
| Working analysts | Add advanced statistics, DAX performance and case-study experience. |
| MIS, finance & operations staff | Move from manual reports to modelling, SQL and Power BI. |
| Business owners & managers | Understand analytics deeply enough to lead data projects. |
| Freelancers & consultants | Offer case-study grade analysis and dashboards to clients. |
Career opportunities
Roles this program prepares you for. Outcomes vary by learner.
- Data Analyst
- Business Analyst
- BI Developer
- Reporting / MIS Analyst
- Statistical Analyst
- Operations Analyst
Data Analytics (Extended) vs related courses
Pick the right course for your goal.
| Course | Duration | Focus |
|---|---|---|
| Data Analytics (Extended, this course) | 12 months | Everything in the 6-month course plus advanced statistics, advanced tools and 6 case studies: 62 modules |
| Data Analytics | 6 months | Excel, SQL, Power BI, statistics, Python and AI tools: 42 modules |
| Data Science | 6 months | Python, machine learning, deep learning intro and deployment: 60 modules |
What is included
Everything in one program.
- 62 modules in 10 phases
- 8 Advanced Excel modules and 6 more advanced tools modules
- 8 SQL modules and 8 Power BI modules
- 12 statistics modules, from basics to advanced modelling
- 6 Python modules and 4 AI and machine learning basics modules
- 6 live business case studies
- Two-part capstone, portfolio and interview preparation
- Certificate from Skill Training Institute
Certificate on completion
Awarded by Skill Training Institute after you complete the program.
Frequently Asked Questions
About the Data Analytics (Extended) course.
What is the Data Analytics (Extended) course?
It is a 12-month diploma at Skill Training Institute, Alpha 1, Greater Noida with 62 modules in 10 phases. It covers Advanced Excel, SQL, Power BI, Python, statistics and AI tools, then adds advanced statistics, advanced tools and six live business case studies before a capstone.
How is it different from the 6-month Data Analytics course?
The first phases cover the same core skills. The extended course then adds 8 advanced statistics modules, 6 advanced Excel, SQL and Power BI modules, 6 business case studies across sales, marketing, HR, finance, supply chain and retail, and a two-part capstone.
Do I need coding or maths knowledge?
No. The course starts with Excel, then SQL, and Python is taught from the basics. Statistics is built up step by step, and school-level maths is enough to start.
What advanced statistics will I learn?
Multiple and logistic regression with diagnostics, ANOVA and non-parametric tests, experiment design, time series forecasting with ARIMA, PCA, factor analysis and segmentation, all applied to business data.
What are the business case studies?
Six case studies in sales and revenue, marketing and customer analytics, HR, finance and credit risk, supply chain and e-commerce. Each one takes a business brief through cleaning, analysis, a Power BI dashboard and a management presentation.
Which tools will I use?
Excel including Power Query and VBA, MySQL, Power BI Desktop and Service, Python with Jupyter, NumPy, Pandas, Matplotlib, Seaborn and scikit-learn, and AI assistants such as ChatGPT.
Who should choose the 12-month course?
Learners who want deeper statistics and more portfolio-grade projects, or who want more time to practise. If you want to get job-ready faster, the 6-month course may suit you better.
What kind of jobs does this prepare me for?
Roles such as data analyst, business analyst, BI developer, reporting or MIS analyst and statistical analyst. Outcomes vary by learner, and we do not guarantee a job. The final phase covers portfolio, resume and interview preparation.
What laptop do I need?
A laptop with at least 8 GB RAM is recommended, with Windows preferred because Power BI Desktop runs on Windows. Ask us about your setup on WhatsApp.
What is the fee, and can I attend a demo class first?
Fees depend on the mode and batch you choose. WhatsApp or call +91-9773854034 for the current fee, batch timings and to book a free demo class.
Ready to start your data analytics career?
504, MSX Tower-2, Alpha-1 Commercial Belt, Greater Noida. Email contactus@skilltraininginstitute.in or see all courses.