Data Analytics Course in Greater Noida (Alpha 1)
Excel, SQL, Power BI and Python for a business analyst role, with AI tools to work faster. 42 modules with live projects, ending in a capstone for your portfolio.
What is the Data Analytics course?
The Data Analytics course at Skill Training Institute, Alpha 1, Greater Noida is a 6-month program with 42 modules in 7 phases. You learn Advanced Excel, SQL, Power BI, statistics and Python for data analysis, use AI tools to speed up your work, and build industry projects and a capstone for your portfolio.
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What's inside the course
Built around one goal: turning data into decisions.
- Advanced Excel: lookups, pivot tables, Power Query and dashboards
- SQL with MySQL: joins, window functions and CTEs
- Power BI: data modelling, DAX and interactive dashboards
- Statistics, hypothesis testing and A/B testing
- Python with NumPy, Pandas, Matplotlib and Seaborn
- AI assistants for formulas, SQL, DAX and Python, with output checking
- Machine learning basics: regression, classification and clustering
- Business storytelling, KPIs and stakeholder presentations
- Industry projects, capstone, portfolio and interview preparation
The data lifecycle you will work through
Every project follows the same five stages.
Full syllabus: 42 modules, 7 phases
Every module has its own focus. Filter or search.
Phase 1: Foundations & Advanced Excel Modules 1–8, Weeks 1 to 5
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 6 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, Weeks 5 to 9
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, Weeks 9 to 13
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, Weeks 13 to 15
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, Weeks 15 to 19
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, Weeks 19 to 22
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: Projects, Storytelling & Career Modules 39–42, Weeks 22 to 24
Business Problem Framing, KPIs & Data Storytelling Projects & career
Solve the right problem and explain it well.
- Turning business questions into analysis
- Choosing KPIs and metrics
- Insight, context and recommendation
- Presenting to non-technical audiences
Industry Projects: Sales, HR, Finance & Marketing Projects & career
Practise on realistic business data.
- Project brief and dataset
- Cleaning and analysis in Excel, SQL or Python
- Dashboard in Power BI
- Findings presentation
Capstone Project Projects & career
Deliver one complete analytics project.
- Problem definition and data collection
- SQL, Excel or Python analysis
- Power BI dashboard
- Final presentation and review
Portfolio, Resume & Interview Preparation Projects & career
Get ready for analyst roles.
- Portfolio and GitHub basics
- Resume and LinkedIn for analytics roles
- Excel, SQL, Power BI and statistics interview questions
- Mock interviews
Tools update often, so we teach current versions. Machine learning is covered as an introduction. Phase timing is indicative.
Tools you will use
Taught hands-on inside project work.
Spreadsheets
- Excel
- Power Query
- Pivot tables
- Macros basics
Databases
- MySQL
- MySQL Workbench
BI & visualisation
- Power BI Desktop
- Power BI Service
- DAX
- Matplotlib
- Seaborn
Programming
- Python
- Jupyter Notebook
- Google Colab
Data analysis
- NumPy
- Pandas
AI & ML
- ChatGPT
- Copilot features
- scikit-learn
Hands-on projects
The capstone takes a business problem from raw data to a dashboard and a presentation.
- Excel MIS dashboard
- Data cleaning with Power Query
- Pivot-based sales analysis
- SQL analysis on a relational database
- Power BI sales dashboard
- HR and attrition dashboard
- A/B test analysis report
- Exploratory data analysis in Python
- Customer churn or segmentation model
- Capstone data analytics project
Who is this course for?
Beginners, career switchers and working professionals.
| If you are | You will |
|---|---|
| Students & freshers | A complete path from Excel to Power BI and Python, with a project portfolio. |
| Working professionals switching careers | Build analytics skills step by step alongside your job. |
| MIS, accounts & operations staff | Replace manual reports with pivot tables, SQL and Power BI dashboards. |
| Sales, HR & marketing professionals | Analyse your own data and present decisions with numbers. |
| Business owners & managers | Read dashboards and ask better questions of your data. |
| Freelancers | Offer reporting, dashboard and analysis work to clients. |
Career opportunities
Roles this program prepares you for. Outcomes vary by learner.
- Data Analyst
- Business Analyst
- MIS / Reporting Analyst
- Power BI Developer
- Operations Analyst
- Sales or Marketing Analyst
Data Analytics vs related courses
Pick the right course for your goal.
| Course | Duration | Focus |
|---|---|---|
| Data Analytics (this course) | 6 months | Excel, SQL, Power BI, statistics, Python and AI tools for business analysis: 42 modules |
| Data Science | 6 months | Python, machine learning, deep learning intro and deployment: 60 modules |
| ChatGPT & AI Tools Mastery | 2 months | Using AI tools for daily work, with no coding or model building: 24 modules |
What is included
Everything in one program.
- 42 modules in 7 phases
- 8 Advanced Excel modules
- 8 SQL modules
- 8 Power BI modules
- 4 statistics modules and 6 Python modules
- 4 AI and machine learning basics modules
- Industry projects, capstone 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 course.
What is the Data Analytics course?
It is a 6-month program at Skill Training Institute, Alpha 1, Greater Noida with 42 modules in 7 phases. It combines Advanced Excel, SQL, Power BI, statistics, Python for data analysis and AI tools, with live projects and a capstone.
Do I need coding or maths knowledge?
No. The course starts with Excel, then SQL, and Python is taught from the basics in Phase 5. Statistics is taught step by step, and school-level maths is enough to start.
Which tools will I use?
Microsoft Excel including Power Query and pivot tables, MySQL and MySQL Workbench, Power BI Desktop and Service, Python with Jupyter, NumPy, Pandas, Matplotlib and Seaborn, scikit-learn, and AI assistants such as ChatGPT.
Is Power BI covered in depth?
Yes. Phase 3 has 8 modules covering Power Query, data modelling, DAX, interactive dashboards, row-level security basics and publishing to Power BI Service.
How is AI used in this course?
Phase 6 shows how to use AI assistants to write and debug formulas, SQL, DAX and Python, and how to check their output. It also covers machine learning basics with scikit-learn and data privacy, including India's DPDP Act 2023.
How is this different from the Data Science course?
Data Analytics focuses on business analysis: Excel, SQL, Power BI, statistics and Python, with machine learning at an introductory level. The Data Science course goes deeper into machine learning, deep learning, NLP and deployment.
What projects will I build?
Projects include an Excel MIS dashboard, a SQL analysis, Power BI sales and HR dashboards, an A/B test report, exploratory analysis in Python, a customer churn or segmentation model, and a final capstone.
What kind of jobs does this prepare me for?
Roles such as data analyst, business analyst, MIS or reporting analyst, Power BI developer and operations or sales analyst. Outcomes vary by learner, and we do not guarantee a job. Phase 7 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.