AI ANALYTICS · BUSINESS INTELLIGENCE · AUTOMATION
Turn Data Into Decisions With AI Powered Data Analytics.
Learn how to analyze data, build interactive dashboards, uncover business insights and use AI-assisted workflows with Excel, SQL, Power BI, Python and modern AI tools.
Modern businesses use data and AI to make better decisions: measuring marketing performance, understanding customers, forecasting trends and automating routine reporting. This Data Analytics Course teaches you practical, project-based skills. It suits students, graduates, professionals, marketers and business owners. AI speeds up routine work while your judgement keeps results accurate.
- Excel · SQL · Power BI · Python
- Project-based learning
- Data Analytics with AI
- Certificate of Completion
Course overview
Analytics fundamentals
Questions, KPIs and the analytics lifecycle.
Data cleaning and preparation
Fix errors, gaps and duplicates before analysis.
Exploratory data analysis
Find patterns and outliers with Excel, SQL and Python.
AI-assisted analysis
Use AI to draft, explain and summarize, then verify.
Business intelligence and reporting
Reports that decision makers can read quickly.
Interactive dashboards
Power BI dashboards with filters and KPI cards.
Data-driven decision-making
Turn findings into clear recommendations.
Real-world case studies
Business scenarios using illustrative data.
What is AI powered data analytics?
Data analytics is the process of turning raw records into insight for decisions. In AI-assisted analytics, AI tools help with the heavy lifting: spotting patterns in large datasets, suggesting cleaning steps, summarizing results and drafting reports. Analysts still define the question, check the numbers and decide what to do.
From raw data to insight
Collect, clean, analyze, visualize and communicate. Each step is practiced in the course.
Where AI helps
Data cleaning suggestions, formula and query drafts, summaries of findings and chart ideas.
Why humans stay essential
AI can be wrong or invent details. You will learn to verify results against the source data.
Traditional vs AI-assisted analytics
| Task | Traditional approach | AI-assisted approach |
|---|---|---|
| Data cleaning | Manually scan columns for errors. | AI suggests checks; you verify before applying. |
| Formulas and SQL | Write from scratch or search forums. | Describe the goal, get a draft, test it. |
| Summaries | Write findings by hand. | AI drafts a summary; you confirm each number. |
| Pattern finding | Build many charts to look for trends. | AI proposes patterns to investigate. |
| Reporting | Rebuild weekly reports manually. | Reusable templates with AI-drafted commentary. |
Examples are illustrative. Accuracy always depends on human verification.
Why learn data analytics with AI?
Data-driven decisions
A retailer compares stores before choosing where to stock more.
Marketing performance
A team finds which channel brings the cheapest qualified leads.
Sales and revenue
A manager spots a declining product line early.
Customer behaviour
A business separates repeat buyers from one-time buyers.
Reports and dashboards
Owners see KPIs in one view instead of many files.
Forecasting and trends
Seasonal demand patterns support stock planning.
Repetitive reporting
A weekly report becomes a repeatable template.
AI productivity
Drafting formulas and summaries faster, then validating them.
Skills help you work with data; they do not guarantee career outcomes.
Complete course curriculum: 12 modules
Curriculum and tools are configurable and will be finalized according to the actual training program.
Start by learning how analysts think: turning a business problem into a question that data can answer.
Topics
- Introduction to data analytics
- Types of data and analytics
- Structured and unstructured data
- Data analytics lifecycle
- Business problems and analytical questions
- KPIs, metrics and performance measurement
- Introduction to the role of a data analyst
Practical exercise
Convert a business scenario into analytical questions and a KPI list.
Learning outcome
Explain the analytics lifecycle and choose suitable metrics for a business goal.
Excel is the most common starting point for analysis. Build reliable, clean and well-presented workbooks.
Topics
- Essential formulas and functions
- IF, SUMIFS, COUNTIFS and lookup functions
- Data sorting and filtering
- Data cleaning and formatting
- Removing duplicates and handling missing values
- Pivot tables and pivot charts
- Conditional formatting
- Interactive Excel dashboards
- Practical sales reporting exercise
Practical exercise
Clean a messy sales sheet and build a pivot-based sales report.
Learning outcome
Prepare data, summarize it with pivots and present it in a simple dashboard.
Most business data lives in databases. Learn to retrieve and summarize it yourself.
Topics
- Database concepts and relational tables
- SELECT, WHERE and ORDER BY
- GROUP BY and aggregate functions
- INNER JOIN and LEFT JOIN
- Subqueries and common table expressions
- CASE statements
- Window functions
- Business reporting queries
- Practical customer and sales database exercises
Practical exercise
Write queries that answer questions such as top products, repeat customers and monthly revenue.
Learning outcome
Query multi-table data and produce summaries ready for reporting.
Move from static reports to interactive dashboards that decision makers can explore.
Topics
- Power BI interface and fundamentals
- Importing Excel, CSV and database data
- Power Query transformations
- Data modelling and relationships
- Measures and calculated columns
- DAX fundamentals
- KPI cards, charts and interactive filters
- Dashboard layout and storytelling
- Publishing and sharing reports
- Practical business intelligence dashboard
Practical exercise
Build a multi-page dashboard from a sample sales model.
Learning outcome
Model data, write basic DAX measures and design a clear interactive report.
Use Python to handle larger or messier data and repeat analysis consistently.
Topics
- Python fundamentals
- Variables, lists and dictionaries
- Working with CSV and Excel files
- Introduction to Pandas and NumPy
- Data filtering and transformation
- Missing-value handling
- Grouping and aggregation
- Exploratory data analysis
- Practical data analysis notebook
Practical exercise
Analyze a sample dataset in a notebook and summarize findings.
Learning outcome
Load, clean, group and explore data with Pandas and NumPy.
Statistics helps you judge whether a pattern is meaningful or just noise.
Topics
- Mean, median and mode
- Variance and standard deviation
- Probability fundamentals
- Sampling and distributions
- Correlation and regression concepts
- Hypothesis testing fundamentals
- Understanding outliers
- Interpreting statistical results
- Practical business examples
Practical exercise
Interpret spreads, outliers and correlations in a sample business dataset.
Learning outcome
Apply core statistical ideas and explain results in plain business language.
Learn to use AI assistants as a productivity aid while keeping you responsible for accuracy.
Topics
- Introduction to AI-assisted analytics
- Using AI assistants to understand datasets
- Writing effective prompts for data analysis
- Generating and explaining Excel formulas
- Assisting with SQL query creation
- Understanding Python code with AI
- Creating analytical summaries
- Identifying possible patterns and anomalies
- AI-assisted chart recommendations
- Validating AI-generated results
- Data privacy, security and responsible AI use
Practical exercise
Use AI to draft a formula, a query and a summary, then verify each against the data.
Learning outcome
Prompt effectively, check AI output and protect sensitive data.
Good analysis only matters if people understand it and act on it.
Topics
- Principles of effective visualization
- Selecting suitable chart types
- Bar charts, line charts and scatter plots
- Dashboard design fundamentals
- Visual hierarchy and readability
- Presenting insights to stakeholders
- Avoiding misleading charts
- Turning findings into recommendations
Practical exercise
Redesign a cluttered chart set and present three recommendations.
Learning outcome
Choose the right chart and communicate findings clearly and honestly.
Measure how campaigns perform and where budget works hardest. Uses illustrative sample data, clearly labelled.
Topics
- Campaign performance analysis
- Lead generation metrics
- Cost per lead and conversion rates
- Return on ad spend calculations
- Customer acquisition cost
- Funnel analysis
- Channel comparison
- Campaign reporting dashboards
- AI-assisted marketing insights
Practical exercise
Compare channels in an illustrative campaign dataset and report cost per lead and ROAS.
Learning outcome
Calculate core marketing metrics and build a campaign report.
Understand what sells, to whom, and which customers stay.
Topics
- Revenue and sales trend analysis
- Product performance
- Customer segmentation
- Customer retention and churn concepts
- Repeat purchase analysis
- Sales forecasting fundamentals
- Customer lifetime value concepts
- Business KPI dashboards
Practical exercise
Segment sample customers and chart revenue trends by product.
Learning outcome
Analyze sales and customer behaviour and present KPI views.
Reduce repetitive work with consistent, checkable reporting processes. Advanced automation and integrations depend on the tools and access available.
Topics
- Automating repetitive analytical tasks
- Standardized reporting workflows
- Data refresh concepts
- Excel and Power BI reporting processes
- AI-assisted report summaries
- Error checking and quality assurance
- Documentation and reproducibility
- Introduction to connecting analytics tools
Practical exercise
Turn a manual weekly report into a repeatable template with a QA checklist.
Learning outcome
Design reliable reporting workflows with quality checks and documentation.
Bring everything together in end-to-end projects you can document and present.
Topics
- End-to-end sales analytics project
- Marketing campaign analytics project
- Customer segmentation project
- Power BI executive dashboard
- Python exploratory analysis project
- AI-assisted business reporting project
- Presenting insights and recommendations
- Documenting projects for a professional portfolio
Practical exercise
Complete a capstone, then present findings and recommendations.
Learning outcome
Deliver a documented project from raw data to business recommendation.
Tools and technologies
Microsoft Excel
Spreadsheet analysis, pivots and quick dashboards. Used for cleaning, summarizing and reporting. Modules 2, 7, 11.
SQL
Retrieves and summarizes data from relational databases. Modules 3, 7, 10.
Power BI
Builds interactive dashboards and data models. Modules 4, 8, 11.
Python
Flexible analysis and repeatable workflows. Modules 5, 6, 12.
Pandas
Python library for cleaning, filtering and grouping tables. Module 5.
NumPy
Python library for numerical calculations. Modules 5, 6.
ChatGPT and AI assistants
Help draft formulas, queries, summaries and explanations, always verified by you. Module 7, 9, 11.
Data visualization tools
Charts and dashboard design across tools. Modules 4, 8.
Tool names are used descriptively. No partnership or endorsement is implied.
Hands-on project showcase
Proposed course projects, not completed student work. Datasets are illustrative samples.
Project 1: Sales Performance Analytics Dashboard
Business problem: Leadership cannot see which products and regions drive revenue or whether targets are met.
Sample dataset: Illustrative multi-year sales transactions with products, regions and targets.
Tools: Excel, SQL, Power BI
Approach: Clean data; query totals; model relationships; build revenue trend, product and target visuals.
Expected output: Interactive dashboard with KPI cards and a short insight summary.
Skills practiced: Data cleaning, joins, DAX measures, KPI design.
Project 2: Marketing Campaign Analytics
Business problem: Spend is rising but it is unclear which channels produce efficient leads.
Sample dataset: Illustrative campaign data: spend, impressions, leads, conversions.
Tools: Excel, SQL, Power BI
Approach: Calculate cost per lead, conversion rate and ROAS; compare channels; identify weak spots.
Expected output: Campaign report with channel comparison and recommendations.
Skills practiced: Funnel analysis, metric calculation, reporting.
Project 3: Customer Segmentation Analysis
Business problem: A business treats every customer the same despite different behaviour.
Sample dataset: Illustrative customer and order history.
Tools: Python, Pandas, Excel
Approach: Prepare data; compute recency, frequency and value; group customers; describe each segment.
Expected output: Segment profiles with suggested actions.
Skills practiced: Grouping, aggregation, interpretation.
Project 4: Business Intelligence Dashboard
Business problem: Executives need several metrics in one view instead of scattered reports.
Sample dataset: Illustrative sales, finance and customer tables.
Tools: Power BI, DAX
Approach: Define KPIs; model tables; design layout; add filters; review readability.
Expected output: Executive dashboard published for sharing.
Skills practiced: Data modelling, design, storytelling.
Project 5: AI-Assisted Reporting Automation
Business problem: Weekly reports take hours of repetitive manual work.
Sample dataset: Illustrative weekly performance data.
Tools: Excel, AI assistants
Approach: Build a template; use AI to draft summaries; verify numbers; add QA checks.
Expected output: Reusable report workflow with validated summary.
Skills practiced: Prompting, validation, process documentation.
Project 6: Python Data Analysis Project
Business problem: A raw dataset needs to be understood before anyone can use it.
Sample dataset: Illustrative messy dataset with gaps and outliers.
Tools: Python, Pandas, NumPy
Approach: Load data; handle missing values; explore distributions; visualize; document findings.
Expected output: Notebook with charts and conclusions.
Skills practiced: Cleaning, EDA, visualization, documentation.
Marketing analytics
Cost per lead
Spend divided by leads generated.
Conversion rate
Share of leads or visitors who take the next step.
Return on ad spend
Revenue attributed to ads divided by ad spend.
Customer acquisition cost
Total acquisition spend per new customer.
Funnel analysis
See where prospects drop off between stages.
Channel comparison
Compare channels by efficiency, not volume alone.
Illustrative sample data is used in exercises and is clearly labelled as such.
Sales and customer analytics
Revenue trends
Track sales over time and spot seasonality.
Product performance
Rank products by revenue and margin.
Customer segmentation
Group customers by behaviour and value.
Retention and churn
Understand who stays and who leaves.
Repeat purchase analysis
Measure how often customers return.
Forecasting and lifetime value
Fundamentals of sales forecasts and customer lifetime value.
Concepts are taught at a foundational level.
Data analytics project workflow
- Define the question
- Collect data
- Clean and prepare
- Explore and analyze
- Visualize
- Verify results
- Present recommendations
Sample business analytics use cases
Retail
Track best-selling products and slow stock.
Digital marketing
Compare ad channels by cost per lead.
Service businesses
Measure enquiry-to-booking conversion.
Finance teams
Monitor margins and budget variance.
Operations
Report turnaround times and workloads.
Education
Analyze enrolment and engagement trends.
Who should enroll?
College students
Build a practical skill set alongside studies.
Fresh graduates
Add portfolio-ready analytics projects.
Working professionals
Use data to improve reports and decisions.
Aspiring data analysts
Follow a structured path across core tools.
Digital marketers
Measure campaigns beyond platform dashboards.
Sales and business professionals
Understand pipelines, products and customers.
Business owners
Read your own numbers with confidence.
Career transitioners
Learn analytics from fundamentals.
Prerequisites
Beginners can start with the foundational modules. Basic computer literacy and an interest in working with data are helpful. No prior coding is required.
Career pathways
Data Analyst
Responsibilities: clean data, analyze trends, report findings.
Tools: Excel, SQL, Python.
Skills: statistics, communication.
Business Intelligence Analyst
Responsibilities: build dashboards and KPI reports.
Tools: Power BI, SQL.
Skills: modelling, storytelling.
Reporting Analyst
Responsibilities: produce recurring reports accurately.
Tools: Excel, Power BI.
Skills: QA, automation concepts.
Power BI Developer
Responsibilities: build data models and measures.
Tools: Power BI, DAX, Power Query.
Skills: data modelling.
Marketing Data Analyst
Responsibilities: analyze campaigns and funnels.
Tools: Excel, SQL, Power BI.
Skills: marketing metrics.
Junior Business Analyst
Responsibilities: gather requirements, analyze processes.
Tools: Excel, SQL.
Skills: KPIs, stakeholder communication.
Opportunities depend on individual skills, experience, qualifications and the job market. No placement, salary or employment is guaranteed.
Why choose LeadsWave Solutions?
Practical learning approach
Exercises in every module.
AI-focused workflows
AI used with validation built in.
Business-oriented use cases
Marketing, sales and reporting scenarios.
Hands-on exercises
Work with real tools, not slides.
Structured curriculum
Twelve ordered modules.
Projects and portfolio
Document your work.
Presenting findings
Learn to explain insights to stakeholders.
Certificate of Completion
Subject to course requirements.
LeadsWave also runs an AI automation course.
Your learning journey
- 1Understand data and analytical thinking
- 2Learn spreadsheets, databases and data preparation
- 3Build dashboards and business intelligence reports
- 4Explore Python and statistical analysis
- 5Apply AI tools to analytical workflows
- 6Complete practical business projects
- 7Present findings and develop a portfolio
Duration is confirmed by the training team.
Skills you will develop
- Cleaning and organizing datasets
- Writing SQL queries
- Building interactive dashboards
- Performing exploratory data analysis
- Using Python libraries for data analysis
- Interpreting business metrics
- Using AI assistants responsibly
- Presenting findings through reports and visualizations
Download the course curriculum
Get the module list and topic outline.
Frequently asked questions
It teaches analytics fundamentals and tools (Excel, SQL, Power BI, Python) together with AI assistants that help with analysis, summaries and reporting. You stay responsible for verifying results.
Yes. It begins with foundations and builds step by step. Basic computer literacy and interest in data are helpful.
No. Excel and SQL come first and Python is introduced from fundamentals.
Excel, SQL, Power BI, Python with Pandas and NumPy, AI assistants and visualization techniques. The final tool list is confirmed by the training team.
Yes. Module 2 covers Excel analysis and Module 3 covers SQL, including joins, CTEs and window functions.
Yes. Module 4 covers data import, Power Query, modelling, DAX basics, dashboards and publishing.
Yes. Module 5 covers Python basics, Pandas and NumPy and exploratory analysis.
AI assistants help explain data, draft formulas and queries, summarize findings and suggest charts. Module 7 also covers validation, privacy and responsible use.
Yes. Each module includes a practical exercise and the course proposes six projects plus a capstone. Datasets used are illustrative samples.
A Certificate of Completion is provided, subject to course completion requirements confirmed by LeadsWave.
They relate to roles such as Data Analyst, BI Analyst and Reporting Analyst. Outcomes depend on your skills, experience, qualifications and the job market, and nothing is guaranteed.
Use Enroll Now or message us on WhatsApp. Our team will share batch details.
These are confirmed by the LeadsWave team when you enquire.
Support arrangements such as doubt-clearing are confirmed by the training team when you enquire.
Build Your Future with AI Powered Data Analytics
Learn to analyze data, build dashboards, uncover business insights and use AI-assisted workflows to work more effectively with information.
Enroll NowContact LeadsWave SolutionsDownload Course CurriculumWhatsApp Enquiry
WhatsApp +91 72074 91736 · info@leadswavesolutions.com