School of Data Engineering & Analytics · Foundation–Intermediate

Data Analytics with Python & SQL Course

Turn raw data into business decisions with SQL, Python, statistics, visualisation and a portfolio-ready analytics capstone.

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Data Analytics with Python & SQL course illustration at Brightnest AI Academy

Analytics Foundations and Business QuestionsSQL Foundations for AnalyticsAdvanced SQL and Analytical QueriesPython Data Analysis with Pandas

ExcelSheetsSQLPythonPostgreSQL

Duration9–11 weeks71 hours

Batch startsConfirm with admissionsOpen for registration

Learning formatLive mentor-led instruction, guided labs, assignments, feedback and project reviewsLive online / classroom

Curriculum8 modulesLabs and assessed capstone

Portfolio2 projectsPlus module evidence

LevelFoundation–IntermediateCourse level

PathwayData Engineering & Analytics EngineerRelated career pathway

How you will learn

Live instructor-led sessions that connect concepts to real workplace decisions.

Guided labs and workshops in every module.

Assignments, checkpoints and practical feedback.

Portfolio documentation, demonstrations and capstone review.

Access to recordings and LMS resources according to the published batch policy.

Career preparation based on completed work and target roles.

Course curriculum

What you will learn, module by module

Analyze, transform and communicate data using SQL, Python and practical statistics. Progress from Analytics Foundations and Business Questions to Data Analytics Capstone through guided labs, assessed projects, and portfolio evidence.

01Module 1 · 5 hoursAnalytics Foundations and Business QuestionsTranslate a business brief into KPIs, dimensions and an analysis plan.

Topics you will cover

Analytics lifecycle
KPI design
Dimensions/measures
Data types
Descriptive versus diagnostic analysis
Stakeholder questions
Data quality

Tools and platforms: Excel/Sheets optional, SQL, Python
Portfolio evidence: Analytics requirements brief
Assessment: Case-study quiz

02Module 2 · 8 hoursSQL Foundations for AnalyticsAnswer a business question set using a relational sales database.

Topics you will cover

SELECT
Filtering
Sorting
CASE
Aggregates
GROUP BY
HAVING

Tools and platforms: PostgreSQL/MySQL/SQLite
Portfolio evidence: SQL analysis script
Assessment: SQL challenge set

03Module 3 · 10 hoursAdvanced SQL and Analytical QueriesBuild retention, funnel and cohort analyses using window functions and CTEs.

Topics you will cover

CTEs
Subqueries
Window functions
Ranking
Cohort/retention logic
Pivots
Query optimisation basics

Tools and platforms: PostgreSQL or cloud warehouse SQL
Portfolio evidence: Analytics SQL portfolio
Assessment: Advanced SQL assessment

04Module 4 · 10 hoursPython Data Analysis with PandasClean and combine multiple raw datasets into an analysis-ready table.

Topics you will cover

Pandas
NumPy
Cleaning
Joins
Groupby
Reshaping
Dates

Tools and platforms: Python, Pandas, NumPy, Jupyter
Portfolio evidence: Clean analysis notebook
Assessment: Data-wrangling lab

05Module 5 · 8 hoursStatistics for Data AnalystsAnalyze an experiment or campaign test and communicate statistical and business significance.

Topics you will cover

Distributions
Sampling
Confidence intervals
Hypothesis tests
Correlation
Regression intuition
A/B testing

Tools and platforms: Python, SciPy/statsmodels optional
Portfolio evidence: Experiment analysis report
Assessment: Statistics case

06Module 6 · 8 hoursExploratory Data Analysis and VisualisationProduce an EDA report with a clear narrative and recommended actions.

Topics you will cover

EDA framework
Outliers
Segmentation
Distributions
Relationships
Visual selection
Chart integrity

Tools and platforms: Python, Matplotlib/Plotly optional
Portfolio evidence: Executive EDA report
Assessment: EDA review

07Module 7 · 8 hoursAnalytical Data Products and AutomationAutomate a recurring KPI report from raw files/database to final output.

Topics you will cover

Reusable queries
Parameterized notebooks
Simple ETL
Scheduled scripts
Data quality checks
Exports
Stakeholder-ready outputs

Tools and platforms: Python, SQL, Git, scheduler optional
Portfolio evidence: Automated analytics workflow
Assessment: Automation lab

08Module 8 · 14 hoursData Analytics CapstoneComplete a real-world business analytics project such as revenue, customer, operations or product analysis.

Topics you will cover

Question framing
SQL extraction
Python analysis
Statistics
Visualisation
Executive summary
Recommendations

Tools and platforms: SQL, Python, GitHub
Portfolio evidence: End-to-end data analytics portfolio project
Assessment: Capstone presentation

Applied portfolio

Projects you will build

2 portfolio projects plus module evidence

Portfolio project 1

Executive Revenue Analytics

Analyze revenue, customers and products using SQL/Python and present actionable insights.

SQL scripts · notebook · charts · executive summary

Portfolio project 2

Product Funnel Analysis

Build acquisition-to-conversion funnel and cohort analysis with recommendations.

SQL cohort queries · Python analysis · KPI report

Course value

Why this course

Analytics work is credible when learners can turn business questions into clean SQL, reliable Python analysis, practical statistics, and decision-ready communication.

The curriculum progresses from Analytics Foundations and Business Questions to Data Analytics Capstone, with guided labs, assessments, and two portfolio projects: Executive Revenue Analytics and Product Funnel Analysis.

Course fit

Who this course is for

Students, graduates, business analysts and professionals moving into data analytics.

Foundation–IntermediateData Engineering & Analytics Engineer

PrerequisitesNo prior analytics experience is required. Basic spreadsheet familiarity is helpful.

Practical capabilities

What you will be able to do

Translate a business brief into KPIs, dimensions and an analysis plan.
Answer a business question set using a relational sales database.
Build retention, funnel and cohort analyses using window functions and CTEs.
Clean and combine multiple raw datasets into an analysis-ready table.
Analyze an experiment or campaign test and communicate statistical and business significance.
Automate a recurring KPI report from raw files/database to final output.
Complete a real-world business analytics project such as revenue, customer, operations or product analysis.

Tools and platforms

Technology you will use in this course

ExcelSheetsSQLPythonPostgreSQLMySQLSQLitecloud warehouse SQLPandasNumPyJupyterSciPystatsmodelsMatplotlibPlotlyGit

Career relevance

Data Engineering & Analytics Engineer

This course supports the development of skills relevant to roles such as Data Analyst, Business Data Analyst, and Junior Analytics Engineer. The strongest learner outcome is a portfolio that shows the problem, implementation, testing or evaluation, documentation and a clear explanation of decisions—not a certificate alone.

Course evidence and instruction

Academy advisor

Ranjeet Kumar

Advisor, Brightnest AI Academy · Innovation & Growth Leader

A technologist and data leader with 15+ years of experience applying data, artificial intelligence and machine learning to complex problems, scalable products and business growth.

What our learners say

Learner experience

I started with basic Excel knowledge. The SQL, Power BI and Python projects helped me explain business insights clearly and move into an analyst role.

Industry and technology ecosystem

Microsoft

Course FAQs

Clear answers before you enrol

Turn raw data into business decisions with SQL, Python, statistics, visualisation and a portfolio-ready analytics capstone.

Is the Data Analytics with Python & SQL course suitable for beginners?

This course progresses from foundation to intermediate level. No prior analytics experience is required. Basic spreadsheet familiarity is helpful.

What will I build during the course?

You will complete guided labs in every module and build two portfolio projects: Executive Revenue Analytics and Product Funnel Analysis. Deliverables include working files or code, documentation, testing or evaluation evidence, and a final presentation.

Which tools and platforms are covered?

Key tools include Excel, Sheets, SQL, Python, PostgreSQL, MySQL, SQLite, and cloud warehouse SQL. Additional platforms are introduced in relevant modules through practical tasks, and the toolset may evolve as industry practice changes.

How long does the course take?

The course includes approximately 71 guided learning hours across 8 modules, normally delivered over 9–11 weeks depending on batch intensity and learner practice time.

Which career paths can this course support?

The curriculum supports the development of skills relevant to roles such as Data Analyst, Business Data Analyst, and Junior Analytics Engineer. Career outcomes depend on prior experience, project quality, interview readiness and market conditions; employment is not guaranteed.

Will I receive mentor and career support?

The course includes live instruction, lab support, assignment feedback, project reviews and career preparation covering portfolio development, resume writing, LinkedIn profile improvement, and interview guidance.

Ready to start?

Ready to start your Data Analytics with Python & SQL journey?

Review the full curriculum, experience a live class and confirm the right starting point before enrolling.

Book a Free Demo Class Speak with a Course Advisor: +91 73030 11395

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