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DataX

Data Science, Analytics & AI

Data is only as valuable as your ability to read it, model it, and act on it.

Co-developed with iHub Anubhuti–IIITD

Next-Gen Internet & Computing

Duration

~3 months

Format

Fully online

Schedule

1.5-hour live classes on alternate days

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Why this programme

Data is only as valuable as your ability to read it, model it, and act on it.

Learn the complete route from raw data to a decision-ready insight: collect, clean, analyse, visualise and model data, then communicate what it means. AI-assisted analytics complements—not replaces—sound data practice.

Your starting point

Aimed at engineering students with basic Python and school-level statistics. Experience with spreadsheets or small data tasks is a helpful starting point. Prior SQL, BI-tool or cloud experience is not expected.

These notes describe the programme’s intended starting point, not a formal eligibility table. Working professionals are welcome to enquire; no additional entry criteria are implied here.

What you can take forward

  • Clean and analyse data with Python and SQL.
  • Interpret statistical evidence and business experiments, and build forecasting and predictive models. Formal hypothesis tests and A/B testing are an optional module extension.
  • Create dashboards in Power BI or Tableau and explain findings to a non-technical audience.
  • Use LLMs to speed analysis and reporting while checking outputs and considering ethical data use.

Curriculum map

Ten modules. One working outcome.

Core topics and module build tasks are the programme pathway. Optional extensions are marked separately and are not promised core instruction.

Core learningHands-on buildOptional extension
01Data Science Foundations

Core topics

Spreadsheets and CSV, basic Python and Colab, data types and practical business questions.

Build task

Create an exploratory summary of a dataset.

02Data Handling with Python and SQL

Core topics

Pandas plus SQL SELECT, WHERE, GROUP BY, joins and aggregations.

Build task

Answer concrete questions with SQL and Python.

Optional extension · not a core promise

Window functions.

03Data Cleaning and Preparation

Core topics

Duplicates, missing values, inconsistent types, simple validation and data-quality logs.

Build task

Produce a clean, reproducible dataset.

04Exploratory Data Analysis and Statistics

Core topics

Distributions, averages, variation, correlation and uncertainty using examples.

Build task

Write an evidence-based exploratory report.

Optional extension · not a core promise

Formal hypothesis tests and A/B testing.

05Data Visualisation and Storytelling

Core topics

Effective chart choice, KPIs and one supported dashboard platform (Power BI or equivalent).

Build task

Create an interactive dashboard and a short insight narrative.

06Predictive Modelling

Core topics

One simple regression or classification use case, a baseline and evaluation.

Build task

Produce a basic predictive analysis with limitations.

Optional extension · not a core promise

Ensembles and SHAP.

07Time Series and Business Analytics

Core topics

Trend, seasonality, straightforward business metrics and naive forecast baselines.

Build task

Create a simple sales or demand analysis.

Optional extension · not a core promise

ARIMA and Prophet.

08Big Data and Cloud Basics

Core topics

A repeatable CSV/API-to-clean-table-to-dashboard pipeline, scripts and Git basics.

Build task

Automate a data refresh or reporting workflow.

Optional extension · not a core promise

Spark and cloud warehouses.

09AI-Powered Analytics

Core topics

AI-assisted SQL and reporting, independently checked for numerical errors, misleading charts and privacy risks.

Build task

Validate an AI-assisted analysis against its source data.

10Capstone Project Capstone begins in module 8

Core topics

Solve a practical analytics question.

Capstone deliverables

Deliver a cleaned dataset, reproducible queries or scripts, dashboard, recommendations and explanation; complete an individual viva.

Tools named as examples or recommendations in the outline are illustrative; the teaching approach may use one supported tool or stack where indicated.

Portfolio & capstone

Evidence you can show

An end-to-end analytics capstone with a cleaned dataset, reproducible queries or scripts, a dashboard, recommendations and an explanation, presented in an individual viva.

Learning rhythm

One hands-on assignment per module, quizzes and a mid-programme assessment. Guided work includes structured and recorded content, live sessions, exercises, coding practice and case studies.

Illustrative next steps

Where this can lead

Entry-level pathways to explore—not placement promises.

  • Data analyst
  • Junior data scientist
  • BI analyst

Certification

Certification with iHub Anubhuti–IIITD

Co-developed with iHub Anubhuti–IIITD

Delivery & assessment

Duration

~3 months

Format

Fully online

Live classes

1.5 hrs, alternate days

Modules

10, capstone from module 8

Each programme runs about 3 months, fully online, with 1.5-hour live classes on alternate days and about 30–35 hours of live instruction. Structured and recorded content sits alongside hands-on exercises, coding work, case studies, quizzes and guided projects. There is one hands-on assignment per module plus quizzes and a mid-programme assessment. The capstone begins in module 8, includes mentor checkpoints, and concludes with a final demo, assessment and individual viva. Certification considers assignments, the assessment, capstone and a minimum attendance requirement.

One assignment per moduleQuizzes + mid-programme assessmentCapstone + individual vivaMinimum attendance requirement

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