DATADATA SCIENCE

DATA

Data Science

Turn raw data into insight, models, and business decisions.

  • IntermediateLevel
  • 34 Hours+Hours
  • 3 MonthsDuration
  • 480+Students

Taught by Karthik Subramanian · Data Science Mentor

Data Science
CertificateIncluded on completion
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Project-based learning

Build real work you can show in interviews.

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Lifetime access

Revisit lessons anytime after you enroll.

✦
Mentor support

Learn with guidance from industry experts.

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Career-ready skills

Practical modules aligned to real roles.

Overview

What this course covers

A complete data science journey covering Python, statistics, data wrangling, visualisation, and machine learning. You will work through real datasets to build a portfolio that proves you can deliver insight end to end.

Outcome

Finish with practical skills and a portfolio-ready project in Data Science you can use for jobs, freelancing, or your next role.

Skills

What you'll learn

  • Python for data science

  • Statistics and probability essentials

  • Data cleaning and wrangling

  • Exploratory data analysis & visualisation

  • Building predictive models

  • Communicating results to stakeholders

Audience

Who this course is for

Beginners

Starting Data Science from scratch and want a clear path.

Students & grads

Building a portfolio and interview-ready skills.

Working pros

Upskilling into data roles with practical projects.

Before you start

Prerequisites

  • Basic computer and internet skills
  • Curiosity and consistency to practise weekly
  • Open to learners of every background

Syllabus

Curriculum overview

8 modules · week-by-week topics · labs · certificate on completion

01Week 1: Python & Tools Setup

Hands-on lessons and a guided exercise covering python & tools setup.

Topics covered

  • Syntax & tooling
  • Core data structures
  • Functions & modules
  • Practice exercises

Lab: Practice lab: Python & Tools Setup

02Week 2: Statistics for Data Science

Hands-on lessons and a guided exercise covering statistics for data science.

Topics covered

  • Core concepts
  • Hands-on practice
  • Real dataset / case
  • Review checkpoint

Lab: Practice lab: Statistics for Data Science

03Week 3: Data Wrangling with Pandas

Hands-on lessons and a guided exercise covering data wrangling with pandas.

Topics covered

  • Core concepts
  • Hands-on practice
  • Real dataset / case
  • Review checkpoint

Lab: Practice lab: Data Wrangling with Pandas

04Week 4: Exploratory Data Analysis

Hands-on lessons and a guided exercise covering exploratory data analysis.

Topics covered

  • Core concepts
  • Hands-on practice
  • Real dataset / case
  • Review checkpoint

Lab: Practice lab: Exploratory Data Analysis

05Week 5: Data Visualisation

Hands-on lessons and a guided exercise covering data visualisation.

Topics covered

  • Core concepts
  • Hands-on practice
  • Real dataset / case
  • Review checkpoint

Lab: Practice lab: Data Visualisation

06Week 6: Machine Learning Basics

Hands-on lessons and a guided exercise covering machine learning basics.

Topics covered

  • Concept overview
  • Guided walkthrough
  • Hands-on exercise
  • Checkpoint review

Lab: Practice lab: Machine Learning Basics

07Week 7: Model Evaluation

Hands-on lessons and a guided exercise covering model evaluation.

Topics covered

  • Concept overview
  • Workflow steps
  • Evaluation metrics
  • Mini project

Lab: Practice lab: Model Evaluation

08Week 8: End-to-End Data Project

Hands-on lessons and a guided exercise covering end-to-end data project.

Topics covered

  • Core concepts
  • Hands-on practice
  • Real dataset / case
  • Review checkpoint

Lab: Practice lab: End-to-End Data Project

After the course

You'll walk away ready

By completing this course, you will have the practical skills and a portfolio project in Data Science to confidently move forward in your career.

  • Job-ready data fundamentals
  • A completed capstone you can showcase
  • Certificate of completion from Syncpedia

Faculty

Your course mentor

Karthik SubramanianData Science Mentor

Next step

Enroll, or ask the team first.

Open another course, or call the Madhapur desk if you want a person to walk you through it.

Contact the team