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Data Analytics Course

Data Analytics Course

Gain job‑ready skills in data cleaning, analysis, visualisation and basic machine learning using tools like Excel, SQL, Python and BI platforms. Learn how to turn raw data into clear, actionable insights.

Key topics: Excel, SQL, Python / R, Statistics, Dashboards, Intro to Machine Learning Professional Course · Duration as per institution (often 4–6 months) Beginner‑friendly · Ideal for students & working professionals · 4.7 / 5 learner feedback

About This Course

This Data Analytics Course is designed to help you understand the complete analytics workflow – from collecting and cleaning data, to analysing it and presenting insights for business decisions. You start with the basics of spreadsheets, statistics, SQL and Python / R, and progressively move into visualisation and machine learning.

The course is highly practical, with labs, case studies and project work. You will work with real‑world datasets, build dashboards, run analyses and explain your findings in a clear, business‑friendly way. By the end, you’ll be ready for entry‑level data analytics, BI and reporting roles, or to strengthen your profile in your current job.

Syllabus Overview

Module 1 Foundations: Excel, SQL & Basic Statistics
  • Introduction to Data & Analytics – lifecycle, roles and use cases
  • Excel for Data Analysis – formulas, pivot tables, charts
  • Descriptive Statistics – mean, median, variation, correlation
  • Database Concepts & SQL Basics (SELECT, FILTER, JOIN, GROUP BY)
  • Working with CSV / Excel files & simple data cleaning
  • Communicating insights with basic reports & charts
Module 2 Python / R, EDA & Visualisation
  • Introduction to Python / R for Data Analysis
  • Data Wrangling – importing, cleaning & transforming data
  • Exploratory Data Analysis (EDA) – patterns, outliers & distributions
  • Data Visualisation – bar, line, scatter, box plots & custom charts
  • Building Interactive Dashboards (Power BI / Tableau basics)
  • Mini‑project: Analyse and present insights from a real‑world dataset
Module 3 Intro to Machine Learning & Applied Analytics
  • Probability & Inferential Statistics (confidence intervals, hypothesis tests)
  • Machine Learning Basics – regression & classification concepts
  • Building & Evaluating Simple ML Models
  • Case Studies in Marketing, Finance or Operations Analytics
  • Data Storytelling – structuring presentations for stakeholders
  • Best Practices: Ethics, privacy & working with real‑world data

*Exact modules and tools may vary as per institution and delivery format.

Projects Hands‑on Assignments & Capstone
  • Regular lab‑based assignments in Excel, SQL and Python / R
  • End‑to‑end EDA & dashboard mini‑projects
  • Domain‑based analytics case studies (e.g. sales, churn, HR)
  • Capstone project where you pick a dataset, analyse it and present findings
  • Portfolio preparation – GitHub / online profile with your project work

Top Sectors Using Data Analytics

Analytics skills are in demand across IT services, product companies, consulting, e‑commerce, BFSI and startups.

Microsoft
Amazon
Google
IBM
Accenture
TCS
Deloitte
EY

What You Will Gain from This Data Analytics Course

Graduate with a blend of analytical thinking, programming skills and business understanding.

ST

Strong Statistical Base

Build a clear understanding of descriptive and inferential statistics for data‑driven decisions.

PY

Programming Confidence

Write clean code in Python / R to manipulate, analyse and visualise real‑world datasets.

DB

Data Management

Work with SQL databases, join tables and design efficient queries for analytics.

ML

Machine Learning Basics

Apply basic regression and classification techniques to solve simple business problems.

BI

BI & Dashboards

Create interactive dashboards and automated reports for business stakeholders.

CR

Career Readiness

Develop communication, teamwork and presentation skills for analytics interviews and roles.

Sales & Customer Analytics Dashboard
Revenue Users Conversion Churn

Tools & Platforms You May Work With

Become hands‑on with the core tools used in analytics, BI and data science teams.

Python
R (stats & ML)
RStudio / IDEs
MS Excel
Power BI / BI tools
Tableau
SQL / MySQL
Jupyter Notebook
Git & GitHub

Careers & Learning Journey

See how this Data Analytics Course builds your skills and prepares you for analytics careers.

F

Full‑Stack Analytics Exposure

Learn to collect, clean, analyse and present data – moving from raw files to dashboards and simple models that answer business questions.

R

Career Roles You Can Target

Use your course projects and portfolio to target high‑growth entry‑level analytics roles.

Data Analyst Business Intelligence (BI) Analyst Reporting / MIS Executive Junior Data Scientist Analytics Consultant (Entry‑level) Product / Marketing / Operations Analyst
J

Your Learning Journey

A clear, module‑wise path from basics of tools and stats to applied analytics projects.

Module 1: Foundations in Excel, SQL, basic statistics & understanding data problems.
Module 2: Python / R, data cleaning, EDA, visualisation & dashboard creation.
Module 3: Intro ML, domain case studies, capstone project & portfolio building.

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