Skill Development
Data Science
Master data analysis, machine learning and visualisation. Extract insights from data and drive smart business decisions.
4.8 rating
400+ enrolled
3 Months
Beginner to Advanced
Prerequisite Knowledge: NONE | Education Background: ANY
Start Your Data Science Journey
Join 400+ students already enrolled. Get certified and job-ready.
Enroll Now
Enquire Now
This program includes
Industry certificate
Live + recorded sessions
Job placement support
Lifetime access to material
Small batch — personal attention
15+ language support
Recognition
Certification
Official Certification
Earn your certificate and stand out in the job market
Data Science
Industry recognised certificate
Add to LinkedIn & resume
QR code verification
What You’ll Achieve
Course Outcomes
Collect, clean and analyse large datasets using Python
Build and evaluate machine learning models end-to-end
Create compelling data visualisations with Tableau and Power BI
Apply statistical analysis and hypothesis testing
Work with SQL databases for data extraction
Build predictive models for real business problems
Deploy ML models using Flask and cloud platforms
Get job-ready for Data Analyst and Data Scientist roles
Skill Coverage
Skill-Sets Covered in Data Science Program
Data Analysis
Data Visualisation
Machine Learning
Statistics & Probability
SQL & Databases
Deep Learning Basics
Predictive Analytics
Model Deployment
Tools & Technologies You Will Learn
Python
Pandas
NumPy
MySQL
Jupyter
Scikit-learn
TensorFlow
MatplotlibPower BI, Tableau, Excel, Flask & more
Prerequisite Knowledge
NONE REQUIRED
Education Background
ANY
Age Group
18+ Years
Mode
Classroom + Online
Curriculum
Course Syllabus
1Introduction to Data Science
- Data science lifecycle — problem, data, model, deploy
- Types of data — structured, unstructured, semi-structured
- Tools setup — Python, Jupyter, Anaconda
- Real-world data science use cases across industries
2Python for Data Science
- Python basics, data types and control flow
- NumPy — arrays and matrix operations
- Pandas — DataFrames, groupby, merge, pivot
- Data cleaning — handling nulls, duplicates, outliers
3SQL for Data Analysis
- SQL queries — SELECT, WHERE, GROUP BY, JOIN
- Subqueries, window functions and CTEs
- Connecting Python to MySQL and PostgreSQL
- Real project: Sales data analysis with SQL
4Statistics & Exploratory Data Analysis
- Descriptive statistics — mean, median, mode, variance
- Probability distributions and the normal curve
- Hypothesis testing — t-test, chi-square, ANOVA
- EDA techniques with Seaborn and Matplotlib
5Data Visualisation
- Matplotlib and Seaborn — bar, line, heatmap, scatter charts
- Power BI — dashboards, slicers, DAX basics
- Tableau — connecting data, building viz, storytelling
- Plotly for interactive visualisations
6Machine Learning for Data Science
- Supervised learning — regression and classification
- Unsupervised learning — K-means clustering
- Feature engineering and selection
- Model evaluation — cross-validation, ROC-AUC
7Projects & Model Deployment
- End-to-end project: Customer churn prediction model
- Deploying models with Flask REST API
- Hosting on cloud — AWS and Heroku
- GitHub portfolio and Kaggle profile building
8Career Preparation
- Data science interview questions and patterns
- Resume building and LinkedIn optimisation
- Kaggle competitions for experience
- Roadmap — Data Analyst to Data Scientist to ML Engineer
