Master Data Science with Python
This intensive, fast-paced course introduces you to core data science concepts. Learn Python, data handling, visualization, and machine learning through hands-on practice.
- 1:1 Mock Interview Sessions
- Master Classes with Industry Experts
- Hands-on Projects & Real-world Examples
Recommended for Students and Working Professionals
- 4 Core Modules
- 28 Days
- 1 final Project
CURRICULUM
Your Learning Journey
Our curriculum is researched, developed & updated by understanding the global scope & job demands. Conducted by industry-leading expert instructors, the program offers more than 85% of an in-depth practical approach backed by essential theoretical frameworks.
CLASS FORMAT
Physical & Online Classes (Day and Night)
Skills you'll learn
- Python Fundamentals: Write clean, efficient, and readable code.
- Data Manipulation: Clean, transform, and analyze datasets with Pandas.
- Data Visualization: Create compelling charts and visual insights.
- Machine Learning Basics: Build and evaluate predictive models.
Module 1: Python for Data Science
1.1 Environment Setup & Python Refresher
- Working in Jupyter Notebook or Google Colab
- Variables, loops, and conditionals in Python
- Data types: strings, lists, dictionaries
- Hands-on writing code in Google Colab
1.2 Data Cleaning and Transformation
- Reading and exploring datasets (CSV/Excel)
- Cleaning data: trimming whitespace, renaming columns
- Basic transformations using built-in Python functions
- Lab: Write Python scripts to clean and manipulate a simple dataset
Module 2: Working with Data Using Pandas
2.1 Introduction to Pandas
- Series and DataFrames
- Reading data with read_csv()
- Indexing, selecting, and slicing data
2.2 Filtering, Sorting, and Summarizing Data
- Filtering rows and columns
- Sorting by values or index
- Grouping and aggregating data
- Lab: Clean and explore a CSV dataset using Pandas
Module 3: Data Visualization
3.1 Creating Basic Charts with Matplotlib
- Line plots, bar charts, scatter plots
- Customizing titles, labels, and legends
- Saving and exporting plots
3.2 Advanced Visualization with Seaborn
- Histograms, boxplots, heatmaps
- Pair plots and categorical plots
- Interpreting visual trends for insight
- Lab: Create a visual report from a dataset
Module 4: Intro to Machine Learning
4.1 Machine Learning Fundamentals
- What is machine learning?
- Supervised vs. unsupervised learning
- Introduction to features and labels
4.2 Building a Simple Model
- Using Scikit-Learn for Linear Regression
- Train/test split
- Evaluating model performance (MSE, R²)
- Lab: Train and test a basic ML model using Scikit-Learn
Projects & Hands-On Labs
This course emphasizes practical learning through hands-on labs and real-world datasets. Each module includes a project designed to help learners apply Python and data science concepts step by step.
Module 1
Python Data Cleaning Project
Lab:
Clean and Manipulate a Dataset Using Python
Learners write Python scripts to load, clean, and transform real-world datasets using core Python concepts.
Deliverable
A cleaned and transformed dataset with Python scripts.
Module 2
Pandas Data Exploration Project
Lab:
Clean and Explore a CSV Dataset Using Pandas
Learners use Pandas to read, filter, sort, and summarize structured datasets.
Deliverable
A Pandas-based data exploration notebook.
Module 3
Data Visualization Project
Lab:
Create a Visual Data Report
Learners visualize datasets using Matplotlib and Seaborn to uncover trends and patterns.
Deliverable
A visual report with charts and insights.
Module 4
Machine Learning Mini Project
Lab:
Train and Test a Basic Machine Learning Model
Learners build a simple predictive model using Scikit-Learn and evaluate its performance.
Deliverable
A trained ML model with performance evaluation.
- Final Project
Capstone Mini Project
Develop and present a complete AI-powered solution that combines skills learned from all modules.
- Deliverables
- Data loading and cleaning steps
- Visual analysis
- Model training and evaluation
- Clear explanations and insights
- Optional: Include a 2–3 slide presentation or summary of your findings
- Project Requirements
- Select a real-world dataset (CSV or open dataset)
- Perform data cleaning and transformation
- Create meaningful visualizations using Matplotlib or Seaborn
- Build a simple predictive model (e.g., Linear Regression or Classification)
- Interpret findings and present results