Coming soon
Python for Data Science
Clean, analyse and chart data with pandas, NumPy and Matplotlib on real datasets.
- 3 months
- Beginner to intermediate
- 6 parts · 48 topics
Send us one message on WhatsApp, and we will tell you the launch date.
Planned syllabus
The full roadmap, basic to advanced
Every part builds on the one before it. You finish each part with a real project.
- Parts
- 6
- Duration
- 3 months
- Topics
- 48
- Projects
- 6
Part 1Month 1: Python for data
- Python refresher: lists, dicts, functions
- Jupyter Notebook and Google Colab
- NumPy arrays and vector maths
- pandas Series and DataFrame
- Reading CSV, Excel and JSON files
- Selecting, filtering and sorting data
- groupby and aggregation
- Merging and joining tables
Project Analyse a real sales dataset and find the top products.
Part 2Month 1: Cleaning data
- Finding and fixing missing values
- Removing duplicates
- Fixing data types and dates
- Text cleaning with string methods
- Outliers: find and handle them
- Feature engineering basics
- Reshaping: pivot and melt
- A cleaning checklist for every dataset
Project Clean a messy dataset and write what you fixed.
Part 3Month 2: Visualization
- Matplotlib basics: line, bar, scatter
- Seaborn for statistical charts
- Histograms and box plots
- Correlation heatmaps
- Interactive charts with Plotly
- Choosing the right chart
- Telling a story with data
- Exploratory data analysis (EDA) end to end
Project A dashboard of Indian cricket or weather data.
Part 4Month 2: Statistics
- Mean, median, mode and spread
- Probability basics
- Distributions: normal, binomial
- Sampling and the central limit theorem
- Hypothesis testing and p-values
- t-test and chi-square test
- Correlation vs causation
- A/B testing for business
Project Decide which of two website designs works better.
Part 5Month 3: Intro to machine learning
- What machine learning is
- Train and test split
- Linear regression
- Logistic regression
- Decision trees and random forests
- Accuracy, precision, recall, F1
- Cross-validation
- scikit-learn pipelines
Project Predict house prices from a real dataset.
Part 6Month 3: Capstone
- Picking a real dataset (Kaggle, government open data)
- Asking the right business question
- Full EDA and cleaning
- Building and comparing models
- A Streamlit dashboard
- Writing the project report
- GitHub portfolio and README
- Interview questions for data roles
Project A full data project on GitHub with a short report.
This is the planned syllabus. Small changes are possible before launch.
Want this course? Tell us.
Send one message on WhatsApp, and we will tell you as soon as the launch date is fixed.