What you'll be able to do
- Clean and analyze real datasets with Pandas and NumPy
- Visualize data and run EDA with Matplotlib and Seaborn
- Build and evaluate machine-learning models with scikit-learn
- Apply NLP basics and understand the deep-learning workflow
- Deploy a trained model and tune it for better performance
Before you start
- A laptop that can run Python and Jupyter / Anaconda
- Basic programming logic helps but is not required
- School-level maths comfort (no heavy theory needed)
Tools & technologies
The syllabus
Introduction to Python & Installation
Set up the data-science toolchain and write your first Python.
- Why Python for Data Science?
- Installing Python, Jupyter Notebook, and Anaconda
- Python basics: variables, data types, and operators
- Day 1 checkpoint
Control Flow & Functions
Control program flow and write reusable functions.
- Conditional statements and loops
- Functions and lambda functions
- List comprehensions
- Day 2 checkpoint
Python Data Structures
Use core containers and manipulate text.
- Lists, tuples, sets, and dictionaries
- String manipulation
- Regular expressions in Python
- Day 3 checkpoint
File Handling & Exception Handling
Read/write data files and handle errors safely.
- Reading and writing files (CSV, JSON)
- Exception handling with try-except
- File and exception handling lab
- Day 4 checkpoint
Introduction to NumPy
Work with n-dimensional arrays and vectorized math.
- Creating and manipulating arrays
- Mathematical and statistical functions
- NumPy practice lab
- Day 5 checkpoint
Introduction to Pandas
Use Series and DataFrames and move data in/out.
- Series and DataFrames
- Data import and export (CSV, JSON, Excel)
- Day 6 checkpoint
Data Cleaning with Pandas
Clean and reshape a real dataset end to end.
- Handling missing values
- Data transformation and feature engineering
- Mini Project 1: Analyzing the COVID-19 dataset with Pandas
- Day 7 checkpoint
Introduction to Matplotlib
Build and customize core plot types.
- Line, bar, scatter, and pie charts
- Customizing plots
- Day 8 checkpoint
Advanced Visualization with Seaborn
Visualize distributions and relationships with Seaborn.
- Histograms, box plots, and violin plots
- Heatmaps and pairplots
- Day 9 checkpoint
Data Aggregation & Grouping in Pandas
Summarize and combine datasets.
- Pivot tables and the GroupBy function
- Merging and concatenating datasets
- Day 10 checkpoint
Exploratory Data Analysis (EDA) Techniques
Profile data, spot outliers, and engineer features.
- Detecting outliers
- Feature engineering for EDA
- EDA techniques lab
- Day 11 checkpoint
Working with Time Series Data
Handle dates/times and analyze trends.
- The datetime module and pandas time series
- Analyzing trends and patterns
- Day 12 checkpoint
Web Scraping with BeautifulSoup
Extract structured data from web pages.
- Extracting data from websites with BeautifulSoup
- Web scraping lab
- Day 13 checkpoint
API Integration in Python
Pull data from public APIs and analyze it.
- Fetching data from open APIs with requests
- Mini Project 2: EDA on a sales dataset
- Day 14 checkpoint
Understanding Machine Learning
Frame ML problems and know the main learning types.
- What is machine learning?
- Types of ML: supervised, unsupervised, reinforcement
- Day 15 checkpoint
Introduction to Scikit-Learn
Preprocess data and select features for modeling.
- Data preprocessing: scaling, encoding, splitting
- Feature selection
- Scikit-Learn preprocessing lab
- Day 16 checkpoint
Regression Models
Build and interpret regression models.
- Linear regression
- Multiple linear regression
- Regression lab
- Day 17 checkpoint
Classification Models
Build logistic-regression and tree classifiers.
- Logistic regression
- Decision trees
- Classification lab
- Day 18 checkpoint
Model Evaluation Techniques
Measure model quality honestly.
- Accuracy, precision, recall, and F1 score
- The confusion matrix
- Model evaluation lab
- Day 19 checkpoint
Unsupervised Learning: Clustering
Group unlabeled data with clustering.
- K-Means clustering
- Hierarchical clustering
- Clustering lab
- Day 20 checkpoint
Principal Component Analysis (PCA)
Reduce dimensionality and run a regression project.
- Dimensionality reduction with PCA
- Mini Project 3: Predicting house prices using regression
- Day 21 checkpoint
Natural Language Processing (NLP) Basics
Preprocess text and run sentiment analysis.
- Text preprocessing: tokenization, stemming, lemmatization
- Sentiment analysis
- Day 22 checkpoint
Introduction to Deep Learning
Understand neural networks and the Keras workflow.
- Basics of neural networks
- TensorFlow and Keras overview
- Day 23 checkpoint
Model Deployment
Serve a trained model behind a Flask API.
- Deploying ML models using Flask
- Model deployment lab
- Day 24 checkpoint
Real-World Case Study
See how real DS projects are scoped and run.
- Understanding real-life data science projects
- Case study walkthrough
- Day 25 checkpoint
Final Capstone Project Development
Build the end-to-end churn-prediction project.
- Capstone brief: predicting customer churn for a telecom company
- Building the churn-prediction pipeline
Model Optimization & Hyperparameter Tuning
Squeeze more performance with systematic tuning.
- Grid search and random search
- Hyperparameter tuning lab
- Day 29 checkpoint
Project Presentation & Certification
Present results well and plan next steps.
- Presenting your data science project
- Preparing for certification and next steps
Capstone: Predicting Customer Churn for a Telecom Company
You are working as a data science intern for a telecom company.
How you'll learn
This course's public GitHub repo is shared when the next cohort opens —ask us for a preview and we'll send you the link.
The certificate you'll earn
- Issued on program completion, with your name, course and grade.
- Carries a unique certificate ID and QR code.
- Anyone can confirm it in seconds on ourcertificate verification page — recruiters included.
Who this course is for
Anyone comfortable with basic programming logic who wants a practical entry into machine learning. Mathematics is introduced where it’s needed, in plain language, with code alongside.
Upcoming cohorts
New cohort dates are announced regularly — send an enquiry and we'll reserve you a seat in the next one.
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A project-driven path into data science with Python — NumPy, Pandas, visualization, EDA, and machine learning with scikit-learn, ending in a churn-prediction capstone.
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- ~21 hours
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- New cohort announced soon