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Data Science Professional Program
Data Science
Build predictive models with Python, statistics and machine learning.
The Data Science programme covers the full path from data to model: Python, SQL, statistics, and libraries such as Pandas, NumPy, Scikit-learn and TensorFlow. You'll build, evaluate and present machine learning models on real datasets.
What you'll learn
- Data science fundamentals
- Python for data analysis
- Data wrangling and cleaning
- Exploratory data analysis (EDA)
- Statistics and probability
- Machine learning (supervised and unsupervised)
- Deep learning foundations
- Data visualization (Matplotlib, Seaborn, Power BI)
- SQL for data science
- Model evaluation and tuning
- Real-world data projects
Curriculum
8 modules
Weeks 1–2Introduction to Data Science
- The data science lifecycle
- Data roles: analyst, engineer, scientist
- Key data science tools
- Setting up Python (Anaconda, Jupyter)
Weeks 3–4Python for Data Science
- Python basics and data structures
- Data manipulation with Pandas and NumPy
- Working with CSV, JSON and APIs
- Data cleaning and preprocessing
Weeks 5–6Data Visualization
- Visualizing data with Matplotlib and Seaborn
- Storytelling with data
Weeks 7–8Statistics and Probability
- Descriptive and inferential statistics
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Weeks 9–11Machine Learning
- Introduction to machine learning
- Supervised vs unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- Clustering and k-means
- Model evaluation metrics
Weeks 12–13Deep Learning
- Introduction to neural networks
- TensorFlow and Keras fundamentals
- Building simple neural networks
- Image and text data basics
Week 14SQL for Data Science
- SQL basics and joins
- Aggregations and subqueries
- Using SQL for analytics
Weeks 15–16Capstone Project
- End-to-end machine learning project
- Problem definition and data collection
- Model building and deployment
- Project presentation
Who it's for
- Aspiring data scientists and analysts
- Developers moving into AI roles
- Business professionals who want data-driven insight
- Students and graduates pursuing careers in tech
- Entrepreneurs building data-based products
Career paths
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- AI Specialist
- Data Engineer
- Research Analyst
- Business Intelligence Developer
Tools
- Python (Pandas, NumPy, Scikit-learn, TensorFlow)
- SQL
- Power BI / Tableau
- Jupyter Notebook
- GitHub
By the end you can
- Analyze and interpret complex datasets
- Build and evaluate machine learning models
- Communicate insights through visualizations
- Apply data science to real-world problems