Data Science

Curriculum

1. Week 1

   Introduction to Data Science

   Data Science fundamentals, analytics fundamentals, the Data Science workflow and real-world applications of data science.

2. Data Collection and Preprocessing

   Data collection, data manipulation and preprocessing techniques, and preparing datasets for analysis and machine learning.

3. Week 2

   Programming for Data Science

   Python for data analysis and problem solving, along with programming techniques for building efficient data workflows.

4. Statistics and Probability

   Statistics fundamentals, probability concepts and statistical foundations required for data analysis and machine learning.

5. Data Visualization

   Data visualization techniques, analyzing and presenting data, and creating visual representations that communicate meaningful insights.

6. Week 3

   Machine Learning for Data Science

   Machine learning fundamentals, applying ML techniques to data science problems and building predictive models.

7. Advanced Machine Learning

   Advanced machine learning concepts, predictive analytics and ML-based approaches to solving complex data problems.

8. Week 4

   Big Data Technologies

   Big-data concepts, technologies for handling large-scale datasets and data processing workflows.

9. Data Engineering

   Data workflows, data preparation and processing, and techniques for building efficient and reliable data workflows.

10. Data Science in the Cloud

    Cloud-based data science, cloud data workflows and applying data science techniques in cloud environments.

What you walk away with

• A practical data science project demonstrating data preprocessing, visualization, analysis and machine learning
• Hands-on experience with Python, statistics, machine learning, big data technologies and cloud-based data workflows
• A polished portfolio and GitHub profile showcasing data science projects and interview-ready skills