The curriculum is designed to build a strong foundation in data science using Python. Key modules include:

1. Data Manipulation: Working with NumPy arrays and Pandas DataFrames for data cleaning and transformation.
2. Data Visualization: Creating insightful charts using Matplotlib and Seaborn to explore patterns and trends.
3. Machine Learning: Implementing supervised and unsupervised models using Scikit-learn, including regression, classification, and clustering.
4. Model Evaluation: Understanding metrics like accuracy, precision, recall, and ROC curves to assess model performance.

Each module combines theory with practical application to ensure deep understanding:

• Interactive coding sessions using Jupyter Notebooks and real-world datasets.
• Weekly assignments focused on data wrangling, visualization, and predictive modeling.
• Peer reviews and instructor feedback to refine analytical approaches.
• Capstone project involving end-to-end analysis and presentation of insights.

The course runs over 10 weeks with the following weekly structure:

• Two theory sessions covering core concepts and techniques.
• One lab session focused on hands-on coding and project development.
• Capstone project begins in week 8 and continues through week 10.

This immersive program introduces learners to the full spectrum of data science workflows. From data acquisition and cleaning to model building and evaluation, students gain practical skills applicable across industries.

By the end of the course, participants will be able to independently conduct data analyses, build predictive models, and communicate findings effectively.

• Master essential Python libraries for data science and machine learning.
• Build a portfolio of projects showcasing analytical and coding skills.
• Prepare for roles such as Data Analyst, Machine Learning Engineer, or Business Intelligence Specialist.
• Gain confidence in working with structured and unstructured data.