Program Structure:
Duration: 3 Months (12 weeks)
Total Hours: 72 hours (3 hours/day x 2 days/week x 12 weeks)
Time: 5 – 8 pm WAT
Days: Fridays and Saturdays
Credit Units: 12 Credit Units (1 credit unit = 6 hours of instruction)
Course Description:
This course offers an intensive, hands-on introduction to data science, guiding students through foundational Python programming, data wrangling, statistical analysis, and machine learning techniques over a 3-month period. Through practical exercises, real-world projects, and a capstone assignment, participants will develop the skills necessary to collect, clean, analyze, and model data, preparing them for advanced applications in data science.
What You Will Learn:
By the end of this course, participants will be able to:
Master Python for Data Science:
- Understand Python programming fundamentals, including variables, data types, loops, and control flow, tailored for data science applications.
- Write clean, efficient Python code to solve real-world data problems.
Perform Data Wrangling and Exploration:
- Collect, clean, and preprocess datasets, handling missing data, outliers, and inconsistencies.
- Explore and analyze data using Python libraries like Pandas and NumPy to uncover insights and trends.
Conduct Statistical Analysis:
- Apply key statistical techniques, including descriptive and inferential statistics, to analyze datasets.
- Implement probability distributions and hypothesis testing for data-driven decision-making.
Build and Evaluate Machine Learning Models:
- Develop supervised and unsupervised machine learning models, including regression, classification, and clustering algorithms.
- Evaluate model performance using key metrics like accuracy, precision, and recall to make informed improvements.
Visualize Data Effectively:
- Create meaningful data visualizations using libraries such as Matplotlib and Seaborn to communicate insights.
- Understand how to craft visual reports that tell compelling data stories.
Work with Real-World Data Science Projects:
- Develop hands-on experience through real-world projects, applying Python, data wrangling, statistical analysis, and machine learning to solve complex problems.
- Complete a capstone project, integrating all learned skills to design, build, and present a full data science solution
