Welcome to the Comprehensive Python Programming and Data Analysis Course!
Embark on a transitional 11-week exploration where the power of Python programming converges with the art of data analysis. In this dynamic course, you will not only master the fundamentals of Python but also acquire the skills to decipher, analyze, and visualize data effectively.
Course overview
In an era where data reigns supreme, the ability to harness the potential within it is a vital skill. This course is meticulously designed to take you from a Python novice to a proficient data analyst. Each week unveils new layers of knowledge, blending theory with hands-on projects to ensure a well-rounded understanding.
What You’ll Learn:
In an era where data reigns supreme, the ability to harness the potential within it is a vital skill. This course is meticulously designed to take you from a Python novice to a proficient data analyst. Each week unveils new layers of knowledge, blending theory with hands-on projects to ensure a well-rounded understanding.
1. Python Programming Basics: Lay a solid foundation in Python, covering everything from syntax to control flow, and set up your environment for seamless development.
2. Data Analysis with Python: Dive into the world of data analysis, exploring the intricacies of importing, cleaning, and visualizing data. Develop a keen eye for patterns and insights that drive informed decision-making.
3. Advanced Techniques: Elevate your Python prowess with advanced programming concepts, functions, loops, and conditional statements. Leverage Python’s robust data structures and NumPy for nuanced numerical analysis.
4. Data Cleaning and Preprocessing: Tackle real-world challenges as you navigate through handling missing data, outliers, and text data analysis. Merge datasets seamlessly and ensure data integrity.
5. Advanced Data Visualization: Master the art of visual storytelling with Matplotlib, Seaborn, Bokeh, and Plotly. Craft interactive dashboards and geovisualizations that breathe life into your data.
6. Statistical Analysis: Uncover the statistical foundations of data analysis, from descriptive statistics to hypothesis testing and linear regression. Apply statistical models to draw meaningful conclusions from datasets.
7. Final Project: Synthesize your skills in a real-world data analysis project. Present your findings, receive peer reviews, and cap off the course by training a simple machine learning model for classification.
Why This Course:
This course is a blend of teacher-led and student-led learning model. The teacher facilitates instruction and provides some small bits of direct teaching in class or on zoom, recorded video access links but students have a significant amount of choice in the order in which they work, though they will find that the modules are presented in a coherent sequence that will enhance transformational learning, however, they have the choice to learn in whatever order works well for them.
They can choose to collaborate on different projects per time. Students have choice in review activities, whether or not to attempt bonus homework assignments, or to do extra practice where indicated in various modules.
All modules of the course will be implemented through mixed of in person, synchronous instruction with asynchronous practice options with practical data from real world industries like hospitals, schools, banks etc. The grading model will be a Pass or Fail model. Where Pass means your project work and collaborative efforts in learning and the outcomes have satisfactorily proven to the board of reviewers to demonstrate hands on knowledge of data analysis with python as required by the standard set by the National Universities Commission as documented in the curriculum attached.
