Data Science Workflows with Jupyter Notebooks

This course delves into the intricacies of Data Science workflows utilizing Jupyter Notebooks, a pivotal tool for data analysis and visualization. Participants will engage in hands-on projects that cover the entire data science lifecycle, from data collection and cleaning to analysis and visualization. The interactive format encourages collaboration and the sharing of insights, culminating in the opportunity to publish results in Cademix Magazine, showcasing participants' skills to a broader audience.
Throughout the course, learners will explore practical applications of Jupyter Notebooks in real-world scenarios, enhancing their ability to communicate complex data findings effectively. By the end of the program, participants will not only have a robust understanding of data science methodologies but will also have created a portfolio of work that demonstrates their proficiency in utilizing Jupyter Notebooks to solve data-driven challenges.
Introduction to Jupyter Notebooks and Setup
Data Acquisition Techniques: APIs, Web Scraping, and Databases
Data Cleaning and Preprocessing with Pandas
Exploratory Data Analysis (EDA) Techniques
Data Visualization Best Practices using Matplotlib and Seaborn
Statistical Analysis and Hypothesis Testing
Machine Learning Basics: Supervised vs. Unsupervised Learning
Implementing Machine Learning Models with Scikit-Learn
Model Evaluation and Selection Techniques
Final Project: End-to-End Data Science Workflow using Jupyter Notebooks

Equip participants with the skills to effectively utilize Jupyter Notebooks for comprehensive data science workflows, enabling them to tackle real-world data challenges.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic understanding of programming (preferably Python) and familiarity with data analysis concepts.