GEMS NMS

Data Science 101 Workshop
This workshop provided an introductory session aimed at familiarizing students with the fundamentals of data science, including data analysis and machine learning.

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Workshop Outline

Introduction to Data Science

• Topics Covered: We began with a high-level overview of data science, exploring its importance and common real-world applications such as recommendation systems and healthcare analytics. Key steps in any data science project were covered, including Data Collection, Cleaning, Analysis, and Modeling.

• Skills Taught: Students gained a foundational understanding of the data science field and its impact across industries, setting a strong context for further exploration.

Basic Data Analysis with Python

• Topics Covered: Using a small dataset, we demonstrated how to load, inspect, and visualize data with Python libraries. Commands like head() and describe() were used to explore the dataset, followed by visualizations (bar charts, histograms) to identify patterns.

• Skills Taught: Students learned basic data handling techniques in Python, as well as how to create visualizations with Pandas, Matplotlib, and Seaborn to extract initial insights.

Hands-on Example: Data Analysis

• Topics Covered: We conducted a practical, step-by-step exploratory data analysis (EDA), focusing on data cleaning, handling missing values, and calculating summary statistics like mean and median. Participants identified patterns and relationships within the data.

• Skills Taught: Attendees learned how to perform EDA and derive insights from data, building their skills in data cleaning and basic statistical analysis.

Introduction to Machine Learning

• Topics Covered: We discussed machine learning basics, differentiating supervised from unsupervised learning. A simple predictive model (e.g., linear regression or k-nearest neighbors) was introduced using a small dataset to show how machine learning models are trained and tested.

• Skills Taught: Students gained a foundational understanding of model building and evaluation with Scikit-learn, experiencing firsthand how data is used for predictions.