Introduction to Data Science
Artificial Intelligence (AI)
COURSE OVERVIEW
This three-day bootcamp is designed to demystify the field of Data Science for beginners. It bridges the gap between raw information and actionable insights, focusing on the Data Science Life Cycle: from asking the right questions to communicating findings through storytelling. Using Python as the primary tool, participants will learn how to clean "messy" data, perform exploratory analysis, and build their first predictive models. This course emphasizes practical skills over dense theory, ensuring you leave with a clear roadmap for your data journey.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Navigate the Data Pipeline: Understand the roles of Data Engineers, Data Scientists, and Data Analysts.
- Wrangle Data with Python: Use Pandas to filter, transform, and clean datasets for analysis.
- Discover Patterns: Apply Exploratory Data Analysis (EDA) to find correlations and trends.
- Master Data Storytelling: Create compelling visualizations with Matplotlib and Seaborn.
- Build Baseline Models: Understand the logic behind Linear Regression and Classification.
- Apply Ethical Frameworks: Identify bias in data and understand the importance of data privacy.
Duration: 3 Days / 24 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: Foundations and Data Manipulation
Focus: Understanding the field and mastering the tools of the trade.
- What is Data Science? The intersection of statistics, computer science, and domain expertise.
- Introduction to Python for Data: Setting up Jupyter Notebooks and learning NumPy fundamentals.
- Pandas Deep Dive:
- Loading data (CSV, Excel, SQL).
- Slicing, dicing, and filtering dataframes.
- Dealing with missing values and "dirty" data.
- Hands-on Lab: Taking a raw "Retail Sales" dataset and transforming it into a clean, structured format for analysis.
Day 2: Exploratory Data Analysis & Visualization
Focus: Extracting meaning and visualizing the "Why" behind the numbers.
- Statistical Thinking: Understanding mean, median, standard deviation, and distributions.
- Exploratory Data Analysis (EDA): Techniques for spotting trends, outliers, and correlations.
- The Art of Visualization:
- Choosing the right chart (Bar, Line, Scatter, Heatmap).
- Customizing plots for clarity and impact using Seaborn.
- Hands-on Lab: Visualizing a "Global Health" dataset to identify key factors influencing life expectancy across different regions.
Day 3: Modeling, Ethics, and the Future
Focus: Predicting the future and staying responsible.
- Introduction to Machine Learning: The difference between "Classic" programming and "Learning" from data.
- Your First Models:
- Regression: Predicting numerical values (e.g., forecasting revenue).
- Classification: Categorizing data (e.g., Identifying spam vs. not spam).
- The Ethical Data Scientist: Recognizing bias in algorithms and the importance of GDPR/Data Privacy.
- Final Project: Building a "mini-analysis" end-to-end—from cleaning a dataset to presenting a final predictive insight.
The Road Ahead: Careers in Data Science and building your professional portfolio.
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