Introduction to Data Science
❖ What is Data Science?
❖ Data Science vs. other fields (e.g.
Statistics, Computer Science)
❖ Applications of Data Science
❖ Tools and technologies used in Data
Science
Data Collection and Cleaning
Data sources and types
❖ Data collection techniques (e.g. scraping,
APIs)
❖ Data cleaning and preprocessing
❖ Data quality assessment and validation
Exploratory Data Analysis
❖ Data visualization
❖ Exploratory data analysis techniques (e.g.
clustering, dimensionality reduction)
❖ Descriptive statistics
Statistical Inference
❖ Probability theory and distributions
❖ Hypothesis testing
❖ Confidence intervals
❖ Regression analysis
Machine Learning
❖ Supervised learning
❖ Unsupervised learning
❖ Deep learning

Data Management and Storage
❖ Data management system
Data Visualization and
Communication
❖ Effective data visualization techniques
❖ Communicating insights and results to
stakeholders
❖ Presenting data and analysis in a clear and
concise manner.
❖ PowerBI, Tableau and Hadoop
Project
❖ Apply Data Science techniques to a realworld problem.
❖ Develop a project from data collection and
cleaning to analysis and visualization
❖ Present the results to stakeholders in a
clear and concise manner