Data Science Program (1) : Foundations of Data Science

Module 1: Introduction to Data Science

• History of Data Science
  Explore the evolution and key milestones in data science, from early statistical methods to modern big data analytics.

• Applications of Predictive Modeling
  Real-world examples of predictive modeling across industries such as finance, healthcare, marketing, and manufacturing.

• Skills of a Data Scientist
  Overview of core skills including programming (Python, SQL), statistics, machine learning, data wrangling, communication, and domain knowledge.

Module 2: Python Programming for Data Science

• Basic Python syntax, data types, variables, expressions

• Functions, modules, and data structures (lists, dictionaries, tuples)

• File I/O for data handling

Module 3: Data Analysis with NumPy and Pandas

• Numerical computing with NumPy: arrays, mathematical operations

• Data manipulation with Pandas: Series, DataFrames, file reading/writing, data cleaning

Module 4: Data Collection and Cleaning

• Techniques for data sourcing, web scraping

• Handling missing data, data preprocessing

Module 5: Descriptive Statistics and Data Visualization

• Measures of central tendency and variability

• Visualization techniques using Matplotlib and Seaborn

Module 6: Time Series Analysis and Forecasting

• Fundamentals of time series data

• Forecasting models and applications

Module 7: Introduction to Machine Learning

• Overview of supervised and unsupervised learning

• Building and evaluating predictive models: regression, classification

Module 8: Advanced Machine Learning Concepts

• Probabilistic classification, logistic regression

• Decision trees, support vector machines, ensemble methods

Module 9: Databases and Data Privacy

• Relational databases and SQL basics

• Data privacy principles, compliance and ethical data handling

This curriculum is designed to provide foundational knowledge and practical skills for aspiring data scientists, suitable for a 3-6 month self-paced or instructor-led online course

Sources : The Data Science Handbook, 2nd Edition