PossibleNewFDSIIOutline.md

Module 1: Introduction to Data Science

• Week 1: Overview of Data Science
  • What is Data Science? Scope and Applications
  • The Data Science Process: From Data Collection to Model Deployment
  • Introduction to the Data Science toolkit: Python, SQL, Git

Module 2: Data Manipulation and Analysis

• Week 2: SQL for Data Science
  • Basics of SQL: Queries, Joins, Aggregations
  • Integrating SQL with Python (using libraries like SQLAlchemy)
  • Hands-on: SQL exercises on real-world datasets
• Week 3: Data Wrangling with Pandas
  • Advanced Data Manipulation with Pandas
  • Data Cleaning Techniques
  • Hands-on: Cleaning and preparing a dataset for analysis

Module 3: Data Visualization

• Week 4: Visualization with Matplotlib and Seaborn
  • Creating plots, histograms, scatter plots, and interactive visualizations
  • Visualizing multi-dimensional datasets
  • Hands-on: Exploratory Data Analysis (EDA) on a real-world dataset

Module 4: Web Scraping and Data Collection

• Week 5: Introduction to Web Scraping
  • HTML basics and web structure
  • Using Python libraries (BeautifulSoup, requests) for web scraping
  • Ethical considerations and best practices
  • Hands-on: Scraping and compiling data from websites

Module 5: Introduction to Machine Learning

• Week 6-7: Basics of Machine Learning
  • Supervised vs Unsupervised Learning
  • Regression and Classification techniques
  • Clustering and Dimensionality Reduction
  • Hands-on: Building and evaluating simple machine learning models

Module 6: Intermediate Machine Learning

• Week 8-9: Advanced Machine Learning Techniques
  • Decision Trees, Random Forests, and Ensemble Methods
  • Introduction to Neural Networks and Deep Learning
  • Model Evaluation and Fine-Tuning
  • Hands-on: Advanced projects incorporating multiple techniques

Module 7: Practical Applications of Data Science

• Week 10: Time Series Analysis
  • Understanding and analyzing time-series data
  • Forecasting models
  • Hands-on: Predicting stock market trends or weather patterns
• Week 11: Natural Language Processing (NLP)
  • Basics of text processing and analysis
  • Sentiment analysis and text classification
  • Hands-on: Analyzing social media data or customer reviews

Module 8: Capstone Project and Industry Tools

• Week 12-13: Capstone Project
  • Working on an end-to-end Data Science project
  • Incorporating data collection, processing, analysis, and machine learning
• Week 14: Introduction to Industry Tools and Best Practices
  • Overview of tools like Tableau, PowerBI for data visualization
  • Introduction to cloud platforms (AWS, Azure) for Data Science
  • Data Science in the industry: Roles, expectations, and career paths