Python for Data Science

Python for Data Science

5-days of hands-on intensive workshop

Welcome to "Python for Data Science," a comprehensive course designed to equip you with the necessary skills and knowledge to dive into the world of data science using Python. This course covers everything from the foundational basics of Python programming to advanced data science techniques, including data manipulation, analysis, visualization, and machine learning. By the end of this course, you will be proficient in leveraging Python's powerful libraries to analyze and interpret complex datasets, extract meaningful insights, and make data-driven decisions.

Learning Outcomes

Upon successful completion of this course, you will be able to:

• Understand the fundamentals of Python programming and its application in data science.
• Perform data manipulation and cleaning using Pandas to prepare datasets for analysis.
• Analyze and interpret data using statistical methods and data visualization with Matplotlib and Seaborn.
• Apply NumPy for efficient numerical computations on large datasets.
• Implement machine learning algorithms to solve real-world problems using Scikit-learn.
• Evaluate and improve the performance of machine learning models.
• Understand the theoretical foundations behind machine learning algorithms and their practical applications.
• Develop a comprehensive portfolio of projects demonstrating your ability to solve data science problems with Python.

Prerequisites

• Basic understanding of programming concepts (variables, functions, loops).
• Familiarity with mathematical concepts (algebra, statistics).
• A willingness to learn and explore the vast domain of data science.

Course Outline

1. Introduction to Python for Data Science
   1. Overview of Data Science and its Importance
   2. Setting up Python Environment (Anaconda, Jupyter Notebooks)
   3. Basic Python Syntax and Concepts
      1. Variables, Data Types, and Operators
      2. Control Flow (If statements, Loops)
      3. Functions and Modules
      4. Exception Handling
      5. Working with Files
2. Data Manipulation with Pandas
   1. Introduction to Pandas
   2. Series and DataFrames
   3. Data Indexing and Selection
   4. Data Cleaning (Handling Missing Data, Data Formatting)
   5. File Operations (Reading and Writing CSV, Excel files)
   6. Grouping and Aggregating Data
   7. Merging, Joining, and Concatenating DataFrames
3. Data Analysis and Visualization
   1. Statistical Analysis Basics
   2. Introduction to NumPy
      1. NumPy Arrays
      2. Array Operations and Broadcasting
      3. Advanced Array Manipulations (Indexing, Slicing, Iterating)
   3. Data Visualization with Matplotlib
      1. Plotting Basics (Line plots, Bar charts, Histograms)
      2. Customizing Plots (Labels, Legends, Colors)
   4. Advanced Data Visualization with Seaborn
      1. Statistical Plots (Box plots, Violin plots, Pair plots)
4. Recommendation engine
   1. Collaborative filtering
      1. User based filtering
      2. Item based filtering
   2. Data collection and cleaning
   3. Creating a movie recommendation system similar to Netflix
      1. Sourcing data
      2. Munging and wrangling data
      3. Pivots and correlations
      4. Hyperparameters
      5. Visualization and presentation
5. Reinforcement Learning
   1. Introduction to Reinforcement Learning
      1. Definitions and Key Concepts
      2. Differences from other types of machine learning
   2. Environment and Agents
      1. Understanding the environment
      2. Role of agents
   3. Markov Decision Processes (MDPs)
      1. States, Actions, and Rewards
      2. Policy and Value Functions
   4. Types of RL Algorithms
      1. Model-based vs. Model-free
      2. On-policy vs. Off-policy
   5. Key Algorithms
      1. Q-Learning
      2. SARSA (State-Action-Reward-State-Action)
      3. Deep Q-Networks (DQN)
   6. Policy Gradient Methods
      1. Introduction to Policy Optimization
      2. REINFORCE algorithm
      3. Actor-Critic Methods
6. Introduction to Machine Learning
   1. Machine Learning Concepts and Terminology
   2. Types of Machine Learning Algorithms (Supervised, Unsupervised, Reinforcement Learning)
   3. Data Preprocessing for Machine Learning
      1. Feature Engineering
      2. Handling Categorical Data
      3. Scaling and Normalization
   4. Splitting Data into Training and Testing Sets
7. Machine Learning with Scikit-learn
   1. Overview of Scikit-learn
   2. Implementing Regression Models
   3. Implementing Classification Models
   4. Clustering Techniques
   5. Dimensionality Reduction
   6. Model Evaluation and Validation
      1. Cross-Validation
      2. Performance Metrics (Accuracy, Precision, Recall)
   7. Tuning Machine Learning Models (Grid Search, Random Search)
8. Advanced Topics and Project Work
   1. Introduction to Deep Learning and TensorFlow/Keras (Overview)
   2. Time Series Analysis
   3. Natural Language Processing (NLP) Basics
   4. Project Work: Applying the learned concepts on real-world datasets to solve problems.

This course structure provides a thorough pathway from the very basics of Python programming to the advanced application of machine learning algorithms in data science, ensuring a solid foundation for any aspiring data scientist. PLease note that the instructor uses an Agile methodology for course coverage and as such, the exact sequence may not be followed.