Python for Data Science

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.
• Analyse 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.

• 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.

Module 1: Introduction to Python for Data Science

• Overview of Data Science and its Importance
• Setting up Python Environment (Anaconda, Jupyter Notebooks)
• Basic Python Syntax and Concepts
  • Variables, Data Types, and Operators
  • Control Flow (If statements, Loops)
  • Functions and Modules
  • Exception Handling
  • Working with Files

Module 2: Data Manipulation with Pandas

• Introduction to Pandas
• Series and DataFrames
• Data Indexing and Selection
• Data Cleaning (Handling Missing Data, Data Formatting)
• File Operations (Reading and Writing CSV, Excel files)
• Grouping and Aggregating Data
• Merging, Joining, and Concatenating DataFrames

Module 3: Data Analysis and Visualization

• Statistical Analysis Basics
• Introduction to NumPy
  • NumPy Arrays
  • Array Operations and Broadcasting
  • Advanced Array Manipulations (Indexing, Slicing, Iterating)
• Data Visualization with Matplotlib
  • Plotting Basics (Line plots, Bar charts, Histograms)
  • Customizing Plots (Labels, Legends, Colors)
• Advanced Data Visualization with Seaborn
  • Statistical Plots (Box plots, Violin plots, Pair plots)

Module 4: Recommendation engine

• Collaborative filtering
  • User based filtering
  • Item based filtering
• Data collection and cleaning
• Creating a movie recommendation system similar to Netflix
  • Sourcing data
  • Munging and wrangling data
  • Pivots and correlations
  • Hyperparameters
  • Visualization and presentation

Module 5: Introduction to Machine Learning

• Machine Learning Concepts and Terminology
• Types of Machine Learning Algorithms (Supervised, Unsupervised, Reinforcement Learning)
• Data Preprocessing for Machine Learning
  • Feature Engineering
  • Handling Categorical Data
  • Scaling and Normalization
• Splitting Data into Training and Testing Sets

Module 6: Machine Learning with Scikit-learn

• Overview of Scikit-learn
• Implementing Regression Models
• Implementing Classification Models
• Clustering Techniques
• Dimensionality Reduction
• Model Evaluation and Validation
  • Cross-Validation
  • Performance Metrics (Accuracy, Precision, Recall)
• Tuning Machine Learning Models (Grid Search, Random Search)

Module 7: Advanced Topics and Project Work

• Introduction to Deep Learning and TensorFlow/Keras (Overview)
• Time Series Analysis
• Natural Language Processing (NLP) Basics
  • Project Work: Applying the learned concepts on real-world datasets to solve problems.