Python for Data Science and Machine Learning Bootcamp

Course Content

Module 1: Introduction to Python

• Lesson 1.1: Python Basics
  • Python syntax and basic programming concepts.
  • Variables, data types, and operators.
• Lesson 1.2: Control Structures and Functions
  • Conditional statements and loops.
  • Defining and invoking functions.
• Lesson 1.3: Working with Data Structures
  • Lists, tuples, dictionaries, and sets.
  • List comprehensions and lambda functions.

Module 2: Data Manipulation with Pandas

• Lesson 2.1: Introduction to Pandas
  • Series and DataFrame objects.
  • Importing and exporting data (CSV, Excel, SQL).
• Lesson 2.2: Data Cleaning and Preparation
  • Handling missing data.
  • Data transformation and normalization.
• Lesson 2.3: Data Aggregation and Grouping
  • GroupBy operations.
  • Pivot tables and cross-tabulations.

Module 3: Data Visualization

• Lesson 3.1: Introduction to Matplotlib
  • Basic plotting functions.
  • Customizing plots (labels, titles, legends).
• Lesson 3.2: Advanced Visualization with Seaborn
  • Statistical plots (histograms, KDE plots, pair plots).
  • Customizing Seaborn plots.

Module 4: Statistical Analysis

• Lesson 4.1: Descriptive Statistics
  • Measures of central tendency and dispersion.
  • Correlation and covariance.
• Lesson 4.2: Inferential Statistics
  • Probability distributions.
  • Hypothesis testing and p-values.
• Lesson 4.3: Regression Analysis
  • Simple and multiple linear regression.
  • Evaluating regression models.

Module 5: Introduction to Machine Learning

• Lesson 5.1: Machine Learning Basics
  • Overview of machine learning and its applications.
  • Supervised vs. unsupervised learning.
• Lesson 5.2: Data Preprocessing for Machine Learning
  • Feature scaling and encoding.
  • Train-test split and cross-validation.

Module 6: Supervised Learning Techniques

• Lesson 6.1: Classification Algorithms
  • Logistic regression, K-Nearest Neighbors, Decision Trees.
  • Evaluating classification models (confusion matrix, ROC curve).
• Lesson 6.2: Regression Algorithms
  • Linear regression, Ridge and Lasso regression.
  • Evaluating regression models (RMSE, R-squared).
• Lesson 6.3: Ensemble Methods
  • Random Forest, Gradient Boosting, AdaBoost.
  • Hyperparameter tuning with Grid Search and Random Search.

Module 7: Unsupervised Learning Techniques

• Lesson 7.1: Clustering Algorithms
  • K-Means clustering, Hierarchical clustering.
  • Evaluating clustering results.
• Lesson 7.2: Dimensionality Reduction
  • Principal Component Analysis (PCA).
  • t-Distributed Stochastic Neighbor Embedding (t-SNE).

Module 8: Advanced Topics and Techniques

• Lesson 8.1: Natural Language Processing (NLP)
  • Text preprocessing and feature extraction.
  • Sentiment analysis and topic modeling.
• Lesson 8.2: Deep Learning Introduction
  • Basics of neural networks.
  • Implementing neural networks with TensorFlow and Keras.

Module 9: Real-World Projects and Case Studies

• Lesson 9.1: Capstone Project
  • Comprehensive project covering the entire data science pipeline.
  • Applying learned skills to solve a real-world problem.
• Lesson 9.2: Case Studies
  • Analysis of successful data science and machine learning projects.
  • Learning from industry practices and methodologies.