Understand and apply fundamental Python programming concepts including syntax, data types, control flow, functions, and modules.
Utilize Python data structures and libraries such as NumPy, Pandas, and Matplotlib for effective data manipulation, analysis, and visualization.
Develop skills in data cleaning, preprocessing, exploratory data analysis, and applying machine learning algorithms using Python libraries.
Gain foundational knowledge of advanced topics including web scraping, natural language processing, deep learning, and big data processing with PySpark.
Evaluate, validate, and deploy machine learning models, and understand ethical considerations in data collection.
Content Outline
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1. Introduction to Python Programming
Overview of Python Language
Syntax and Basic Constructs
Variables and Data Types
Operators and Expressions
Control Structures: Conditionals and Loops
Functions: Definition, Arguments, Return Values
Modules and Code Organization
2. Data Structures in Python
Lists: Creation, Access, Manipulation
Tuples: Characteristics and Use Cases
Dictionaries: Key-Value Pairs, Operations
Sets: Properties and Applications
3. Working with Python Libraries for Data Science
3.1 NumPy for Numerical Computing
Introduction to NumPy
Arrays: Creation, Indexing, and Slicing
Mathematical and Statistical Operations
Linear Algebra Functions
3.2 Pandas for Data Manipulation and Analysis
Pandas Data Structures: Series and DataFrames
Loading and Inspecting Data
Data Indexing, Selecting, and Filtering
Handling Missing Data and Duplicates
Data Aggregation and Grouping
Data Cleaning and Preprocessing Techniques
3.3 Data Visualization
Introduction to Matplotlib
Plotting: Line, Bar, Scatter, Histogram
Customizing Plots: Labels, Legends, Colors
Introduction to Seaborn for Statistical Plots
Creating Advanced Visualizations
4. Control Flow in Python
Conditional Statements (if, elif, else)
Looping Constructs (for, while)
Loop Control Statements (break, continue, pass)
5. Functions and Modules
Defining and Calling Functions
Parameters and Argument Passing
Return Statements
Lambda Functions
Creating and Importing Modules
6. File Handling in Python
Reading and Writing Text Files
Working with CSV and JSON Files
File Modes and Context Managers
Exception Handling in File Operations
7. Exploratory Data Analysis (EDA)
Importance of EDA
Summary Statistics
Identifying Patterns and Outliers
Visual Techniques for EDA
8. Data Cleaning and Preprocessing
Handling Missing Values
Detecting and Treating Outliers
Data Transformation and Scaling
Feature Engineering Basics
9. Introduction to Machine Learning with Python
9.1 Machine Learning Concepts
Overview of Supervised and Unsupervised Learning
Common Algorithms: Regression, Classification, Clustering
9.2 Scikit-Learn Library
Data Splitting: Train/Test
Model Training and Prediction
Model Evaluation Metrics
Cross-Validation and Hyperparameter Tuning
10. Model Evaluation and Validation
Accuracy, Precision, Recall, F1 Score
Confusion Matrix
Cross-Validation Techniques
Grid Search for Hyperparameter Optimization
11. Advanced Topics in Data Science
11.1 Web Scraping and Data Collection
Introduction to Web Scraping
Using Requests and BeautifulSoup
Parsing HTML and Extracting Data
Ethical Considerations and Legal Aspects
11.2 Introduction to Natural Language Processing (NLP)