Learning Objectives
5 objectives- 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
Preview1. 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)
- NLP Concepts: Tokenization, Stemming, Lemmatization
- Sentiment Analysis
- Libraries: NLTK and SpaCy
11.3 Introduction to Deep Learning with Python
- Fundamentals of Neural Networks
- TensorFlow and Keras Overview
- Building and Training Deep Learning Models
- Model Deployment Basics
11.4 Handling Big Data with PySpark
- Introduction to Big Data and Apache Spark
- PySpark DataFrames and Operations
- Distributed Processing Concepts
- Running Machine Learning Algorithms on Big Data
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