Python for Data Science

By kelvin mwelwa Uncategorized
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About Course

1. Introduction to Python for Data Science
Why Python for Data Science?
Python is one of the most popular languages in Data Science for several reasons:- Easy to learn and read, especially for beginners.- Large ecosystem of libraries (e.g., NumPy, Pandas, scikit-learn, TensorFlow, Matplotlib).- Vast community support with thousands of tutorials, Q&A forums, and tools.- Versatile: useful in automation, web development, data analysis, machine learning, and more.- Seamlessly integrates with databases, APIs, and visualization tools.
Installing Python (Anaconda, Jupyter Notebook, VS Code)
1. Anaconda Distribution (Recommended):
– Comes with Python, Jupyter Notebook, and essential data science libraries.
– Download from https://www.anaconda.com/products/distribution
– Install and launch ‘Anaconda Navigator’ or ‘Jupyter Notebook’.
2. Jupyter Notebook:
– Interactive coding environment, especially great for data analysis and visualization.
– Can also be installed using `pip install notebook` (if Python is already installed).
3. VS Code (Visual Studio Code):
– Lightweight code editor by Microsoft.
– Supports Python with extensions. Download from https://code.visualstudio.com
– Add Python Extension in VS Code marketplace.
– You can run `.py` scripts and even open Jupyter Notebooks directly.
Python IDEs and Environments
IDEs (Integrated Development Environments) help you write and manage code efficiently.
Popular Python IDEs for Data Science:- Jupyter Notebook: Best for interactive data exploration.- VS Code: Great general-purpose code editor with Python support.- PyCharm: Powerful IDE, especially for large projects.
Environments:- **Virtual Environments** allow isolated Python setups for different projects.
– Create one using: `python -m venv myenv`
– Activate on Windows: `myenv\Scripts\activate`
– Activate on macOS/Linux: `source myenv/bin/activate`- With Anaconda, you can use: `conda create -n myenv python=3.10`
Writing and Running Python Scripts
Ways to write Python code:
1. Jupyter Notebook:
– Use cells to write and execute code interactively.
– Useful for data visualization and step-by-step coding.
2. py Files (Python Scripts):
– Use any text editor or IDE to write Python code in a `.py` file.
– Run the script in terminal: `python scriptname.py`
Example script (`hello.py`):
“`python
print(“Hello, Data Science!”)
“`
3. Integrated Terminal in IDEs:
– VS Code or PyCharm lets you run scripts within the IDE itself.
Tips:- Save your work frequently.- Use comments (`#`) to document your code.- Practice writing small functions and exploring data sets.

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What Will You Learn?

  • Python for Data Science - Full Course Outline
  • 1. Introduction to Python
  • - Why Python for Data Science?
  • - Installing Python (Anaconda, Jupyter Notebook, VS Code)
  • - Python IDEs and environments
  • - Writing and running Python scripts
  • 2. Python Basics
  • - Variables and Data Types
  • - Operators (Arithmetic, Logical, Comparison)
  • - Strings and String Operations
  • - Type Casting
  • - Input and Output
  • 3. Control Structures
  • - Conditional Statements (if, else, elif)
  • - Loops: for, while
  • - Loop control: break, continue, pass
  • 4. Data Structures in Python
  • - Lists and List Comprehension
  • - Tuples
  • - Dictionaries
  • - Sets
  • - Iterating over data structures
  • 5. Functions and Modules
  • - Defining Functions
  • - Function Arguments and Return Values
  • - Lambda Functions
  • - Built-in Functions
  • - Modules and Packages
  • - import, from, as
  • 6. Working with Libraries
  • - Introduction to NumPy
  • - - Arrays, Array Operations
  • - - Indexing and Slicing
  • - - Broadcasting
  • - Introduction to Pandas
  • - - Series and DataFrames
  • - - Reading and Writing Data (CSV, Excel, JSON)
  • - - Data Cleaning and Transformation
  • - - Handling Missing Data
  • 7. Data Visualization
  • - Matplotlib
  • - - Line, Bar, Histogram, Scatter plots
  • - - Customizing plots
  • - Seaborn
  • - - Advanced plots (heatmaps, pairplots, violin plots)
  • - - Styling and themes
  • 8. Data Analysis and Preprocessing
  • - Data Exploration and Summary Statistics
  • - GroupBy and Aggregation
  • - Sorting and Filtering
  • - Merging and Joining Datasets
  • - Feature Engineering
  • - Handling Missing/Outlier Values
  • - Encoding Categorical Data
  • - Scaling and Normalization
  • 9. Introduction to Statistics for Data Science
  • - Descriptive Statistics
  • - Probability Basics
  • - Distributions (Normal, Binomial, etc.)
  • - Hypothesis Testing
  • - Correlation and Covariance
  • 10. Introduction to Machine Learning with scikit-learn
  • - ML Workflow Overview
  • - Supervised vs Unsupervised Learning
  • - Train-Test Split
  • - Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
  • - Regression (Linear Regression)
  • - Classification (Logistic Regression, KNN, Decision Trees)
  • - Clustering (K-Means)
  • - Cross Validation
  • 11. Mini Projects / Case Studies
  • - Exploratory Data Analysis (EDA) Project
  • - Classification Project (e.g., Iris, Titanic)
  • - Regression Project (e.g., House Prices)
  • - Clustering Project (e.g., Customer Segmentation)
  • 12. Capstone Project
  • - Real-world dataset
  • - Complete data science workflow:
  • - - Data Collection
  • - - Cleaning
  • - - Visualization
  • - - Modeling
  • - - Evaluation
  • - - Deployment (optional)
  • Optional Add-ons
  • - Working with APIs and Web Scraping (using requests, BeautifulSoup)
  • - Introduction to SQL with Python
  • - Introduction to Time Series Analysis
  • - Basics of Deep Learning (using TensorFlow/Keras)
  • - Data Science Interview Prep

Course Content

1. Introduction to Python
Why Python for Data Science? Python is one of the most popular languages in Data Science for several reasons:- Easy to learn and read, especially for beginners.- Large ecosystem of libraries (e.g., NumPy, Pandas, scikit-learn, TensorFlow, Matplotlib).- Vast community support with thousands of tutorials, Q&A forums, and tools.- Versatile: useful in automation, web development, data analysis, machine learning, and more.- Seamlessly integrates with databases, APIs, and visualization tools.

  • Introduction to Python for Data Science
    01:00
  • Introduction to python for data science

2. Python Basics
Variables and Data Types

3. Control Structures
- Conditional Statements (if, else, elif) - Loops: for, while - Loop control: break, continue, pass

4. Data Structures in Python
- Lists and List Comprehension - Tuples - Dictionaries - Sets - Iterating over data structures

5. Functions and Modules
- Defining Functions - Function Arguments and Return Values - Lambda Functions - Built-in Functions - Modules and Packages - import, from, as

6. Working with Libraries
- Introduction to NumPy - - Arrays, Array Operations - - Indexing and Slicing - - Broadcasting - Introduction to Pandas - - Series and DataFrames - - Reading and Writing Data (CSV, Excel, JSON) - - Data Cleaning and Transformation - - Handling Missing Data

7. Data Visualization
- Matplotlib - - Line, Bar, Histogram, Scatter plots - - Customizing plots - Seaborn - - Advanced plots (heatmaps, pairplots, violin plots) - - Styling and themes

8. Data Analysis and Preprocessing
- Data Exploration and Summary Statistics - GroupBy and Aggregation - Sorting and Filtering - Merging and Joining Datasets - Feature Engineering - Handling Missing/Outlier Values - Encoding Categorical Data - Scaling and Normalization

9. Introduction to Statistics for Data Science
- Descriptive Statistics - Probability Basics - Distributions (Normal, Binomial, etc.) - Hypothesis Testing - Correlation and Covariance

10. Introduction to Machine Learning with scikit-learn
- ML Workflow Overview - Supervised vs Unsupervised Learning - Train-Test Split - Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score) - Regression (Linear Regression) - Classification (Logistic Regression, KNN, Decision Trees) - Clustering (K-Means) - Cross Validation

11. Mini Projects / Case Studies
- Exploratory Data Analysis (EDA) Project - Classification Project (e.g., Iris, Titanic) - Regression Project (e.g., House Prices) - Clustering Project (e.g., Customer Segmentation)

12. Capstone Project
- Real-world dataset - Complete data science workflow: - - Data Collection - - Cleaning - - Visualization - - Modeling - - Evaluation - - Deployment (optional)

Optional Add-ons
- Working with APIs and Web Scraping (using requests, BeautifulSoup) - Introduction to SQL with Python - Introduction to Time Series Analysis - Basics of Deep Learning (using TensorFlow/Keras) - Data Science Interview Prep

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