Introduction to Python for Data Science (10 credits)


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This course is designed to introduce the students to the basics of the Python programming environment, including fundamental Python programming techniques used in data science. The course aims to teach students basic data visualization, manipulation, and exploration techniques using the popular Python data science libraries. This course provides a unique opportunity for the student to get hands-on experience with popular Python libraries such as NumPy, Pandas, and Matplotlib. By the end of this course, the student will understand the data science workflow and Python programming basics and learn how to take tabular data, clean it, manipulate it, visualize it, and run basic analyses.

Learning outcomes

  • Get familiar with the basics of data science, its workflow, and its challenges
  • Get familiar with the basic concepts of Python
  • Get familiar with the history of Python and why it is important for data science
  • Get familiar with essential Python libraries for data science
  • Learn how to set up Jupyter Notebook environment and get started using Jupyter Notebooks
  • Get familiar with the basic syntax and rules of writing codes in the Python programming language
  • Learn how to work with different data structures of Python
  • Learn about the concept of functional programming in Python
  • Get familiar with NumPy arrays and why it is important for vector and matrix operations
  • Learn how to use different NumPy functions to operate on arrays
  • Ger familiar with the Pandas library, DataFrame, and Series
  • Learn how to work with tabular data and manipulate them
  • Learn how to use the Matplotlib library to produce basic plots of data and results
  • Learn how to plot charts with custom configs and annotations

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: Introduction

  • 1.4. What is Data Science?
  • 1.5. Who is a Data Scientist?
  • 1.6. Demand for Data Scientists
  • 1.7. Data Science Workflow
  • 1.8. Data Science Challenges
  • 1.9. Programming in Data Science
  • 1.10. Python for Data Science

Chapter 3: Getting Started with Python

  • 3.1. Jupyter Notebook
  • 3.2. Anaconda
  • 3.3. Anaconda Installation
  • 3.4. Getting Started with Jupyter Notebook
  • 3.5. Python Syntax
  • 3.6. Input and Output
  • 3.7. Variables
  • 3.8. Data Types
  • 3.9. Python Operators
  • 3.10. Arithmetic Operations
  • 3.11. Comparison Operations
  • 3.12. Logical Operations
  • 3.13. String Operations

Chapter 4: Python Data Structures

  • 4.1. Introduction
  • 4.2. List
  • 4.3. List Indexing
  • 4.4. List Slicing
  • 4.5. List Manipulation: Add New Elements
  • 4.6. List Manipulation: Change and Remove Elements
  • 4.7. Tuple
  • 4.8. Accessing Tuple Elements
  • 4.9. Working with Tuples
  • 4.10. Set
  • 4.11. Set Manipulation
  • 4.12. Dictionary
  • 4.13. Accessing Dictionary Elements
  • 4.14. Dictionary Manipulation

Chapter 5: Python Programming Fundamentals

  • 5.1. Conditions: Introduction
  • 5.2. Conditions: if
  • 5.3. Conditions: else
  • 5.4. Conditions: elif
  • 5.5. Loops: Introduction
  • 5.6. Loops: for
  • 5.7. Loops: for in data structures
  • 5.8. Loops: while
  • 5.9. Loops: break, continue
  • 5.10. Functions: Introduction
  • 5.11. Functions: user-defined functions I
  • 5.12. Functions: user-defined functions II
  • 5.13. Comprehensions

Chapter 6: Introduction to NumPy

  • 6.1. Introduction to NumPy
  • 6.2. Array
  • 6.3. Arrays Primary Functions
  • 6.4. Intrinsic NumPy Array Creation
  • 6.5. Creating Random Arrays
  • 6.6. Standard Mathematics Operations
  • 6.7. Broadcasting in NumPy
  • 6.8. Vector and Matrix Mathematics
  • 6.9. Statistics in NumPy
  • 6.10. Common Mathematical Functions
  • 6.11. Comparison and Filtering in NumPy Arrays
  • 6.12. View and Copy

Chapter 7: Data Manipulation with Pandas

  • 7.1. Introduction to Pandas
  • 7.2. Pandas Series
  • 7.3. Pandas DataFrames
  • 7.4. DataFrames: Access Elements
  • 7.5. DataFrames: Insert and Delete
  • 7.6. DataFrames: Concatenate and Merge
  • 7.7. Input and Output: Part 1
  • 7.8. Input and Output: Part 2
  • 7.9. Data Intuition: Summary
  • 7.10. Data Intuition: Statistics
  • 7.11. Data Intuition: Filtering
  • 7.12. Handle Missing Values

Chapter 8: Data Visualization with Matplotlib

  • 8.1. Introduction to Matplotlib
  • 8.2. Plot
  • 8.3. Bar Plot
  • 8.4. Histogram
  • 8.5. Pie Chart
  • 8.6. Scatter Plot
  • 8.7. Plot Attribute
  • 8.8. Subplots

Chapter 9: Final Tasks

  • 9.1. Final Project
  • 9.2. Self-study Essay
  • 9.3. Congrats! You did it!

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