Enrol yourself in the Using Data Science Tools in Python course and lab to gain hands-on expertise on using Python for data science. Python’s robust libraries have given data scientists the ability to load, analyse, shape, clean, and visualise data in easy use, yet powerful ways. The course and lab provide the skills you need to successfully use these key libraries to extract useful insights from data, and as a result, provide great value to the business.
Lessons 1: Introduction
- Course Description
- How To Use This Course
- Course-Specific Technical Requirements
Lessons 2: Setting Up a Python Data Science Environment
- Topic A: Select Python Data Science Tools
- Topic B: Install Python Using Anaconda
- Topic C: Set Up an Environment Using Jupyter Notebook
- Summary
Lessons 3: Managing and Analysing Data with NumPy
- Topic A: Create NumPy Arrays
- Topic B: Load and Save NumPy Data
- Topic C: Analyse Data in NumPy Arrays
- Summary
Lessons 4: Transforming Data with NumPy
- Topic A: Manipulate Data in NumPy Arrays
- Topic B: Modify Data in NumPy Arrays
- Summary
Lessons 5: Managing and Analysing Data with pandas
- Topic A: Create Series and DataFrames
- Topic B: Load and Save pandas Data
- Topic C: Analyse Data in DataFrames
- Topic D: Slice and Filter Data in DataFrames
- Summary
Lessons 6: Transforming and Visualizing Data with pandas
- Topic A: Manipulate Data in DataFrames
- Topic B: Modify Data in DataFrames
- Topic C: Plot DataFrame Data
- Summary
Lessons 7: Visualising Data with Matplotlib and Seaborn
- Topic A: Create and Save Simple Line Plots
- Topic B: Create Subplots
- Topic C: Create Common Types of Plots
- Topic D: Format Plots
- Topic E: Streamline Plotting with Seaborn
- Summary
Appendix A: Scraping Web Data Using Beautiful Soup
- Topic A: Scrape Web Pages
Hands-on LAB Activities
Setting Up a Python Data Science Environment
- Setting Up a Jupyter Notebook Environment
Managing and Analysing Data with NumPy
- Creating a NumPy Array
- Using the NumPy Array Attributes
- Loading and Saving NumPy Data
- Analysing Data in a NumPy Array
- Using Fancy Indexing
- Using the NumPy Statistical Summary Functions
Transforming Data with NumPy
- Manipulating Data in a NumPy Array
- Using the reshape Function
- Using the ravel and flip Functions
- Using the transpose and concatenate Functions
- Using the sort and argrsort Functions
- Using the insert and delete Functions
- Using the Arithmetic Functions and Operators
- Using the Comparison Functions and Operators
- Modifying Data in NumPy Arrays
Managing and Analysing Data with pandas
- Creating Series and DataFrames
- Using the Series and DataFrame Attributes
- Loading and Saving DataFrame Data
- Analysing Data in a DataFrame
- Slicing and Filtering Data in a DataFrame
Transforming and Visualising Data with pandas
- Manipulating Data in a DataFrame
- Modifying Data in a DataFrame
- Using the DataFrame Arithmetic Functions and Operators
- Creating a Scatter Plot
Visualising Data with Matplotlib and Seaborn
- Creating a Line Plot
- Creating Subplots
- Creating Box Plots
- Creating a 3-D Scatter Plot
- Creating a Histogram
- Formatting Plots
- Creating a JointGrid
- Creating a Linear Regression Plot
Standard: