Python Data Science Quick Start
Published by Pearson
A Hands-On Intro to NumPy, Pandas, and Matplotlib
- Explore the most popular Python libraries with real-world demos on data manipulation, visualization, working with time series data, and more
- Learn how to publish and consume data in the web tier
- Put your knowledge into practice with interactive exercises
- Get access to a full set of labs to hone your skills after the class
Python has emerged as a popular and effective language in the world of data science. The dynamic nature of the language, the relative simplicity of the syntax, and the abundance of fast and powerful libraries have all been important contributory factors in the growth of Python data science libraries.
This course takes a detailed look at the most popular Python libraries for numeric processing, statistical analysis, and visualization. You will also learn how to integrate data science with web applications via REST APIs and web page data wrangling.
What you’ll learn and how you can apply it
- Use NumPy and Pandas for efficient data manipulation
- Use Matplotlib for visualization
- Work with time series data
- Consume and publish data on the web tier
This live event is for you because...
- You are a Python developer and you want to extend your skills into data science
- You work with large volumes of data and you want to use Python to automate manual processes and gain insights into the data
Prerequisites
- At least 3 months experience in Python programming
- Familiarity with basic statistical concepts such as standard deviation and percentiles would be beneficial
Course Set-up
- Install Python from https://www.python.org/downloads/
- Install any IDE, e.g., Visual Studio Code, PyCharm, or Spyder
- Download course materials from https://github.com/andyolsen/python-data-science/
Recommended Preparation
- Attend: Introduction to Python Programming (live course by Arianne Dee)
Recommended Follow-up
- Watch: Machine Learning in Python for Everyone (Video Collection) by Mark Fenner
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: Getting Started with NumPy (35 minutes)
- Introduction to NumPy
- NumPy arrays
- Manipulating array elements
- Manipulating array shape
- Exercise
- Q&A
Break (5 minutes)
Segment 2: NumPy Techniques (40 minutes)
- NumPy Universal functions
- Aggregations
- Broadcasting
- Manipulating arrays using boolean logic
- Additional techniques
- Exercise
- Q&A
Break (5 minutes)
Segment 3: Getting Started with Pandas (40 minutes)
- Introduction to Pandas
- Creating a Series
- Using a Series
- Creating a DataFrame
- Using a DataFrame
- Exercise
- Q&A
Break (5 minutes)
Segment 4: Pandas Techniques (40 minutes)
- Universal Functions
- Merging and Joining Datasets
- A Closer Look at Joins
- Exercise
- Q&A
Break (5 minutes)
Segment 5: Working with Time Series Data (30 minutes)
- Introduction to Time Series Data
- Indexing and Plotting Time Series Data
- Exercise
- Q&A
Segment 6: Working with the Web Tier (30 minutes)
- Publishing data via REST APIs
- Consuming data via REST APIs
- Data wrangling from web sites
- Exercise
- Q&A
Course Wrap Up (5 minutes)
Your Instructor
Andy Olsen
Andy Olsen is a freelance consultant, instructor, and developer with more than 30 years of experience in IT. Andy began his professional career as a C/C++ developer and has also worked in Rust, Go, and other languages as the years passed. Andy is actively involved in a wide range of technologies including full-stack development, cloud native applications, data science, and more. Andy is passionate about technology education and runs training courses around the world across diverse market sectors.