Learn Data Science, Statistics, and Machine Learning using Python Training

Course #:TP2604

Learn Data Science, Statistics, and Machine Learning using Python Training

Designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science, this course’s content can be adjusted based on student experience level with Python to include full overview of Python and programming if necessary. The course can be adjusted to be between 3-5 days, depending on desired student outcomes and student experience.

Objectives

  • Learn how to program with Python
  • How to create amazing data visualizations
  • How to use Machine Learning with Python

Topics

  • Programming with Python
  • NumPy with Python
  • Use matplotlib and Seaborn for data visualizations
  • Web scraping with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Connect Python to SQL
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn
  • and much more!

Each section of the course consists of several lectures and ends with a full project exercise.
Each Machine Learning Topic has a full walkthrough project and a full exercise to test comprehension.

Skills Gained

By the end of this course training students will be able to:

  • Comfortably program with Python
  • Use Python and pandas to read data from a variety of sources (SQL, Excel, CSV, HDFS, etc)
  • Use multiple libraries to create data visualizations
  • Use Python's SciKit Learn library to implement Machine Learning Models
  • Understand how to use Spark to deal with big data and distributed systems

Prerequisites

Students should have some programming experience in a programming language.

Duration

4 Days

Outline of Learn Data Science, Statistics, and Machine Learning using Python Training

1. Python Basics Overview

 

2. Python for Statistics

 

3. Python for Data Analysis - NumPy

 

4. Python for Data Analysis - pandas

 

5. Python for Data Analysis - pandas Exercises

 

6. Python for Data Visualization - matplotlib

 

7. Python for Data Visualization - Seaborn

 

8. Python for Data Visualization - pandas Built-in Visualization

 

9. Python for Data Visualization - Plotly and Cufflinks

 

10. Python for Data Visualization - Geographical Plotting

 

11. Data Capstone Projects

 

12. Introduction to Machine Learning

 

13. Linear Regression

 

14. Cross Validation and Bias Variance Trade-Off

 

15. Logistic Regression

 

16. K Nearest Neighbors

 

17. Decision Trees and Random Forests

 

18. Support Vector Machines

 

19. Recommender Systems

 

20. K Means Clustering

 

21. Principal Component Analysis

 

22. Natural Language Processing

 

23. Hadoop and MapReduce Overview

 

24. Spark Overview

 

25. PySpark Overview

 

26. PySpark Exercises

 

27. Introduction to MLlib with Spark

We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.