Big Data Series - Learn Data Science, Statistics, and Machine Learning using Python New course for 2017

Course#: BSPT1100 - Next Public Course 13-16 March

About this Course

Course Type Course Code Duration
Python BSPT1100 4 Days

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 when delivered as a group in house program.

Why Attend this Course?

This course contains numerous hands on exercises to build your Advanced skillset.

What Makes this Course Stand Apart?

New content developed for 2017 delivery.

What you will Learn?

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

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

Audience

Students wanting to use Python for Data Science and Statistical Analysis

Prerequisites

This course is designed for beginners and those with some programming experience.

Course Outline

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 Project

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

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Call to speak to your account manager or a consultant on

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We are all technical with a wealth of Learning & Development experience so can talk you through any specific requirements or the details of one of our courses.

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