Python for DataScience

This course focuses on Python specifically for data science, and will introduce data manipulation and cleaning techniques using the popular python pandas data science library, will continue with NumPy adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Audience

• Software engineers
• Business analysts
• STEM background

Goals

In this course you will learn:

• Understand techniques such as lambdas, NumPy, sklearn and manipulating files
• Describe common Python functionality and features used for data science
• Query DataFrame structures for cleaning and processing
• Understand the basics of visualization using Mathplotlib/SeaBorn by creating charts and graphs to better visualize your data.
• Use iPython Notebook to create Python code.

Outline

Module 01: DataScience: Introduction to pandas 1

• DataFrames
• Insert
• Delete
• Select

Module 02: DataScience: Introduction to pandas 2

• Merging
• Conditionals

Module 03: DataScience: Introduction to NumPy

• Vectors
• Matrix operations
• Sorting
• Indexing
• Broadcast

Module 04: DataScience: Introduction to sklearn 1

• Preprocess
• Model Select
• Pipeline

Module 05: DataScience: Introduction to sklearn 2

• Feature Selection
• Metrics
• One Hot Encoding

Module 06: Matplotlib visualization / SeaBorn

• 2D plotting
• Histograms
• HeatMap

Module 07: IPython Notebook

Prerequisite

• There is no prerequisite for this course.

Technical requirements

To attend this course, you need to have:

• PC/Laptop with internet access
• Updated web browser