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Data Science with Python: Data Analysis and Visualization

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Beginner

Data Science with Python:


Data Analysis and Visualization

This class is a comprehensive introduction to data science with Python programming language. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular data analytics and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.

* Tuition paid for part-time courses can be applied to the Data Science Bootcamps if admitted within 9 months.

Course Overview

This class is a comprehensive introduction to Python for Data Analysis and Visualization. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular Python data analysis and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.

Prerequisites

If you have good knowledge of basic data types (e.g. string, numeric), data structures (e.g. list, tuple, dictionary) and are familiar with concepts of list comprehension and for/while loop, you are good to go with the Python for Data Analysis and Visualization course. We will cover these basic Python programming topics in the course as well, but move at a relatively fast speed.

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course. Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

Syllabus

Unit 1: Introduction to Python

Python is a high-level programming language. You will learn the basic syntax and data structures in Python. We demonstrate and run codes within Ipython notebook, which is a great tool providing a robust and productive environment for interactive and exploratory computing.

  • Introduction to Ipython notebook
  • Basic objects in Python
  • Variables and self-defining functions
  • Control flow
  • Data structures

Unit 2: Explore Deeper with Python

Python is an object-oriented programming (OOP) language. Having some basic knowledge of OOP will help you understand how Python codes work. More often than not, you will have to deal with data that is dirty and unstructured. You will learn many ways to clean your data such as applying regular expressions.
Introduction to object-oriented programming

  • How to deal with files
  • Run Python scripts
  • Handling and processing strings

Unit 3: Scientific Computation Tool

There are two modules for scientific computation that make Python powerful for data analysis: Numpy and Scipy. Numpy is the fundamental package for scientific computing in Python. SciPy is an expanding collection of packages addressing scientific computing.

  • Numpy
  • Scipy

Unit 4: Data Visualization

Python can also generate graphics easily using “Matplotlib” and “Seaborn”. Matplotlib is the most popular Python library for producing plots and other 2D data visualizations. Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing statistical graphics.

  • Seaborn
  • Matplotlib

Unit 5: Data manipulation with Pandas

Pandas provides rich data structures and functions for working with structured data. The “DataFrame” object in Pandas is just like the “data.frame” object in R. Pandas makes data manipulation (filter, select, group, aggregate, etc.) as easy as in R.

  • Pandas
  • Final Project
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