Python for Data Science

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  • Course platform: EDX
  • Offered by: University of California
  • Level: Advanced
  • Price: FREE (Certificate paid)
  • Approx. 10 weeks (8-10 hrs/week)

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Free Python for Data Science (Cert. Paid)

Why Python for Data Science?

The programming requirements of data science demand a very versatile yet flexible language which is simple to write the code but can handle highly complex mathematical processing. Python is most suited for such requirements as it has already established itself both as a language for general computing as well as scientific computing. Moreover, it is being continuously upgraded in the form of a new addition to its plethora of libraries aimed at different programming requirements. Below we will discuss such features of python which makes it the preferred language for data science.

  • It is simple and easy to learn, we can achieve results in fewer lines of code.
  • Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion on the general flow of the program.
  • It supports cross-platform, thus the same code works with multiple environments.
  • It executes faster than other similar languages used for data analysis like R and MATLAB.
  • Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing a very large volume of data transformation.
  • Python has got a very large collection of libraries and packages, which serve as special-purpose analysis tools where we can directly use code from other languages (Java or C).

About this course

In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?

This free Python for data science course is a part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:

  • Python
  • Jupyter notebooks
  • Pandas
  • Numpy
  • Matplotlib
  • Git
  • Sci-kit Learn
  • NLTK (Natural Language Toolkit)
    and many other tools.

In this free python for data science course, you will learn the above tools all within the context of solving compelling data science problems.

After completing this free python course, you’ll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.

By learning these skills, you’ll also become a member of a worldwide community that seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.

What you’ll learn

  • The basic process of data science
  • Python and Jupyter notebooks
  • An applied understanding of how to manipulate and analyze uncurated datasets
  • Basic statistical analysis and machine learning methods
  • How to effectively visualize results

By the end of this free python course, you should be able to find a dataset, formulate a research question, use the tools and techniques of this course to explore the answer to that question, and share your findings.

Prerequisites

Even though this is a free Python course, as it is an advanced-level course, a learner is required to have knowledge/experience on the following:

  • Previous experience with any programming language (Java, C, Pascal, Fortran, C++, Python, PHP, etc.) is expected.
  • This includes a high school, or undergraduate equivalent, to an introduction to computer science course.
  • Learners should be comfortable with loops, if/else, and variables.

Syllabus

There are different modules in this free python course, broken down into 10 weeks.  The beginning of the course is heavily focused on learning the basic tools of data science, while the latter half of the course is a combination of working on large projects and introductions to advanced data analysis techniques.

Week 1

Introduction:

  • Welcome and overview of the course.
  • Introduction to the data science process and the value of learning data science.

Week 2

Background:

  • In this optional week, we provide a brief background in python or Unix to get you up and running.
  • If you are already familiar with python and/or Unix, feel free to skip this content.

Week 3

Jupyter and Numpy:

  • Jupyter notebooks are one of the most commonly used tools in data science as they allow you to combine your research notes with the code for the analysis.
  • After getting started in Jupyter, we’ll learn how to use NumPy for data analysis.
  • NumPy offers many useful functions for processing data as well as data structures which are time and space-efficient.

Week 4

Pandas:

  • Pandas, built on top of NumPy,  adds data frames that offer critical data analysis functionality and features.

Week 5

Visualization:

  • When working with large datasets, you often need to visualize your data to gain a better understanding of it.
  • Also, when you reach conclusions about the data, you’ll often wish to use visualizations to present your results.

Week 6

Mini Project:

  • With the tools of Jupyter notebooks, NumPy, pandas, and Visualization, you’re ready to do sophisticated analysis on your own.
  • You’ll pick a dataset we’ve worked with already and perform an analysis for this first project.

Week 7

Machine Learning:

  • To take your data analysis skills one step further, we’ll introduce you to the basics of machine learning and how to use sci-kit learn – a powerful library for machine learning.

Week 8

Working with Text and Databases:

  • You’ll find yourself often working with text data or data from databases.
  • This week will give you the skills to access that data.
  • For text data, we’ll also give you a preview of how to analyze text data using ideas from the field of Natural Language Processing and how to apply those ideas using the Natural Language Processing Toolkit (NLTK) library.

Week 9 and 10

Final Project:

  • These weeks let you showcase all your new skills in an end-to-end data analysis project.
  • You’ll pick the dataset, do the data munging, ask the research questions, visualize the data, draw conclusions, and present your results.

Note:

If you have already done this course, kindly post your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification: Python for Data Science

Course Platform EDX
Level Advanced
Class length <1 month
Program details Course
Enrollment Free Course (paid certificate)
Course Subjects Data Science, Data Science with 'Python', Machine learning, Natural language processing
Offered by University University of California, San Diego

Videos: Python for Data Science

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