The Open Data Science Bootcamp

  • Categories data science
  • Duration 20h
  • Total Enrolled 17
  • Last Update November 18, 2020

About Course

Our Python-based data science curriculum introduces key concepts and best practices in data cleaning, data analysis, machine learning, statistical analysis, natural language processing, deep learning, data visualization, and more. Working through this curriculum will effectively help you build the skills needed to become a successful data scientist who is ready for real-world data challenges.


We believe that education and knowledge should be open and free for everyone. This is a completely open and free online bootcamp for people who want to learn data science. We have gathered the best free learning resources and organized them into a systematic and easy-to-follow curriculum. Here you will learn everything about data science and grow from a beginner to a pro. All the content here is completely free, meaning you won’t have to pay any tuition to start learning. All you need to do is register for a free account and start learning!

The open data science bootcamp

What Will I Learn?

  • Learn the basics of data science
  • Learn how databases and SQL work
  • Learn how to program in Python
  • Learn the basics of various data science packages such as Pandas
  • Learn the basics of statistics
  • Learn how to gather, analyze and interpret data

Topics for this course

31 Lessons20h


The purpose of this section is to help students who are completely new to data science get warmed up and ready to go. We will cover various topics including how to set up your computer to do data science, using GitHub and Kaggle to share your work, using the command line in your OS, etc. Are you ready?

Introduction to SQL?

This is an introduction to the Structured Query Language (SQL), which is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS). It is particularly useful in handling structured data, i.e. data incorporating relations among entities and variables. SQL is a powerful tool for creating, updating, deleting, and requesting information from databases. It is an essential skill for any data scientist because relational databases are one of the most important data sources for any data science process.

Introduction to Python?

Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects. Python is absolutely the most important skill for any data scientist to master, since most of the popular data science modules and libraries are built on top of Python nowadays. In this module, you will get to learn the basics of Python programming and build a solid foundation for later modules where you will learn all the cool things about the Python data science packages.

Intermediate Python?

After you are familiar with the basic concepts in Python programming, it is time to take your skills to the next level! As a data scientist, mastering the intermediate level Python coding is extremely beneficial since it allows you to work on more complicated problems and better leverage the power of Python.

Introduction to Numpy?

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. NumPy provides Python with a powerful array processing library and an elegant syntax that is well suited to expressing computational algorithms clearly and efficiently. We'll introduce basic array syntax and array indexing, review some of the available mathematical functions in NumPy, and discuss how to write your own routines. Along the way, we'll learn just enough about Matplotlib to visualize results from our examples.

Learn the Basics of Machine Learning?

Machine learning, the field of computer science that gives computer systems the ability to learn from data, is one of the hottest topics in data science. Machine learning is transforming the world: from spam filtering in social networks to computer vision for self-driving cars, the potential applications of machine learning are vast. This section covers the foundational machine learning concepts and tools that will help you advance in your career. Whether you’re trying to analyze a dataset using machine learning, or you’re a data analyst trying to upgrade your skills, this is the best place to start.

Deep Learning for Beginners?

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

Natural Language Processing?

Natural Language Processing or NLP is a field of Artificial Intelligence that gives the machines the ability to read, understand and derive meaning from human languages. It is a discipline that focuses on the interaction between data science and human language, and is scaling to lots of industries. Today NLP is booming thanks to the huge improvements in the access to data and the increase in computational power, which are allowing practitioners to achieve meaningful results in areas like healthcare, media, finance and human resources, among others.

About the instructor

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1 Courses

17 students

The Open Data Science Bootcamp

Material Includes

  • Videos
  • Quizzes
  • Assignments
  • Projects


  • Basic understanding of programming
  • Basic math and statistics background
  • Desire to learn and grow

Target Audience

  • Students
  • Professionals
  • Anyone interested in data science