Online Classes > DataQuest

Datacamp | DataQuest | Metis

Candidates can choose between either two (2) R or two (2) Python classes.
Classes can be accessed at the Dataquest Website. You are responsible for the cost of classes at Dataquest.
Note: When you complete the classes, upload the Dataquest certificates  to

DataQuest: Python track
1.    Python for Data Science: Fundamentals  
In our introductory course on Python for data science, you’ll get an overview of the Python programming language and how you can use it for data science. You will learn to code using real-world mobile app data while learning key Python concepts such as lists and for loops. You’ll also learn how to update variables, how to work with different kinds of data, how to manipulate Python dictionaries, and how to use custom functions to speed up your workflow. Additionally, we’ll cover some coding best practices that’ll help you build good habits right from the start, and show you how to use Jupyter Notebook, a popular tool used in the Data Science workflows for easy sharing of data science projects. At the end of the course, you will combine all the skills you have learned to create your own data science portfolio project. In this guided project, you’ll analyze different app profiles on the iOS App Store in order to make recommendations for the most profitable types of apps to develop.

By the end of this course, you'll be able to:

  • Understand the fundamentals of programming in Python.
  • Understand the fundamentals of data science.
  • Use Jupyter Notebook.
  • Build a portfolio project.

2.    Python for Data Science: Intermediate  
In our Python for Data Science Intermediate course, we’ll cover some key techniques for working with the Python programming language for data science. To start off, you’ll learn how to clean and prepare data in Python, a critical skill for any data analyst or data scientist job. To do this, you’ll dig into some real-world data about artwork at the Museum of Modern Art and learn to manipulate text, clean messy data, and more. You’ll also get to practice summarizing numeric data and formatting strings in Python. Next, you will unlock the true power of Python as we dive into object-oriented programming (OOP) and how it relates to data science. Then, you’ll apply this new understanding by building your own class.  Finally, you’ll learn how clean, standardize, and analyze date and time data using Python’s datetime module. At the end of the course, you will combine all the skills you learned to create a portfolio project centered around Hacker News post titles to find out what types of posts are most likely to be successful at what times.

By the end of this course, you'll be able to:

  • Clean and analyze text data.
  • Understand object-oriented programming in Python.
  • Work with dates and times. 

DataQuest: R track
1.    Introduction to Programming in R  
In the world of data science, R is a popular programming language for a reason. It was built with statistical manipulation in mind, and there’s an incredible ecosystem of packages for R that let you do amazing things – particularly in data visualization – that  would be much more difficult in Python.  In this free introductory course on R, you’ll go hands-on with R for data science, learning critical R concepts such as matrices, vectors, lists, and more, and writing your own code to practice them right in your browser window. And you’ll learn all of this while working with real-world data, much as you would for a real data science project.  You will also learn how to update variables, work with different kinds of data, and how to import data into R and save it as a dataframe. We’ll also cover how to how to install packages to extend R's functionality for working with dataframes, a crucial skill for extending your data science toolkit. And you’ll learn the basics of using R Studio, which is a popular free and open-source development environment that’s widely used in the R data science community, so that you can easily share projects.

By the end of this course, you'll be able to:

  • Understand The basis of syntax in the R programming language.
  • Use comparison operators to make calculations.
  • Work with basic data structures in R.

Intermediate R Programming  
In our Intermediate Programming in R course, you will continue building your R data science skill set. We’ll take you beyond the basics to enhance your understanding of R, supercharge your workflow, do some pretty neat stuff along the way.  To start off, you will learn how to use control structures in your R programming to control the flow of your code. Then, you will learn to work with vectorized functions to make the most of R's functionality. You will also learn how to use functions in your code to speed up your workflow and write better code to avoid common pitfalls.  Next, you will learn about how to work with functionals and understand why they're suitable alternatives to loops, and you’ll get hands-on practice with single and multivariable functions. Towards the end of the course, you will learn the basics of working with strings and string manipulation as you analyze with real-world data from the World Cup.  By the time you get to the end of this course, you’ll be quite comfortable with programming in R, and you’ll have built the fundamental skills you need to dive into a variety of unique data science projects of your own!

By the end of this course, you will be able to

  • Use control structures.
  • Use fuctionals in place of for-loops.
  • Manipulate strings and dates.
  • Use built-in and custom functions.

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