Q2-2021 EXAM: EARLY REGISTRATION OPENS ON DECEMBER 7, 2020

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 certificates@fdpinstitute.org

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.

https://www.dataquest.io/course/python-for-data-science-fundamentals/


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.
https://www.dataquest.io/course/python-for-data-science-intermediate 


DataQuest: R track
1.    Introduction to Data Analysis 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.
This is the first course in the DataQuest Data Analyst in R path, and in it, you'll be learning about the fundamentals of R. You'll learn to use variables, operators, write logical expressions and data analysis workflow in R.
As you learn these new R programming skills, you'll be writing your 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.
We’ll also cover 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:

  • Use the R programming language to import and manipulate data.
  • Use logical expressions and understand the data analysis workflow.
  • Set up your own development environment for projects.
https://www.dataquest.io/course/introduction-to-data-analysis-in-r/ 

2.    Data Structures in R   
This is the second course in the Data Analyst in R path. In it, you'll build on the R programming skills you learned in the first course as you start to work with some of the most common data structures in R: vectors, matrices, lists, and dataframes. 
As you learn these new R programming skills, you'll be writing 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.
At the end of the course, you'll be ready to dig into your first real data science project: an analysis of COVID-19 virus trends. Our guided projects will challenge you to synthesize and apply everything you've learned while still providing enough direction that you know where to go!

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

  • Work with vectors, matrices, lists, and Dataframes in R.
  • Understand why these data structures are used.
  • Build your first data science project using R.
https://www.dataquest.io/course/data-structures-in-r/


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