Candidates can choose between either four (4) R or four (4) Python classes. Candidates who are familiar with either Python or R can choose any four (4) courses on Statistics, Machine Learning or Data Analysis. 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 four Dataquest certificates using this form.
DataQuest: Python track
1. Introduction to Python
Python is one of the most widely used programming languages, and knowing how to use it is a highly sought-after skill if you want a career as a data professional. In this course, you will learn the fundamentals of programming with Python - no previous coding experience is necessary. By the end of the course, you will be able to write basic Python programs.
2. For Loops and Conditional Statements in Python
This course continues with the fundamentals of Python for data science. It is divided into four parts to make mastering the fundamentals quicker and easier. This course focuses on how to repeat a process using a for loop, how to use conditional statements, including if, else, and elif statements, and how to create a portfolio project in Jupyter Notebook.
3. Dictionaries, Frequency Tables, and Functions in Python
This course is part of the fundamentals of Python for data science. It has four parts. It focuses on developing additional data science fundamentals in Python, such as dictionaries and functions, discovering how to build frequency tables, and performing real-world data analysis tasks in an interactive coding environment.
4. Python Functions and Jupyter Notebook
This course is also part of the fundamentals of Python for data science. It has four parts. This course focuses on developing additional data science fundamentals in Python, such as writing functions with multiple inputs, discovering how to use and install Jupyter Notebook, and performing real-world data analysis tasks in a guided project.
DataQuest: R track
1. Introduction to Data Analysis in R
This course will teach the basics of how R works, and it will give an introduction to how R contributes to the data analysis workflow. No prerequisite is required for this course. This course focuses on a) fundamental knowledge of R, including syntax, data types, values, and vectors, b) Tidyverse libraries, data exploration techniques, and the data analysis process, and c) basic data analysis, manipulation, and visualization in R.
2. Data Structures in R
This course builds on the Introduction to Data Analysis in R course and expands the fundamental programming concepts and technique. It introduces the most common data structures encountered in the data analysis workflow. This course focuses on a) reinforcing fundamental coding knowledge and introducing basic data structures in R, b) building proficiency at working with vectors, matrices, lists, and DataFrames, and c) applying newly acquired data science skills in a guided data project.
3. Control Flow, Iteration and Functions in R
This course builds on the two courses listed above. It introduces the control flow (including if-else statements), conditionals, and how to write for loops and while loops. It covers functions and functional programming as well as when to choose iteration over vectorization. At the end of the course, there is a two-part project that will teach how to build an efficient, reproducible data analysis workflow using R and R Studio. This course only covers the first part of the project.
4. Specialized Data Processing in R: Strings and Dates
This course continues to build on the material covered in the three R courses listed above. This course introduces string indexing and concatenation to interpret, process, and analyze text data. Next, you’ll leverage the Lubridate package to overcome the unique difficulties of working with dates and times in R. The second part of the two-part project on how to build an efficient, reproducible data analysis workflow using R and R Studio is covered in this course.