What does each learning path cover?

In this article:


Overview

We offer 4 learning paths. Each path is designed to teach you the concepts and skills you need to excel in its respective job outcome: as a Data Analyst in R, Data Analyst in Python, Data Scientist in Python or Data Engineer. 

If you're unsure what these roles actually encompass, you can learn more about them here.

Dataquest learning paths


The Data Analyst in R Path (Beta)

This path teaches the essential skills you'll need to start pursuing an entry-level job as a Data Analyst. Data Analysts' deliver value to their companies by taking data, using it to answer questions, and communicating the results to help make business decisions. Common tasks done by data analysts include data cleaning, performing analysis and creating data visualizations.

Depending on the industry, the data analyst can go by a different title (e.g. Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst). Regardless of job title, the data analyst is a generalist who can fit into many roles and teams to help others make better data-driven decisions.

What material does this path cover?

  • Basic and intermediate programming concepts
  • Modern R workflows with RStudio and tidyverse packages
  • Probability and statistics for data analysis
  • How to clean and visualize data
  • Collaboration tools like git and SQL databases

This is an abridged version of what you'll learn on this path — to get a more comprehensive look at the Data Analyst Path curriculum, you can visit the path page here.

What plan do I need to subscribe to?

Since this path is currently in Beta, we're making it available for free to all of our students for a short time. 


The Data Analyst in Python Path 

This path teaches the essential skills you'll need to start pursuing an entry-level job as a Data Analyst. Data Analysts' deliver value to their companies by taking data, using it to answer questions, and communicating the results to help make business decisions. Common tasks done by data analysts include data cleaning, performing analysis and creating data visualizations.

Depending on the industry, the data analyst can go by a different title (e.g. Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst). Regardless of job title, the data analyst is a generalist who can fit into many roles and teams to help others make better data-driven decisions.

What material does this path cover?

  • Python Programming 
  • Data Cleaning/Data Wrangling
  • Data Visualization
  • Command Line Programming
  • SQL & Databases
  • APIs
  • Web Scraping
  • Statistics and Probability

This is an abridged version of what you'll learn on this path — to get a more comprehensive look at the Data Analyst Path curriculum, you can visit the path page here.

What plan do I need to subscribe to?

The complete Data Analyst path can be accessed under both our Basic and Premium subscription plans.


The Data Scientist in Python Path

This path covers the skills you'll need to make valuable predictions using Machine Learning algorithms — it's worth noting that this path encompasses the entirety of the Data Analyst path, since Data Scientists rely on these skills as well. 

What material does this path cover?

In addition to the Data Analyst material listed above, this path also covers: 

  • Machine Learning 
  • Linear Algebra
  • Neural Networks
  • Algorithms
  • Natural Language Processing
  • Kaggle Competitions
  • Spark

This is both our largest and fastest growing path, with more courses being added on a monthly basis — to get a more comprehensive look at the Data Scientist Path curriculum, you can visit the path page, here

What plan do I need to subscribe to?

The complete Data Scientist path requires a Premium subscription to access. 

However, as we mentioned above, the first half of the Data Scientist path is comprised of the material covered in the Data Analyst path — because progress transfers from path to path, you can easily complete the Data Analyst path under the cheaper Basic plan first, then upgrade to the Premium plan when you're ready to tackle the Data Science-specific material.


The Data Engineer Path

This path teaches you how to build data pipelines to work with large datasets. Data engineers build and optimize the systems that allow data scientists and analysts to perform their work. Every company depends on its data to be accurate and accessible to individuals who need to work with it. 

Data engineers ensure that any data is properly received, transformed, stored, and made accessible to other users. It currently covers processing large datasets in pandas, optimizing code performance, and how different data types can help speed up your analysis.

This path is relatively new and still in beta — we're currently releasing courses in an iterative rollout (i.e. we add new courses as we complete them).

What material does this path cover?

  • PostgreSQL
  • Optimizing Databases
  • Processing Large Datasets w/ Pandas
  • Optimizing Large Dataset Performance
  • Building Data Pipelines

Again, this path is our newest and will continue to grow as you work through it. To get a more comprehensive look at the Data Engineer Path curriculum, you can visit the path page,  here, or download a copy of it, here.

What plan do I need to subscribe to?

The Data Engineer path can only be accessed under our Premium subscription plan.

You can view the full list of courses contained in each path here:

Data Analyst in R- Learn how to manipulate and analyze data with R. 

Data Analyst in Python - Learn how to manipulate and analyze data with Python.

Data Scientist in Python - Learn how to make inferences and predictions from data with Python.

Data Engineer - Learn how to build data pipelines to work with large datasets.


Are there any prerequisites?

The Data analyst and Data scientist paths don't require any previous background (i.e. math, programming, etc.), while the Data engineer path assumes previous knowledge of data science topics (completing our Data Scientist path is sufficient experience).


What are guided projects?

Each path includes projects that allow you to practice and sharpen your newly learned skills. 

Our projects deal with real life scenarios, like searching for patterns in crime, creating a stock trading algorithm or investigating airplane accidents. Completing these projects also helps you start building a portfolio while learning. We recommend uploading every completed project to your Github account to showcase to potential employers.

You can look through all the projects we have available on our project page.

For more information on how you can use projects to build your portfolio, our portfolio-building blog series is definitely worth a look as well!

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