A Better Way to Learn:
The Dataquest
Teaching Method

An Outcome-Based Learning Platform

Dataquest’s courses teach skills faster than other platforms — with better retention. The best way to understand why Dataquest delivers better learning outcomes is to understand the principles that guide the design and development of the learning experience.

Our teaching principles break down into three main pillars:

  • Contextual Learning An outcome-based platform requires a different structure than a topical platform. Applied learning teaches learners to combine multiple skills and tools to solve a problem or accomplish a task. Dataquest projects and lessons simulate the challenges that working data professionals face every day.

  • Efficient Learning The largest expense when it comes to learning is time. Dataquest courses maximize learning time by eliminating videos, setup, and fill-in-the-blank exercises. Learners apply skills as they learn, and they receive feedback from our proprietary answer-checking system. This maximizes retention and keeps learners motivated.

  • Structured Paths Paths guide learners step by step through the foundational skills they need to become well-rounded data practitioners. Paths aren’t repetitive, and they don’t have gaps. Each lesson prepares the learner for the one that follows to accelerate progress.

A Deeper Look at
Contextual Learning

The best way to learn how to complete data projects is by building data projects. Dataquest learners spend their time working through real-world data challenges that teach learners to combine multiple skills and tools to solve a problem or accomplish a task.

Contextual learning has several benefits:
  • It builds confidence and experience.
  • It trains learners for professional data work.
  • It helps learners build a portfolio of projects.

Predict The Weather
with Python

View Project

Analyze Customers
with SQL

View Project

Model Churn
with Excel

View Project

Predict Football Winners
with Python

View Project

Classify Images
with Python

View Project

Build a Spam Filter
with R

View Project

Design Principles That
Support Contextual Learning

Applied learning is a proven approach to improving student engagement and retention. We've used leading principles in digital instructional design to guide our course development:

Transfer This is a learner’s ability to apply what they’ve learned to a novel situation — “Transfer is the ultimate aim of education, as ensuring that the facts and skills learners learn are going to be usable in a variety of situations is the point of learning” (Mckeough, Lupart, & Marini, 1995).

Problem-based Learning (PBL) is an active approach to learning wherein learners collaborate to understand and solve complex, ill-structured problems (Barrows, 2000; Savery, 2006). PBL students are more motivated to reach their outcomes and engage in deeper learning and problem-solving approaches.

Authentic Learning This learning environment promotes authentic contexts and activities, access to expert performances, multiple roles and perspectives, reflection and articulation, coaching and scaffolding, and collaborative knowledge-building (Herrington & Oliver, 2000).

Student-Centered Learning SCL is an “environment that allows learners to take some real control over their educational experience and encourages them to make important choices about what and how they will learn” (Doyle, 2008. p. xv). SCL evokes a change in attitude from a fixed mindset to a growth mindset, and it refocuses an instructor’s role from a teacher to a facilitator.”

Maximizing Learning
Efficiency

The largest expense when it comes to learning is time. Motivation is key to learning, and making quick, measurable progress is one of the best ways to build and maintain a learning habit.

How we create efficiency:
  • Meaningful challenge
  • Real-time feedback that removes blockers and deepens understanding
  • Elimination of video lectures

Meaningful Challenge and Real-time Feedback

Learning happens best with the correct degree of challenge. Too much challenge can be a blocker, especially if the help is unavailable. Dataquest's proprietary answer-checking system automatically evaluates coding exercises and provides guidance on incorrect submissions. This keeps learners from getting stuck and losing motivation. This allows us to create open-ended exercises that mimic how people actually code.

Elimination of Video Lectures

Lectures are the foundation of many online and in-person courses. They have also been proven to be less effective than active learning.

Students learn best when presented with a scenario and guided to experiment with it immediately. Lectures can trick you into feeling like you've absorbed a concept. Practice is critical to retaining and applying new skills. Dataquest's split-screen environment quickly introduces concepts, invites you to practice them, and assists with additional resources as necessary.

Structured Paths

We've organized Dataquest's courses into paths that guide learners through skills acquisition one step at a time. This eliminates the need to chart your own curriculum, and it prevents learning from becoming repetitive or creating gaps.

Backwards Curriculum Design

We create courses with the end in mind. We start with a simple question: "What should the learner be able to do by the end of the course?"

Working with industry professionals, we determine a real-world answer to this question and build learning objectives based on that scenario. These learning objectives provide the scope for the course. From there, we create a project that, when completed, will demonstrate a learner has mastered the skill and can apply it in a job setting.

For example, here is a learning objective for one of our machine learning courses:
    • Learners will be able to train a linear regression model.

The project in this course requires learners to train a linear regression model to predict home sale prices. The course then teaches the skills required to complete the project successfully.

Gapless Learning for the Beginner and the Advanced

We built Dataquest's guided paths with both beginners and advanced learners in mind. The paths' structure allow beginners to tackle new skills without getting stuck, and advanced learners can skip the learning sections and complete the exercises to test their skills and identify gaps. This eliminates the need for long assessment, and it enables learners to progress quickly to the appropriate challenge level.

[course_offerings_landing show_search="0"]

Common Learning Objectives

97% of learners would recommend Dataquest for career advancement. Individual learners report a median salary increase of $30,000 per year. Here are some common skills they learn that drive those outcomes:

1. Answer a business question using SQL

  • Convert a business question into a data question.
  • Write SQL queries to answer the question using advanced techniques like multiple joins, aggregation, window functions, etc.
  • Analyze the data, ensuring statistical validity and creating recommendations.
  • Create a report summarizing the findings, including text, data, and visualizations.

2. Use Python or R to clean and analyze data

  • Work with data from various sources, including Excel spreadsheets, CSVs, data stored in SQL-based databases, JSON data, via an API, etc.
  • Use Python and pandas or R and the Tidyverse to clean and convert data into the required format for analysis.
  • Analyze data programmatically, ensuring analysis is statistically valid.
  • Summarize findings in writing with supporting data and visualizations.

3. Build data pipelines and automate reporting

  • Analyze existing reports, and identify the sources of the data (i.e., spreadsheets, a MySQL database, APIs, PDFs, etc.).
  • Export, clean, and pipe data from and to various sources and systems.
  • Write a script to automate report creation, saving the final deliverable as an Excel spreadsheet.

4. Build prediction models

  • Acquire and clean existing data in preparation for modeling.
  • Separate the data into training and test sets.
  • Select the best approach and algorithm for the task. Optimize and customize the algorithm to fine-tune results.
  • Summarize findings in writing supported by data.
  • Build automated systems that use predictive analytics to get more from your team’s data.
  • Grow a basis for advanced skills, like deep learning and AI.

5. Follow engineering best practices

  • Use command line scripts to automate repeated commands or data processing.
  • Simplify data pipelines and workflows.
  • Work with data across multiple tools or applications.

Outcomes Survey

Dataquest surveys over 5,000 learners each year to better understand our platform's impact on their careers.

Beyond some of the hard numbers, learners report that Dataquest has positively impacted their careers and lives.

"I am getting the attention and trust I always wanted my employer to have in me." - Annual Survey Respondent
landing Banner

4.7

Average course rating on our platform out of 5

92%

Achieved their learning goal using Dataquest.

97%

Recommend Dataquest for learning data skills.

  • I use what I learned on Dataquest in my current job

    64%

  • Average time spent learning each week

    6 hours

  • Staff expansion and onboarding

    12%

  • Overall rating of the Dataquest Platform

    8.4

For Teams

We train data teams at businesses around the world. From healthcare to banking, teams rely on Dataquest to upskill their data talent and align their organizations on best practices.

Providing effective training in valuable skills not only helps you get the most out of your data, it will also improve retention. According to a 2019 LinkedIn report, 94% of employees say they’ll stay longer if their company invests in their learning.

Upskill your team
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo
Team Logo