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TLDR: DataCamp Career Track Review
💥 DataCamp is the place to learn data science with portfolio-ready projects, courses, skill tracks and career tracks.
💥 Career tracks train you on specific career skills, and typically take 6 months to a year to complete.
💥 DataCamp has 14 data science-related career paths using R, Python and SQL.
🚨 Sign up for DataCamp here. 🚨
So what exactly is DataCamp?
DataCamp is a learning platform for all things data science.
Using text and video-based tutorials and exercises, you’ll learn:
And much more.
From beginner to advanced, DataCamp offers data science courses, skill tracks, career tracks and portfolio-ready projects.
DataCamp Pros & Cons
What’s a DataCamp career track?
Career tracks contain courses, lessons, quizzes and projects.
They give you a strong foundation for starting a career in data science.
⏲️ DataCamp career tracks are only for developers serious about starting a career in data science.
They typically take 6 months to a year to complete. ⏲️
DataCamp has 14 career tracks including:
⚠️ Level: Beginner
📚 Courses: 19
⏱️Estimated completion time: 76 hours
In this data science career track, you’ll go through 19 DataCamp R courses.
Using text and video tutorials, quizzes, and interactive exercises, you’ll learn about:
✅ how to import, clean, manipulate and visualize data
✅ working with real-world datasets to learn statistical and machine learning techniques
And much more.
You can check out the Data Scientist with R career track
👉 here. 👈
DataCamp’s Data Scientist with R career track has 19 courses including:
🔷 Introduction to the Tidyverse
🔷 Data Manipulation with dyplr
🔷 Joining Data with dyplr
🔷 Introduction to Data Visualization with ggplot2
🔷 Intermediate Data Visualization with ggplot2
🔷 Introduction to Importing Data in R
🔷 Intermediate Importing Data in R
🔷 Working with Dates and Times in R
🔷 Introduction to Writing Functions in R
🔷 Exploratory Data Analysis in R
🔷 Case Study: Exploratory Data Analysis in R
🔷 Supervised Learning in R: Classification
🔷 Supervised Learning in R: Regression
🔷 Cluster Analysis in R
Courses increase in difficulty as you progress along the career track.
Now let’s see what’s inside one of these courses, Cleaning Data in R.
Want to know about Introduction to R?
Check out our review here.
Course Inside DataCamp Career Track: Cleaning Data in R
🏃♀️ Exercises: 44
📹 Videos: 13
⏱️ Estimated Time: 4 hours
There are 4 chapters in Cleaning Data in R:
- Common Data Problems – convert data types, apply range restraints, remove duplicated data points
- Categorical Text and Data – fix whitespace and capitalization inconsistencies, collapse multiple categories, reformat strings
- Advanced Data Problems – data cleaning, verify values, missing values
- Record Linkage – merge multiple data sets together, calculate similarity between strings, join datasets into master dataset
Each chapter contains lessons, videos, coding challenges and quizzes.
Let’s sneak a peek inside one of these chapters… 👀
Chapter 2: Categorical Text and Data
Lessons, quizzes and videos cover key concepts such as collapsing categories, cleaning text data, and detecting inconsistent text data.
In this chapter of Cleaning Data in R, you’ll learn:
- how to fix whitespace & capitalization in category labels
- collapse multiple categories into one
- reformat strings for consistency
Is there a certificate of completion for DataCamp career tracks?
Yes. DataCamp calls their certificate of completion a Statement of Accomplishment.
👍 DataCamp offers a Statement of Accomplishment for every successfully completed course, skill track and career track.
DataCamp delivers a series of 86 portfolio-ready projects.
So when you’ve finished your career track, you can get cracking on:
🟧 Visualizing Covid-19 with ggplot2
🟧 Load, transform and understand images of bees in Python
🟧 Classify song genres from audio data
🟧 Analyze international debt statistics with SQL
And many more.
Projects cover topics such as data manipulation, case studies, applied finance, machine learning and beyond.
DataCamp Community and Support
With a yearly subscription, you’ll have access to live support from the DataCamp team. Plus you’ll get to interact with the DataCamp community.
With a monthly subscription, you’ll only have access to the DataCamp community.
And with the free tier, you won’t have any access to support or community.
There is a free tier on DataCamp, though its offerings are limited.
But a subscription gives you plenty of perks:
🤩 Best Deal!
You can check out DataCamp
👉 here. 👈
DataCamp Career Track Review: Conclusion
So to review:
DataCamp is the place to learn data science using:
💥 skill tracks
💥 career tracks
DataCamp has 14 data science-related career tracks using R, Python and SQL technologies.
So no matter what branch of data science you choose, DataCamp probably has a career track for you.
🤔 Not sure if DataCamp is right for you?
- What's in a DataCamp career track?
DataCamp career tracks include courses, lessons, quizzes and projects in three technologies: R, Python and SQL. They’re designed to give developers a deep understanding of concepts in a specific career discipline. And they typically take 6 months to a year to complete. In DataCamp career tracks, you can expect up to dozens of courses to hone in on building your data science skills. There are currently 14 data science career tracks on DataCamp. Career tracks are designed explicitly for you to get a job in the data science career field.
- Does DataCamp have a data scientist career track?
Yes, DataCamp 14 data scientist career tracks. They cover everything from Data Scientist in R to Machine Learning Scientist with Python to Data Engineer with Python. So no matter what particular field of data science you're interested in, DataCamp probably has something to suit your career goals.
- What DataCamp R courses are there?
There are nearly 150 R courses on DataCamp. They range in difficulty from newbie to advanced. So if you're a newbie, you can start with something like Introduction to R. But if you're a little further along in your understanding of R, you can take a course like Intermediate Regression in R or Cleaning Data in R.