Are you an aspiring data scientist or are you looking to improve your skills? Did you hear of Datacamp but you aren’t sure if it is worth it? In this article, I’ll be giving a detailed Datacamp review. It’s courses, style of learning, features, cost, pros, and cons, and more to give you a broad idea of what it is like learning on Datacamp.
My story and how I discovered Datacamp
Late 2017 I stumbled on an article called data science the ‘Sexiest Job of the 21st century’. The article went on to describe data analysts’ job descriptions, salaries, and core skills that will make one a great data scientist.
I checked the box for more than half of that list and I was intrigued about being a Data Scientist, so I decided to do my research.
Early 2018, I completed a Python beginner’s course using a PDF I found online, but it had nothing on data science.
I went back online to do more research and stumbled on both Datacamp and Dataquest. Datacamp caught my attention immediately and I signed up for it.
I wasn’t sure what to expect from Datacamp initially. I was wary and a bit apprehensive. I was new to both Python (even though I had beginner’s knowledge), data science, and Datacamp.
But Datacamp’s intuitiveness blew me away. The courses were streamlined to meet my needs as a beginner.
I couldn’t do much my first time on Datacamp since I had only one free chapter (I used the free plan which was limited to one free chapter), but it gave an idea of how Datacamp works and I knew I was going to come back.
Since then, I have completed two more Python tracks and an SQL track. Now, let’s know that why I am writing this Datacamp Review.
Why I am writing a Datacamp review
As someone with inside knowledge of Datacamp, I know I am suitable to give a detailed review of what to expect with Datacamp.
I’d like to help others understand the platform, how it works, what it offers, and if you should go ahead and pay for the courses or not.
- If you buy the course through the links mentioned in this article, we can earn some affiliate. It can help us to keep running this blog.
A brief introduction to Datacamp and how it works
What is Datacamp?
Datacamp Inc. is a MOOC (Massive Open Online Courses) company founded in 2014 by three Belgian data scientists, Jonathan Cornelissen, Dieter De Mesmaeker, and Martjin Theuwissen.
It is an online teaching platform strictly for Data Science and Data Analysis.
The platform is an interactive, active, engaging one that provides resources for data science to (aspiring) data scientists at all levels – beginners, intermediate and advanced.
The courses are also self-paced. You work on them at your own rate. But the more time you take, the more money you spend, especially if you make monthly subscriptions.
One of Datacamp’s strongest suits and that which makes it stand out from other platforms is its effective blend of exercises, videos, online interactive coding console and shell, quizzes, practices, and projects.
The coding console with exercises was one of my delights with the platform. As a beginner, I get to use practice right there on the page, focusing on building my skills first without worrying about setting up anything.
Since Datacamp’s main targets are data scientists and data science career tracks, the courses are focused on R, Python, and SQL, with extended courses on Excel, Tableau, Scala, and Git.
Datacamp’s courses are massive. It houses over 354 courses for data scientists and data analysts.
With expert instructors, a range of skills tests and assessments, and content to practice on, It is very obvious they are out to make a difference in the world of Data Science. Let’s know how Datacamp works in this Datacamp Review.
How Datacamp works
As a new user, when you first try to access Datacamp’s official website URL (datacamp.com), it displays a user login/sign-up page.
Trying to access Datacamp’s resources or materials, even if it is the free version, means you have to be logged in first.
The signup page allows users to sign up either by supplying their email & password or getting authenticated from their Google, Linkedin, or Facebook profiles.
Not sure where to start? Datacamp has a skills assessment page that can be accessed from the resources link. The assessment is unlimited and available in 14 technologies. It doesn’t require a sign-in or payment to access.
It is inevitably the first step for users who are confused about their skills level or what tracks they should go for. Assessments are basically a series of 10-15 minutes challenges to check your skill level.
The filter dropdown allows a user to filter the technologies they would prefer to take an assessment on.
Unlike Datacamp’s main interface, the assessment requires a user’s actual code that will reflect their abilities. Questions are adaptive and adjust to the user’s proficiency and level, based on performance on the assessment.
Each question on the assessment has a timer and once the time runs off, the system automatically submits the answer provided (it submits nothing if nothing was written in the input boxes) and automatically moves to the next question.
The main aim of the assessment and why it is proposed as a starting point is because personalized recommendations on which tracks to start with are given based on the score obtained.
Once logged in, one of the first things to do is to understand Datacamp’s unique features, interface, and style of teaching. This is where most questions start.
From the image above, you see the dashboard area for learners.
We will look in detail at the links in the sidebar menu, which is My Progress, career tracks, bookmarks, etc, what they mean to the user, and how they are incorporated into the learning path.
The top menu holds the different sections of Datacamp, namely Learn, Workspace, and Certification, each with its own dashboard and role.
These sections are not expected to be used consecutively. Depending on what you want from Datacamp, you could move from one section to another.
Learn, Workspace and Certification
Learn is the default section. It is the first section individuals users and learners see on the platform.
This section area is the main learning path. Career tracks, skills tracks, assessments, practices, exercises, and videos are all available here.
This section houses Datacamp’s initial and main scheme. To teach data science.
Datacamp believes Data Scientists should work and collaborate seamlessly. They noted the lack of a workspace with infrastructural tools and environment readily available.
They also noticed that version control, collaborative tools, deployment of data and dashboards are in favor of Software Engineers and not intuitive of data analysis.
Workspace is one of their latest innovations to bridge that gap, and it is in its beta stage. As of 2018 when I started using Datacamp, this wasn’t there.
With Workspace, data analysts can access relevant data online, collaborate easily with each other, and have inbuilt quality control.
Workspace main interface models a JupyterLab interface. The left panel holds files, the main page section holds the code and the menu on the top right has options for share, publish, documentation, provide feedback, and suggest a feature.
It is also pre-loaded with functions, packages, and modules commonly used by data scientists. So rather than install them, you can just load or import them when needed.
Certification is a program done by Datacamp which involves a 10-min timed assessment, a coding challenge, and a case study presentation. Certification requires an active, paid subscription.
For more questions on certification, you can check here.
Getting certified by Datacamp allows you access to their career services.
What are career services in Datacamp?
This service is a branch of Datacamp that helps data scientists and users of the platform in careers.
The career services team are career experts, and career coaches guides. They help Datacamp’s certified users with
- defining job goals,
- step-by-step planning on how to accomplish the goals and get the job,
- personal branding – reviewing resumes and profiles on Linkedin, and
By getting certified you have access to
- join workshops with other users on how to network and interview preparation strategies,
- career networking sessions with potential employers, and
- personalized job matching that links you to job opportunities and takes into consideration your professional experience, and personal ambition.
Features of Datacamp
I did mention that Datacamp has many unique features which could make the first time on the platform quite confusing.
In this section, I will try to list out all of Datacamp’s features and how they work towards making you a satisfactory experience on Datacamp.
Datacamp’s courses are divided into two main curriculum or paths. They are Career and Skills tracks.
Although courses on both tracks overlap, It is important that you can distinguish between the two so you can effectively choose the best learning track for you.
Career tracks are course programs mapped out so users can start a new career in major Data Science fields.
As of the time of writing this Datacamp review, there are over 12 career tracks on Datacamp on 3 technologies – R, Python, and SQL, with 6 focused on R, 5 for Python, and 1 for SQL. The career tracks are,
- R programmer
- Data scientist with R
- Data analyst with R
- Python programmer
- Data scientist with Python
- Data analyst with Python
- Quantitative analyst with R
- Statistician with R
- Machine learning with R
- Machine learning scientist with Python
- Data Engineer with Python, and
- Data analyst with SQL server.
The career tracks target core languages and fields in data science are for users with different levels of proficiency or skills.
The beginner tracks start with R or Python programmer, with different courses listed out to teach you the basics of these languages while more advanced tracks are for Machine learning.
How do you use the career track?
The career tracks are not expected to be followed consecutively. Instead, a user is expected to pick the track that best suits their needs and skills.
I joined Datacamp having beginner’s knowledge of Python and I knew I wanted to use Python, not R for data science.
Armed with this information, I chose Data Scientist with Python as my first career track. I didn’t need the Python Programmer track since I already had knowledge of beginner Python.
Enrolling in a Career track is important in becoming skilled in a particular aspect of data science.
Each career track has a number of courses or topics attached to them, estimated time of completion, XPs to be earned. Career tracks also have challenges, practices, and projects to level up your coding.
NB: You can only enroll in one track at a time. You also need to be enrolled in a career track to earn the statement of accomplishment for that track.
Let’s talk about the Skills tracks in this Skills tracks focus on scaling specific skills in different languages. They are made up of 2-7 complementary courses.
They are mostly for programmers seeking to get specific skill-sets, not a career path. E.g of skills tracks includes Time series in Python, Network analysis in R, etc.
The platform offers skills tracks in 5 different technologies – R, Python, Spreadsheet, Tableau, and SQL. with over 50 tracks available to users – 17 in Python, 23 in R, 3 in Spreadsheet, 5 in SQL, and 2 in Theory.
Just like career track, skills track also have courses outlined for each track and instructors to teach them.
NB: Note that courses in the skills track can also be found in career tracks.
Datacamp’s learning interface
I did mention that Datacamp’s unique intermingle of visual lessons, exercises and practice all on the same page is one of the reasons I chose the platform.
When you choose a Career track or Skills track and start actively learning, you will be shown an interface that looks like the image below.
The interface is divided into four active components – an exercise module, an instructions section, an active coding console, and an Ipython shell.
The buttons on the top-right menu show the XPs gained throughout that course, a switch to the mobile version of the same course, a button to show the video of the course, slides, and an option to provide feedback.
The green circle shows if the interactive console is active or not. It changes to red when the console is not active or there is a network problem.
The exercise module is the text area that discusses the main topic. It gives examples and it is directly relevant to the instructions just below it.
The instruction components are challenges directly related to the topic at hand and the exercise material.
There could be one or more instructions to test your understanding of the topic. If you are stuck or confused, it has an option to use hints or show answers.
Using hints and show answers means subtracting from your overall XP gained. If you do not use a hint, you will get the complete XP for that challenge.
The script interface on the right is an active coding console to test or practice exercise examples and run instructions challenges.
To view the output which will display in the ipython shell, you need to click run code.
To use the console for challenges, you click on Submit Answer, which compares your solution with the expected solution and gives you feedback.
Run Code and Submit Answer buttons can be used and reused as often as possible.
The ipython shell displays output for code written in the coding console. For R and Python, this shell holds slides and can be used interactively for testing code on the spot. For SQL, it accommodates the database to be worked on.
XPs are sort of coins to be earned. They hold no monetary value and cannot be used to buy anything within or outside the platform. Instead, they measure your effectiveness and level of commitment.
XPs are calculated based on courses, exercises, and challenges completed within Datacamp.
Each course, exercise, or challenge has a maximum XP to be earned and each time a user uses a hint or shows answers, XPs are deducted.
The total number of XPs earned can be seen at the right-hand corner of your profile page. They are a good way to measure progress and users can compare the XPs earned to expected XP.
Courses and Topics
There are over 354 courses available on Datacamp on R, Python, SQL, Git, Shell, Scala, Spreadsheet, Tableau, Excel, Theory, and PowerBI.
Each course has an instructor and a timely completion estimated on it.
Courses can be filtered based on topics like data manipulation, probability, and statistics, applied finance, programming, etc. Courses are also accessed from tracks – either career or skills.
When a track or topic is selected, the courses under that track are outlined like the images above. Each course taught is part of a chapter that forms a curriculum.
On the main dashboard of a course, the instructor’s profile and expertise, together with other collaborators, course descriptions, number of participants, XPs to be earned, and number of exercises are displayed.
Projects are real-world problems that users need to solve on their own. There are about 79 projects and they can be filtered by technologies, topics, guidance options, and relevance. Think of projects like dataset problems to be solved on Kaggle.
Datacamp’s projects are divided into two. Guided and unguided projects.
Guided projects are real-world problems and datasets with step-by-step tasks given by an instructor on how to solve them.
Some users, like me, felt we were being spoon-fed. And so Datacamp introduced unguided projects.
Unguided projects are mostly for intermediate and advanced projects. With unguided projects, users get to try their hands on messy, open-ended datasets without the guidance of an instructor.
Working on a project takes place on a Jupyter Notebook with step by step analysis of the case and tasks to get the projects done (for guided projects).
For unguided projects, the codes are removed and the users have to get everything done on their own.
Unguided projects have additional support in the form of a live code-along scheduled and uploaded to the platform so users can follow the instructor of the project.
Once a course has been completed, the user can practice on it. There are over 52 practice lessons on R, Python, and SQL. Practice is basically to keep the learner sharp with quick daily challenges.
Unlike assessments, practice is not timed. It also doesn’t require coding. Instead, they consist of multiple-choice questions and the user needs to get 5 challenges correct.
How good are the courses? What does the curriculum cover?
With over 350+ courses on the platform, it is hard not to find something that suits you.
Datacamp’s curriculum covers as many aspects of data science as possible, including R, Python, machine learning, Oracle, Tableau, Power BI, and SQL.
The courses are good. They are structured to cover basic and important topics in each course and are taught by brilliant instructors.
Can I land a job with Datacamp data science courses?
I’d say yes. Especially jobs with beginner and intermediate roles. Datacamp’s courses are abundant and broad.
If you pick relevant tracks, and projects, you will broaden your knowledge, gain hands-on skills and make yourself marketable.
You would also have projects and quizzes you have worked on added to your portfolio. This would boost your profile, resume, and gain you points during an interview. Various details shared in this Datacamp review are self-explanatory providing the answer to this question.
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The cost of learning on Datacamp
Datacamp doesn’t come cheap. They offer 5 pricing subscription models. The first three fall under Personal Plans, while the last two fall under Business Plans.
These plans are aimed at individual and business learners and are billed monthly and annually.
Starting with $0/month for the free plan to a price to be determined per request for enterprise business plans, Datacamp tries to reach as many people as possible.
Datacamp pricing and plans details
The pricing and the details of the plan of the Datacamp are quite simple, so let’s know the pricing plans of Datacamp in this Datacamp Review.
This is the default and most basic plan of Datacamp. It is aimed at individual users. It is mostly a trial version, allowing access to only the first chapters of each career or skill track.
It is also a resourceful way of testing out the platform and trying out Datacamp core features, giving them a feel of the platform and what to expect without the burden of payment.
The first chapters open to free plan users also come with the usage of the interactive editors and coding console, access to the videos, submission of challenges, and practices in that chapter. XPs for the open chapter can also be earned.
Assessments are open to everyone and not dependent on payment, so free plan users can practice as long as they want on assessment questions, to gauge their expertise level.
The free plan provides access to 3 Data literacy courses, 7 projects, 3 sets of practice challenges outside the career track chosen, and workspace access.
The standard plan, starting at $12.42 per month is an advanced level of the free plan. It covers all the essentials to grow your data science skills. It has everything in the free plan, but with more addition.
This includes access to the 354 courses provided by Datacamp (as at the time of this Datacamp review), the 12 career tracks, 50+ skills tracks, unlimited practice challenges, certification, live-code along with certifications, a community chat, and all mobile apps courses and practice.
The main limitation of this plan is the access to projects.
Datacamp names this the plan for learners who want access to all projects.
Starting at $33.25 per month, the plan covers everything in the standard plan including access to 80+ projects, Tableau, PowerBi, and Oracle content, and priority support.
This is a business plan for simple management and small teams. It starts at $25 per month and covers everything in the premium plan in addition to admin roles and permissions, assignments, admin dashboards, and live chats for admin.
This is a plan for advanced integrations, and reporting for large organizations. The price is determined on a request by the organization.
Enterprise plan covers everything in a professional plan in addition to LMS integration, dedicated customer success manager or learning solutions, data export, and custom learning tracks.
A little tip
Datacamp is known to give discounts on paid subscriptions periodically. On special occasions, they could give free access to all their courses for a week.
This is something I have personally benefited from. If your budget is tight, you can watch out for these.
I thought to share this point in this Datacamp Review so that most of you get benefited from the available discounts and offers.
Is Datacamp good for beginners?
The simplicity and structure of Datacamp make it an excellent first choice for beginners.
Before delving into Data Science, ML, and SQL core lectures, Datacamp offers users the chance to level up their skills through assessments, and beginners courses in Python and R.
In this Datacamp Review, I recommend Datacamp to all beginners as it also provides daily practice challenges and quizzes to keep beginners busy.
Pros and cons of Datacamp
This covers what I like and don’t like about the platform. I like a lot of things about Datacamp, but I feel there are things worth looking into. So, let’s discuss some of the Pros and Cons of Datacamp in this Datacamp Review.
Pros of Datacamp which I want to share in this Datacamp Review.
A very intuitive learning interface
Datacamp’s coding interface has been my bias from the beginning. It is simple and practical to learn because the exercises, coding console, and instructions are right on the same page and I could try my hands on challenges immediately.
The structure of the courses and assessments
Datacamp has over 350+ courses separated into career and skills tracks. Assessments are divided into exercise challenges, assessments, practices, and quizzes on different languages, topics, and tracks giving the user different options to choose from.
Career & skills track feature
The career and skills track feature saves time and gives direction. Rather than waste time trying to learn everything, the track gives users the option to just choose what they want, learn it and get a statement of accomplishment.
Though in its BETA stage, the workspace is a beneficial addition to Datacamp. What is a data analyst without a sustainable workspace with work tools and collaboration tools at your fingertips?
Cons of Datacamp which I want to share in this Datacamp Review.
Restriction of access to mobile apps
The use of mobile apps is too common these days for it to be restricted to paid subscriptions. Access should be granted to everyone on the mobile app, just like the website, but some courses will be locked until a subscription is made.
No official certificates
Datacamp gives a statement of accomplishments. For all the money being paid to learn, it is distressing to not have a certificate.
Does Datacamp give certificates?
No. They give statements of accomplishments.
The main reason for this review is to let readers decide if Datacamp is worth the effort and money. My thoughts on that are YES. Datacamp is definitely worth it.
It doesn’t just provide the core skill set but has grown to touch career services, workspace collaboration, and networking, which is something I know Data Scientists can benefit from.