7 Learning Tips for Data Science Self-Study


Working in data science, or acquiring data science skills, does not rely on a degree or traditional career pathway.

how to self study data science

A combination of non-traditional learning with the right skills and experience can take you far, whether you’re looking to start your career in data science, pivot into the field, or simply apply these modern, highly relevant skills to another area of expertise.

“The runway is a lot shorter now for data science,” said Joseph Santarcangelo, Ph.D., IBM data scientist, and instructor for several edX data science courses and programs, from Python to deep learning. “For a lot of it, you don’t have to have a Ph.D. anymore. You don’t have to spend years and years studying something.”

Opportunities Data Science Skills Unlock

The field of data science is full of potential and opportunities. A general search on Indeed for “data scientist” returns over 15,000 data science jobs, many of which pay in the $90k to well over $100k salary range. Data science experts and artificial intelligence practitioners made it to the top 14 and 15 spots of LinkedIn’s 2021 Jobs on the Rise report. And although 2020 was the first year in a while that data scientist wasn’t the number one job in Glassdoor’s annual ranking, it’s back up to the number two spot for 2021.
Data scientist is not the only job role, however, where data science skills are valuable. Experts believe that learning data science skills will help candidates add value to any role, giving job seekers with this skill set an edge over the competition. If you’re currently in a department like marketing or finance, for instance, studying data science could open new career doors for you.

“Data science is a 21st-century job skill that everybody should have,” says Eric Van Dusen, curriculum coordinator for data science education at the University of California (UC), Berkeley. “Every field. I tell students, you all need to come out with this set of skills. You’re going to be a lot more powerful in whatever career you go into.”

How Hard Is It To Learn Data Science?

The difficulty of learning data science depends on your background. Like learning human languages, having an existing background in computer science and mathematics will make the jump to data science easier.

Non-traditional learning paths such as online data science courses and programs from edX provide flexibility to figure out what you like about data science, which path to follow, or whether you’d be happier applying data science skills to a non-data science role.

“You’ll get 70 percent of the way there in your first few steps. A year of studying data science will get you very far.”

“The first step is the largest,” Santarcangelo said. “You’re going to make the biggest jump. You’ll get 70 percent of the way there in your first few steps. A year of studying data science will get you very far.”

Can You Teach Yourself Data Science?

Data science is about doing. Download programs to begin your first programming language. Brush up on the mathematics behind data science. Play with data visualization using open-source tools. The more you explore, the easier it is to learn how to be a data scientist. But eventually, you are likely going to need some guidance.

7 Tips to Guide Self-Studying Data Science

1. Start Anywhere—But Start

To important things to keep in mind as you navigate your learning experience:

  • Start somewhere: There is no “right way” to pursue a career or education in data science. The process itself will teach you where your strengths and interests lie. Some applicable computer science advice from David Joyner, Ph.D. Executive Director, Online Education & OMSCS, College of Computing, Georgia Tech: “I think the best way to learn is to take a computer science class, learn what’s possible and then decide, ‘Using what I’ve learned here, what could I build that would be of strong personal use to me?’ Even if it’s just a personal project.”
  • You don’t have to know everything: Data scientists learn by doing, so choose a project and just dive in. For example, in IBM’s Python Professional Certificate program on edX, a project mini course is built in to provide that critical hands-on experience.

2. Pick Up a Programming Language
You cannot learn data science without learning to code. Data scientists build algorithms and environments to run those algorithms. Of the handful of popular programming languages for data science, here are a few to consider starting with:

  • Python: Python is beginner-friendly, mimics English syntax, offers abundant libraries and community support, and has a wide variety of applications beyond data science. It’s a general-purpose language with enough add-ons that you can perform a wide range of data science tasks from statistical analysis to visualization and beyond.
  • R-programming: R is a contender if you’re interested in or already in research and adding data science to your skillset. It uses statistician syntax, handles massive large-scale data, and communicates those results through robust and rich visualization.
  • Context-specific language: There are lots of powerful and viable alternatives to learning Python or R. Find out which languages your current or ideal company uses. Choose one based on the conditions of your personal journey.

3. Practice The Fundamentals

The data science method looks similar to the scientific method, but with the heaviest emphasis on ensuring that all the data used is of the highest quality. Data wrangling comprises the bulk of data science because without quality data, your insights are meaningless, or worse, incorrect.

Here’s what a typical data science workflow looks like:

  1. Ask the question
  2. Find your data, whether it’s from in-house data, a public training dataset, or data mining you’ve done yourself
  3. Clean the data
  4. Analyze and explore
  5. Communicate and/or visualize the results

4. Dive into the Technical
One area where traditional learning can be beneficial is in the technical aspects of data science. The field has underlying mathematical concepts that separate data scientists from data hobbyists. Some essential concepts for budding data scientists are:

  1. Linear algebra: Training in linear algebra teaches you the very foundations of data science algorithms. Linear algebra also makes it easier to grasp deep-level calculus and statistics.
  2. Calculus: Training in calculus teaches you the underlying theory of machine learning algorithms. Differential calculus looks at the way things change over time.
  3. ProbabilityProbability and prediction are a massive part of the appeal of data science. It’s vital for analyzing data affected by chance and change, i.e., a vast majority of current data.
  4. StatisticsStatistics training unlocks the underlying structure of data and gives it form for insight.
  5. Regression analysis: Learning regression analysis gives you a dynamic understanding of relationships between data points. It opens up rich visualization techniques that help tell powerful data stories and prevent misleading visualizations.

With great instruction, you can master the statistical and mathematical concepts underlying data science and open up creative avenues for manipulating data and communicating conclusions.

5. Delve Into More Advanced Topics
Becoming a well-rounded data scientist involves taking your foundational data science skills beyond simple data analysis. Exploring advanced topics can provide inspiration for your data science specialization:

  • Neural networks: Building machines that can learn without serious human intervention involves building machines that behave like the human brain. The study of all three neural networks—artificial neural networks (ANN), Convolution Neural Networks (CNN), and recurrent neural networks (RNN)—is the study of putting human cognition into the mind of machines.
  • Machine learning: Machine learning applications involve building algorithms that can process data and learn from it, getting better over time without much human intervention. This has applications in a variety of industries and is a hot topic for employers.
  • Deep learning: Going one step beyond machine learning, deep learning uses several layers of algorithms to get closer to human cognition.
  • Natural language processing: Building machine cognition involves machines understanding human communication and the ability for machines to communicate back in human-like language.

Keep in mind that if you plan to stay in data analytics or become a business data analyst, you may not need to delve this deep into artificial intelligence topics.

6. Learn The Tools
There are many tools that data scientists can use to process, analyze, and visualize data. A few common tools include:

  • Github: Not only does Github provide version control, but it can also get your name out there for future employers. It’s a collaborative platform and is one of the first things you should set up on your data science journey.
  • Jupyter notebooks: Essential for working with and sharing open-source software projects.
  • Python or R packages: Make sure you download the packages for your chosen language so you unlock its full capability. Some examples include Pandas, NumPy, MatPlotLib, Scikit-Learn, and RStudio.
  • TensorFlow: The gold standard for open source machine learning platforms.
  • Tableau: The gold standard for data visualization.
  • Apache Spark and Hadoop: Two big data tools essential for large-scale computation and data-intensive tasks.
  • SAS: A statistical analysis tool with a thriving community and support allowing you to mine, manage, and retrieve data.
  • RapidMiner: An end-to-end data science tool.
  • Google BigQuery: A scalable, serverless data warehousing tool.
  • MySQL: An open source relational database management system that works with SQL.
  • Stack OverFlow: A collaboration platform for data science projects.

This is not an exhaustive list. Tools can be overwhelming, but keep in mind the two principles mentioned earlier: start somewhere, and you don’t have to know everything. Instead of focusing on finding the one perfect tool, start playing around with open source tools until you find your favorites.

7. Level Up Your Soft Skills
With all this emphasis on technical skills, it’s easy to forget the soft skills. Whether you’re in research or working for a company, you’ll need to rely on your soft (sometimes called “power”) skills to get results. Making a career in data science is just as much about people skills as it is technical. Qualities like empathy, teamwork, and storytelling can differentiate you from other candidates for data science positions or help advance your sphere of influence within your own company.

Get Started: Learn Data Science on edX

Ultimately, in the data science field, having the right skills and experience is more important than having the right degree. The beauty of starting or advancing a career in data science or analytics is that your path does not have to be linear, so take your time, study hard, and don’t be afraid to revise your goals as you delve deeper into the data science field.

“Being able to extract information from data is actually a very powerful position to be in with data being collected in all aspects of society, ranging from marketing to health and even to sports and entertainment.”

Online courses and programs on edX are a great tool for learning data science outside of a degree program. Audit courses before committing to completing or upgrading to unlocking valuable certificates, move through content at your own pace, and connect with fellow learners, faculty, and subject matter experts for guidance that will help propel your data science career to the next level.

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