Data science is quickly becoming one of the most sought-after skills by employers of all sorts. Businesses, government organizations, medical institutions, and nonprofits – the number of data scientist positions is growing at an impressive rate.
In fact, the latest research says that:
- 67 percent of companies are expanding their data science teams
- A data scientist is ranked top three jobs of 2020
- The shortage of data scientists in 2020 is estimated to be 250,000.
Would you like to join the workforce or continue building your data science skills? Now, with data scientists being in high demand, the resources to learn the craft are plenty.
This post describes four ways to build your competency quickly, based on the most essential skills and qualities for data scientists to make your data science skills future-ready
1. Knowledge of AI, Machine Learning, and Deep Learning
Very soon, process automation will take over a lot of human work. To get there, though, devices and software tools guiding them must be intelligent and autonomous. That’s where AI, machine learning (ML), and deep learning (DL) come in.
Deep learning is a subfield of machine learning, which in turn is a part of AI. One can say that ML and DL is an implementation of AI, so whenever data scientists use it, they use all three.
Tips for learning AI, ML, and DL:
Enroll in an Online Course
The cheapest and reliable way to learn is online. Before choosing a course, make sure that it covers the areas you’re interested in as well as the skill level.
– Deep Learning Specialization (Coursera, intermediate level). A great course on AI, ML, and DL offered by top instructions on the platform
– Google AI courses (Beginner, Intermediate levels). Courses and tutorials designed by experts at Google
– Machine learning (Coursera, intermediate level). One of the most popular ML courses offered by the University of Washington, taken by 117,000+ learners
Write Several Algorithms for Yourself
The algorithms are a major part of online courses, but write a few for yourself. You can improve them later as your knowledge improves, which would be a useful exercise.
Base each algorithm on a specific value, or a way to apply it in business. This “how-to” kind of thinking is essential for developing as a value-driven data scientist.
Pro tip for data science skills
Consider paying for a certificate.
If an online course you finished has this option, get a certificate to prove your skills. This one-time investment can make a difference when looking for jobs later and can make your data science skills future-proof
Related: The ODSC Introduction to Machine Learning (tutorials and videos).
2. Coding Skills
The knowledge of programming languages commonly used in data science – Python, Java, Octave, Scala, R, C++, and others – gives a major advantage in the labor market. They are needed for working with statistical models, decision trees, working with real-time data, cloud computing, and many others.
Python in particular is highly valued by expert data scientists. It’s the preferred choice for most daily tasks they do because it has packages tailored for useful functions, access to data analysis libraries, and plenty of data visualization options.
One of the important use cases of Python right now is COVID-19 research analysis. In this study, for example, they studied extensive data models to help medical researchers with forecasting outbreaks and inform preparations.
Some of the Python code used in this study has been open-sourced, so you can check it out, too.
Python is easier to learn compared to most languages mentioned here. So, you can start by taking an online course to figure it out.
Here are great courses for getting your coding skills up to date:
– Introduction to Data Science in Python (intermediate level). A top-rated course from the University of Michigan
– Statistical Analysis with R for Public Health Specialization (beginner level). A course designed to give you practical skills to apply data analysis method through R in medical research
As someone who finds undiscovered patterns in data, a data scientist needs to be able to communicate their findings effectively.
Here are the most important communication skills to learn:
- Active listening. Research is a major part of a data scientist’s job, and often it involves interacting with different stakeholders. By practicing active listening – the art of repeating back what the other person said in your own words – you’ll be able to engage in the conversation more effectively.
- Business writing. Very few people have the natural ability to write clearly. “Practice concise and on-point writing,” recommends Dan Simmons, a technology writer at BestWritingAdvisor. “Also, consider the knowledge of the people who will read your report, don’t use programming terms and complicated grammar.”
- Presentation skills. Even though this might not be your primary work responsibility, at some point you’ll have to give presentations to colleagues and stakeholders. Practice by giving summaries of your research in simple terms.
A successful data scientist is someone who can interact with partners, colleagues, and the public, discuss results, and get feedback. So, don’t just focus on hard skills in your training.
Check out these courses to get a sense of that you need to learn:
– Routine Communication in Data Analysis (Coursera). Teaches managing the process of data analysis and communicating throughout
– Data Analysis and Presentation Skills: the PwC Approach Specialization (Coursera, beginner level). Learn how to turn data into real-world outcomes and create effective presentations.
4. Critical Thinking Data Science Skills
Okay, now let’s talk about the critical soft skills. Data scientists need to think objectively before jumping to conclusions and making any kind of judgments.
In fact, there are two aspects showing how important critical skills are.
First, a professional data scientist should be able to develop a relevant research question that resembles a real problem. To do so, he or she needs to have a good understanding of that problem, which is something they achieve through extensive research.
A lack of critical skills automatically translates into an inability to achieve research goals and express the real problem properly.
Second, a data scientist must be able to question the data they have. Instead of just dumping raw data into a software tool and generating results, they need to work on removing potentially misleading, irrelevant, or distracting data or features.
Clearly, this would be an extremely hard task for someone with poor critical thinking skills.
Here are some online course recommendations for learning:
– Think Again: How to Reason Deductively (Coursera, beginner level). A top-rated course whose 25 percent of participants reported acquiring a tangible skill for their career
– Creative Thinking: Techniques and Tools for Success (Coursera, beginner level). The course teaches to think creatively and innovatively, offered by top educators on the platform.
Conclusion on making your data science skills future proof
Data is the new oil, they say.
Already, data science has become a major part of the effort to study a wide range of issues and phenomena. With the outbreak of COVID-19, the profession has proven its incredible value, and will continue to play an important role in helping us understand data and making it useful.
Melanie Sovann is a writer specializing in marketing and technology. A journalist by training, she has worked for popular blogs and content agencies over her 7-year career. Now she blogs for business and offers help with essay-style articles, complex guides, and lead magnets. When she isn’t writing, you can find her riding a bike or reading books for Millennials.