Here’s How to Make Your Data Science Skills Future-Ready During the Pandemic
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:
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.
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.
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
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.
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.
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.
