The job market is always changing. At the moment, demand for data-savvy individuals in tech has never been higher. In fact, overall demand for data scientists has increased an incredible 344 percent since 2013. Rapid advancements in AI, big data, and other technologies driving up demand means that the talent pool for data professionals can’t grow quickly enough to keep up with the needs of the tech industry.
As time goes on, data scientists and professionals who manage, test, and create software will play a major role in driving the future of tech and the economy as a whole.
In order to ensure that we have enough qualified applicants to meet future demand, it’s important for data scientists to learn certain key skills—namely, Python and statistics.
Though employers look for a wide range of skills and knowledge when hiring for data science, these key abilities are some of the most universal assets that tech professionals can bring to the table. Let’s take a look at why.
The Makings of a Software Program
For most people, getting into data science starts with the most basic of tech skills: programming. Code is at the core of every software program and it’s required for everything from making a beautiful website to executing big data functions.
Data scientists are typically expected to know one or more programming languages and have in-depth knowledge of statistics in order to set up the necessary software and analyze the data properly.
Yet just a foundational understanding often isn’t enough. Tech pros will need to continually sharpen and hone their understanding of Python and statistics to stay up to date with developments in software and technology. This is where online learning through resources like Udemy’s Statistics library come in handy. Through just a single course, employees can supplement their knowledge and stay up to date on the latest developments.
The Importance of Python for Data Scientists
It can be overwhelming to decide what skills to build as a data scientist. One of the reasons that qualified candidates are so difficult to find in this field is that they are expected to have both a wide breadth of knowledge and in-depth skills and abilities.
There are so many programming languages, platforms, and frameworks out there, and it’s just about impossible to learn them all. That’s why standardizing the tools that data scientists use to some degree could make a big difference in the future of tech job growth.
One tool that all data scientists should strive to learn is the Python programming language. It’s the most in-demand skill for data scientists among employers, and for good reason. It’s relatively basic and easy to learn while still providing impressive versatility and functionality.
It’s also easy to maintain, open source, and offers a range of libraries. Python is now the standard language in data science, and anyone entering this field should strive to gain in-depth knowledge in using it.
Solving Everyday Problems
In addition to learning the tools of the trade like Python, data scientists need to be experts in analysis and problem-solving. Statistics is a key skill for data science, as this mathematical discipline uses large datasets to spot trends.
Though not synonymous with the field of data science, statistics plays an important role in problem-solving and analysis in the field.
When working with big data, it’s important for tech professionals to know how to look at everyday problems on a macro and micro level. Applied statistics are used in every industry to gain insights that drive strategic action.
Data scientists don’t just seek to understand data—they have to collect and analyze the numbers before communicating their findings, and then use those findings to come up with creative solutions to problems.
For example, agencies like the CDC use statistics to find where and how infectious diseases are spread so they can more effectively control them. Politicians use applied statistics in fundraising, to target potential donors who are most likely to give.
There are endless applications for statistics in data science, and an in-depth knowledge of this field is absolutely crucial for data professionals.
Gearing up for Success
If you’re interested in breaking into the growing field of data science, then it’s important to focus your time and energy on the core skills employers are looking for.
While many people who get into the field do not have a degree in data science specifically, most have a master’s degree and in-depth knowledge in computer science and programming, statistics, communication, and machine learning.
You can increase your hire-ability by learning Python, becoming proficient in applied statistics and by keeping up with trends in the industry. To really thrive in today’s job market and become a successful data scientist, you need to look into the future and build skills that won’t be out of style next year.