6 Goals Every Wannabe Data Scientist Should Make for 2019

Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.


Many people who are already working in tech-centric fields realize they want to embark on new paths that result in eventually having careers as data scientists.

That goal in itself is a worthy one, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.


1. Become a Member of a Relevant Organization

Networking with others who are also interested in data science lets people learn about the educational options that exist, understand which tools are most prominent in the data science industry and get encouragement from individuals who were once wannabe data scientists, too.

The Institute for Operations Research and the Management Sciences, or INFORMS, for short, is the largest such international organization and boasts thousands of members. Although the organization has in-person events, there is also a members-only online forum.

However, there may also be local options for aspiring data scientists to explore in their communities. MeetUp.com offers meetings of all kinds around the globe and has more than 5,000 events related to data science.


2. Get Familiar With Emerging Trends and Apply Them to Career Goals

Data science is a fast-moving industry, and the professionals who can best keep pace with the changing landscape are typically those who make conscious efforts to do so. The IoT, open-source tools and predictive analytics are among the trends likely to be prominent in 2019.

Instead of merely learning about the trends and staying abreast of the latest news about them, people seeking to become data scientists need to examine how they could apply those trends to their career goals.

For example, a person could explore new open-source data science software and start working with it as soon as possible to become acquainted with how it works. Or, it could be useful to take an online course about the fundamentals of predictive analytics and learn about why that segment of the field is so crucial to the companies that hire data scientists.


3. Set Specific Goals to Make Progress With Data Science Projects

Many people who are data scientists or want to work in the field have periods where they teach themselves. That means even if people don't have formal data science training yet, they may launch data science projects independently, fueled by curiosity and the desire to improve skills.

People who have their sights set on data science careers should try a particular goal-setting system that teams at Google, Amazon and other notable companies use for their data science projects. It involves coming up with objectives and key results, or OKRs.

The objectives relate to the goal of the project, and the key results express how a person would reach the goal. Also, OKRs are successful if a person meets 70 percent of the key results.

A person could apply OKRs to a data science project by choosing the most meaningful metric associated with it. That metric shapes the objective, and the key results take a deep dive into the processes the individual must go through to make the project fruitful. It's best if each key result has a date associated with it.


4. Consider Getting an Advanced Data Science Degree

If a person wants to increase earning potential in a future data science role, one way to do that is to get an advanced data science degree. Many schools offer Master of Business Administration (MBA) degrees with data science concentrations.

One method of learning more about them and making a shortlist would be to explore at least one school's offerings each weeknight. Taking that approach enables getting details about approximately 20 schools every month, plus allows for gaining knowledge without being overly hasty.

The average salaries for MBA graduates vary depending on things like the chosen concentration and the years of work experience a person has. Since data science skills are in exceptionally high demand, it's likely a data science MBA concentration would make a job candidate stand out from the rest of the field.

Recently compiled statistics show a data science shortage across the U.S. An advanced degree could make a person well-equipped to help fill the gap, plus put them in a position to earn a higher-than-average salary.


5. Improve Data Storytelling Abilities

Finding meaningful information in a collection of data is one necessary skill for a data scientist, but that person must also be an excellent data storyteller. Otherwise, the decision-makers at a business will not be able to grasp why a particular conclusion drawn from the data is valuable. If the audience does not see the insights are sufficiently compelling, they won't make changes.

During 2019, a person could practice communicating their data science results to friends who do not have data science backgrounds. They could ask those people for suggestions to improve.


6. Learn Some New Programming Languages

Data scientists use various programming languages in their work. Learning new ones in 2019 is a proactive move to attain the necessary knowledge to excel in future careers.

If people who want to work in data science don't know any programming languages yet, 2019 is a prime time to expand knowledge. Python is a fast-growing and popular programming language those in the data science field often depend on. It also has an easy-to-understand syntax, making it an excellent first programming language.

R and SQL are a couple of other frequently used languages in data science, making them worth investigation. But, as they work to grow their programming language prowess, people must remember it's more helpful for them to know one or two programming languages exceptionally well, rather than only understand the bare minimum about many others.


Having the Right Mindset Is Crucial

In addition to these goals, it's essential for people to keep themselves motivated even when they experience setbacks.

Developing that dedication to data science work could make future data scientists even greater assets to the companies where they work.

Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.