In today’s hyper-connected world, we’re generating increasing amounts of data. The speed at which this is taking place has given rise to the growing field of data science and its various tools. Businesses large and small now rely on data science for increased productivity and efficiency.
According to a report from Indeed, one of the top job websites, there’s been a 29% increase in demand for data scientists year over year and a 344% increase since 2013. Looking at current trends, data science role descriptions are also expanding rapidly. Professionals in this field are increasingly expected to have machine learning and big data technology skills. Overall, data science has been a game changer across many different sectors.
If you’re interested in pursuing a career in data science, you might be wondering what it takes to have a successful career in the field. The first step, of course, is getting hired. This article looks at the role of a data scientist, the different specializations within the field, and the skills needed to get hired.
What Is a Data Scientist?
A data scientist is an analytical expert in handling statistically relevant data, programming languages, decision making, artificial intelligence, and machine learning. Using their business expertise, statistical skills, and overall intelligence, data scientists handle data processing and make appropriate decisions to mitigate threats to business processes.
Data scientists don’t wait for the data to come to their desk—they’re go-getters. They search for data and collect it, cleanse it, come up with predictive models, and assess the business.
What’s the Difference between a Data Analyst and Data Scientist?
Although the terms “data analyst” and “data scientist” sometimes overlap with each other, there are some key distinctions between the two roles.
A data analyst:
- works only when data is provided;
- is not expected to alter the raw data; and
- can get by with little to no programming skills.
A data scientist:
- sources data themselves;
- can create, amend and utilize raw data; and
- needs to have exceptional programming skills.
Data science is a complex field that incorporates data analytics, but also includes AI and machine learning.
Still confused? Get a rundown on the two roles in our article – Data Scientist vs. Data Analyst: What’s the Difference?
Basic Skills Required for a Data Scientist
Being a data scientist is all about learning how to use various different tools and programming languages, and applying them to solve key issues in a business system. A data analyst should have strong technical skills and be self-motivated. Below is a brief guide to the most sought-after skills for data science professionals.
- Interpersonal Skills
If you decide to pursue a career in data science, you won’t have to worry about being stuck at your desk doing a mundane job. It’s a dynamic role requiring communication and collaboration with various stakeholders. This flow is only possible if you can combine your data science expertise with good interpersonal skills. An individual in this role should know how to collaborate cross-functionally to deliver successful business models and predictions.
Albert Einstein once said, “My only genius talent is inquisitiveness”. Perhaps this is true for many data scientists, too. Innovation only happens when you question something. In data science, even trivial self-questioning leads to great insights, which might help you in developing a great product or brand. So it’s important to ask questions at all stages of your work to develop a great strategy.
If you want to excel in the fast-changing world of data science, then you’ll have to keep learning new skills and picking up the latest technology. Cultivate a sense of curiosity and motivation to learn.
This might not be a skill you expected to develop in order to break into data science, but it’s a valuable one.
Great storytelling is what establishes connections and evokes emotions. Even scripted stories have the power to create bonds and elicit interest from the audience. A data scientist doesn’t need to spin any fake stories, but the way they relay their findings and present facts and data should relate to a broader business story. They need to effectively condense the essence of the data into a narrative that’s easy to understand. This will provide other stakeholders with a clear idea of what decisions are to be made. So the realm of data science is full of great storytellers.
4. A Mind for Numbers
Raw data contains lots of numbers, and data science tools break them down into patterns. So you’ll need to get comfortable with math.
In a data science career, you’ll have to apply statistical formulas to manipulate or make decisions out of the data.
In contrast, data analysts aren’t required to know a great deal of math, they should know basic statistics—with an emphasis on descriptive stats and some probability.
5. Master Logician
Being at the intersection of business, algorithms, infographics, graphs, and data coming from countless collection points, a data scientist needs to be extremely good at testing and validating data logically. It’s almost like connecting dots or a jigsaw puzzle. In a data science career, you’ll need to acquire a lot of specialized knowledge and still apply the principles of basic logic.
Technical Skills Required for a Data Scientist
Data science is a combination of multiple fields (including programming, biostatistics, and economics) as well as various scientific techniques. Let’s explore them in a little more detail to get to know the nuances.
- Machine Learning
Machine learning is a growing subspecialty within data science, so you’ll have to Humans have brains to engrave their memories onto. Certain repetitive processes are done with the help of these memory paths. Although machines have memory devices in them, they can’t use them on their own to make decisions.
What if machines were taught to behave like humans? Machine learning is a relatively new phenomenon that represents data science’s answer to this age-old hypothetical. With the help of machine learning, computers can themselves fetch data and make manipulations independently.
2. Advanced Statistical Analysis
Statistics is a branch of mathematics applied extensively in the field of data science. Complex business problems can be solved using statistics. It can’t be used as a standalone subject—it’s used with a combination of tools (Tableau and Power BI) and programming languages (STATA, R, and Python). If you need to brush up on your stats skills, there are a range of statistics bootcamps available online.
3. Programming Skills
A data scientist must be well-versed in programming. Python and R are the two programming languages preferred in the field of data science. Having hands-on experience in these two programming languages can make the task of analyzing and manipulating data much simpler and easier.
Climb The Corporate Ladder with Data Skills
Now that we’ve covered the basic skills and technical capabilities required to become a data scientist, let’s move on to how to land the job and climb the corporate ladder.
Entering the Field
Getting hired as a data scientist with no prior experience is possible, but you’ll need to invest some time in a specialized data science course. That’s because it’s one of the senior-most positions in the corporate tech setting. Start your career as a business analyst or data analyst and move your way up the ladder to become a data scientist. This will also see you gain crucial experience and an enormous amount of knowledge.
Since many companies can’t afford to hire a lot of full-time employees, it’s advisable to take an internship in an organization of your choice. Most interns will use this opportunity as a stepping stone into a full-time role. The internship period provide lots of opportunities and on the job training. It’s become the new normal for entering the corporate world.
Networking is just as important in data science as it is in any other discipline. And even if you're meeting those connections virtually, getting out there and chatting about data challenges is a pivotal part of growing your career.
Maintaining a stellar LinkedIn profile of your professional achievements and pitching in on message boards will bring you a step closer to the recruiter’s office. It’s also good to find a mentor who can help you navigate through your career path.
Begin a Career as a Data Scientist
With technology gradually taking over the world, data scientists are essential for managing businesses effectively. Any organization harnessing the power of data needs a talented data scientist to advise on important business decisions and predict results.
As the demand for data scientists continues to grow, those interested in pursuing this rewarding career path need access to the appropriate study options to steer their knowledge in the right direction. A great data science course will delve deep into the origins of the field and assign the most relevant coursework to enrich your knowledge pool.
For those looking for a comprehensive data science course, our Data Science bootcamp offers an accelerated online program of classes, mentorship, and professional guidance designed to get you a career in data science, fast. If you need to hold down a job at the same time, you can study part-time, with the exact same rigorous curriculum. Talk to an adviser today to find the best pathway for you.