Back in university during my undergraduate studies in Mathematics and Education, I remember spending a proportionately large amount of my private studies in the library rummaging through literature in Philosophy, Anthropology, and Psychology. Despite being in the Mathematics classes I found myself entrenched and intrigued a lot by the humanities discipline. Such was my curiosity that I remember presenting a research paper to an international audience in South Africa at the tender age of 19 years.

The titled Proclivities of Giftedness and Talentedness and Ramifications on Behavior and Academic Performance was widely acclaimed and earned me an interview on Prime television the South African State Broadcaster SABC. Reflecting back on this event that happened over 20 years ago, am made to realize how I was being prepared for the realms of Data Science as explained in this article.

Data scientists are a fresh breed of analytical data experts who have the curiosity to explore what problems need to be solved and the technical skills required to solve those problems. They utilize their skills in both social science and technology to find trends and manage data.

These analytical experts use industry knowledge, contextual understanding, and skepticism of existing assumptions, to uncover solutions to different challenges. A data scientists work typically involves making sense of disorganized, unstructured data, from different sources such as emails, smart devices, and social media feeds, that don’t come out well into a database.

Before starting your journey in the data science field, there are important matters that you are supposed to be well informed about. In this article, I will discuss a couple of things you should know even before you enter the field. I will also share my standard guidance with those students who are interested in pursuing data science courses. I have no doubt that these tips are worthwhile.

What You Should Know:

You are an expert in your profession

Data scientists make the most impact when they combine research science and business analyst roles. Mixing deep domain knowledge with the right statistical and engineering tools results in better decisions. I have noted that the majority of data scientists concentrate more in the research scientist direction than the business analyst one. They tend to use elaborate methods then under-invest in learning about their domain. They attend machine learning conferences, but rarely attend conferences on, say, advertising or finance. The majority of data scientists don’t even realize that they have a domain. Any team with accumulated knowledge about what works and doesn’t, has domain knowledge, and you can learn about it from your business partners or by networking with professionals from other companies. Knowing your domain is half the battle, you should consequently invest your time there, the same way you did for your hard skills.

Data science is an ambiguous term

The data science field has gradually been splitting into more specific job titles, such as data analysts, data engineer, machine learning engineer, among others. There is a high likelihood of the specialization process to accelerate in the future. Data science covers almost any quantitative work, that’s why you will find data scientists in the same department doing totally different types of work.

Thus, when you’re applying for data science jobs, try to first understand what the specific relevant definition of data science is for that situation, and make sure that it matches yours. Figuring out the deliverable you’ll be responsible for is better than reading actual job descriptions, as they are written to attract a broad range of candidates for a role rather than really detail what the job will entail. You should therefore find out what the job entails, like will you be creating data pipelines or you will be producing analyses of offline data. To get to learn how ambiguous data science is, watch our orientation for a recent cohort

Critical thinking is central

The central part of any job that involves knowledge, is determining what is useful from the rest. If one does a perfect analysis and it turns out that their insight wasn’t actionable, it won’t matter. It’s very vital to spend time thinking about the broader context of your work. What are the most important challenges on your team, and why? Is your current strategy the best way to help your team, or should you change your plan? The answers to these questions can vary each time, its therefore important to check in regularly. Inertia can make a data scientist to march down a path for too long.

Get comfortable with what you are good at

There is an endless number of data science coding languages , frameworks, and tools. When you haven’t worked as a data scientist before, it feels like you have to know all of them to be a real data scientist. Anytime you are in a conversation and you hear someone mention a tool you are not familiar with; you should not freak out. Fortunately, you can safely ignore 99% of the data science tools out there since your company will eventually have its own set of tools. Everyone at the company will be an expert in using those tools and be completely clueless about most of the others. Again, no good organization will care if you’ve used their particular set of tools before. Unless you’re going for a really specialized role, they’ll expect you to learn their stack on the job. All you need is to know enough that will enable you to pass an interview. You should get a small set of tools that work for you, get comfortable with them, and avoid worrying about branching out too much until you get that job.

Find a way of making data useful

A meaningful part of the data science job is navigating it, as there are always going to be things you don’t know. Since the field is poorly defined, there is a massive number of topics that could possibly fall under the definition of ‘data science.’ Information from different platforms makes one feel like they need to be world-class at every skill to be a data scientist. However, this is not true as all you need to do in order to be a great data scientist is finding a way of making data useful. It’s normal to feel impostor syndrome but don’t let it get you down. You should instead embrace situations where you have something new to learn as growth opportunities.

Analyze the elementary tools well

As a data scientist, you don’t have to know every tool. Don’t pretend to be familiar with all tools as you will end up stressing yourself. You should instead focus more on those basic tools you use daily. For instance, if your company uses python programming language, try to really understand pandas, NumPy, Seaborn, and Matplotlib. Lastly, if you find yourself using something regularly, take some time to read its manual .

What You Should Do:

Study broadly

Despite statistics and computer science being considered as the most crucial courses for data scientist, many other courses are helpful as well. Any subject or course that lets you exercise critical thinking and make written arguments, such as history, philosophy, knowledge management, or English, can be useful since that’s a lot of what you do in data science. Social science courses such as psychology or economics can be useful for gaining experience making causal inferences. As a student, you can take your fair share of technical classes, but learn broadly and follow your interests. Click here to get more knowledge on data science.

Practice with real data 

The best way to prepare for a data scientist job is to use real data for answering real questions. This is because: it’s the closest you can get to an actual job without actually having one. You can find something you’re interested in and get your own data. Kaggle is great for learning about modeling. However, with it, the hardest part has already been done for you: That is collecting, cleaning, and defining the problem to be solved with that data.

One can use packages like Beautiful Soup, Scrapy, and rvest to scrap data off the Internet. If you need inspiration then you can use Reddit and Wikipedia. However, the best choice is something that you’re genuinely excited about exploring. You can then ask some questions that interest you and see how well you can answer them. Clean the data, make some graphs and models, and then write up your conclusions somewhere public. Try solving actual real world problems for people in your country, such as doing statistics work for vehicles using a specific route on a Monday morning or homes with no electricity connection in a rural area, in order to get practice with stakeholder management as well.

Publish your work

Feedback helps us to get better at anything we do with including data work. You can post notebooks to personal websites or GitHub. If you write about a topic your networks are interested in, their response teaches you a lot. You will able to get answers to questions like: Was your presentation convincing ? Were you able to persuade them of your main argument? Did they get bored reading and not make it to the end?. In this case, make your code available, and try to get code reviews from other students in order for you to grow. If you use a technique from a class you’re taking, you could even show your lecturers or trainers what you’ve done and get some expert feedback while showing some initiative. And, who knows, you might get yourself a job through one of your analyses.

Strategize your Job Search

Data science is a competitive field since there are a limited number of organizations with great data science brands, and the battle for their internships and entry-level roles is fierce. However, once you gain real data science work experience, its easier to get a second job.Data scientists with a few years under their belts, even from small companies often have less trouble getting hired at top companies. Therefore, if you want to be a data scientist, and you don’t get an offer from the big companies, you should change your strategy by broadening your job search. A lot of companies have interesting problems that need your solution.

Practice communication skills

Communication skills is very important despite being undervalued in data science. Your impact to people is highly affected by the way you persuade them to make decisions or help build products based on your analyses. Thus, a lot of very technical data scientists’ careers are implicitly affected since they are not good at written and verbal communication.  It is of high importance that you practice written, visual and verbal forms of communication for you to be able to make a real difference. If you feel you are poor in English, you can register for some classes in order to improve on your presentations.

Network with other professionals

Try to interact with the outside data science world while you’re a student. There are data science meetups in most major cities, and in my experience, most people are very friendly to students who attend these meetups. Conferences are usually organized by different professional bodies and universities where they give discounted tickets and accommodation to students. You can also join different coding competitions that will allow you to learn from your competitors while building your skills at the same time. Networking is an important part of your data science career growth as it will give you a better understanding of the realities of the field. Click http://info.predictiveanalytics.co.ke/ to interact with fellow data scientists.

What Next?

Choosing a certified training center like  Predictive Analytics Lab
that offers training and mentorship in data science and analytics is an important first step to becoming a great data scientist.

While most data scientists have backgrounds as statisticians, computer engineers or data analysts, others come from non-technical fields such as psychology and economics. It’s interesting to note that, problem-solving skills and the ability to communicate well, and an insatiable curiosity about how things work, have enabled professionals from diverse backgrounds to end up in the same field of data science.

 If you need to connect with other data scientists or find a mentor to walk you through this profession, reach us through Data Science Events .Our team of data scientists will give you insider information into what data scientists do and where to find the best data science jobs.