Data science is the interdisciplinary study of many fields and all things related to data. It is a hodgepodge of statistics, math, computer science, business intelligence to name a few. Data science is a relatively new term but the function of a data scientist has existed even before the job was officially created. Check out our blog on how to become a data scientist. Parts of the jobs were handled by different people, who specialized in one field. But now, it is one person who is expected to have the skill and perform the job of what used to be two or three different roles.
This means that the skills that you have to learn to be a data scientist have become that much more selective and numerous. In this article, we will discuss the skills that make a great data scientist. If you are looking for guidance, checkout our data science for beginners article.
Data scientist job was awarded the title of the “Sexiest Job of the 21st Century”. This alone will tell you how popular is the job of a data scientist. Data scientist and related roles involving big data, data mining and data architects are all seeing increasing traction. The data scientist is popular and sought after job role by companies who are willing to hire the right candidate with a good salary. Of the candidates,
Those who display keen interest and knowledge in data science and machine learning, and data science vs artificial intelligence tend to be paid more for their selective skill set. Similarly, there is a demand for a data scientist who is well versed in any of the trending and top programming languages. This has made python a very valuable skill in the job market for data science. So take a python online course and get upskilled to get better and improving job prospects as a data scientist. According to a report, 'The Quant crunch', published by burning glass, BHEF and IBM, there is expected to be a demand surge of 28% for the position of a data scientist.
Data scientist gets paid the best salary compared to the other roles involving data science. In general, the data scientist is a sought after position. There is a huge market demand for a data scientist across various industries as companies from different fields are getting into data-driven decision making. Of course, every industry has a different level of data science involvement for their products.
Therefore, a data scientist who works in an industry where there is a greater dependency on data science will obviously be better paid than the others. Education also plays a major role in deciding the salary of a data scientist. In recent times, many data scientists have been known to have a masters or a PhD degree and some universities have also started offering data science as a speciality. These graduates do tend to be paid more. The other highly paid data scientists will be those with expertise and experience in business intelligence and other data-oriented roles. Another way to get a well-paid position of a data scientist is to gain knowledge through MOOCS and getting certified with an online course on data science.
To have a successful career as a data scientist, you should have the skills that will be discussed in this article. This list of skills is the minimum basic requirement, Every additional skill that you may possess will take you closer to being more in demand and better paid. The job market has undergone a lot of changes in recent times with tech advancements in machine learning and artificial intelligence. So, to be an awesome data scientist you need to upskill and equip yourself with more skills along with the growth of the data science field.
Non-technical skills There is a set of non-technical skills that are equally important for a person to consider becoming a data scientist. A few of them are general skills that every job requires and some are specific to data science. Once you have a knowledge of the non-technical skills and you decide that data science is the job for you, you can go about learning the technical skills that the data scientist job needs.
Aptitude for data consumption
Less a skill but equally important is your aptitude for data. You should be able to handle an extraordinary amount of data every day. This is so not a career for those who keep on the lookout for different challenges every day. This is more for people who work towards perfection and are also at the same time ready to take risks and make a smarter decision. Every decision you make will again be evaluated with the data and continuous updates are done to a campaign.
Every industry is dependent on data science. But each industry is its own domain with its business model, and requirements. So, it is not enough to have core skills as a data scientist, you should also have knowledge of how things work in the domain you are applying for as a data scientist. This is in-fact a must so that the data scientist will know which data is more relevant and what will work for the industry and what will not work.
Intellectual curiosity and innovation
Healthy amount of intellectual curiosity is a very much needed skill to make the cut as a data scientist. You should be able to spot patterns and trends in seemingly unrelated batches of data. You also need some creativity to come up with visualization and ideas to explain the results to other executives who may not be familiar with data science. You need to make them understand trends and opportunities in simple terms. Curiosity and innovation play a major role when it is time for predicting trends and is useful in making tools that make your life easier.
Communication, both verbal and written is essential to become a data scientist. Many times, data science is a mixture of a job profile that requires a loner and other times, it needs solid teamwork. You should be adaptable and be comfortable working solo or with a team.
You should move well with the team and also be innovative and communicative enough to find interesting ways to present your findings to the non-technical people in your company. You should be able to explain the trends or patterns, give recommendations and suggestions for performance improvement. You should display leadership skills and facilitate meetings and also be comfortable to mentor other juniors going forward.
The data scientist in generals has a master's degree or a PhD. Nowadays, there are degrees in Data science also being offered in bachelors and masters. But if that's not the way you are considering becoming a data scientist, then you have a few other options. You can gain knowledge on the subject from MOOC sites. Or, you can do a course on data science at the Crampete learning centre which will give you a certification that you have gained the skills that make you a data scientist. Crampete also offers classroom-type classes in data science at their learning centre. The technical skills you can easily learn with some perseverance and hard work.
Another needed advantage to be a good data scientist is an analytical mind. You have to make sense of large amounts of different data, and analyze it to get some insights from it. Other than curiosity which will open you to new avenues, you need strong analytical skills to be able to use those avenues to analyze the data and extract information from it. Analytical skills also include assuring the accuracy of the data, creating predictive models, critical thinking, research, data mining, so on and so forth.
Maths and statistics
Mathematics and statistics along with business intelligence are the core skills for data science. Multi-variable calculus, linear algebra, statistical test, maximum likelihood estimator and others are great to be thorough at. Though there are tools that give you out of the box solution for most things, the knowledge of these subjects will help you improve on the prediction where even a small change will produce drastic results. They will also be a handy skill to have in case there is any in-house development happening.
There are a lot of programming languages out there and different blogs will say which is the best language to learn. As such, there is no 'best' programming language. There are the trending ones, that are widely used in the 'now', and there are others. Different languages also have different characteristics. For example, R works for pure data science, and python is great for general purpose. To learn a few programming languages to cover your bases.
If you have knowledge of various programming languages, you can easily hop between the tools that are readily available. See how data science works with python. If you are interested in learning python course online, Crampete offers a few of the trending programming languages courses.
You should have a working knowledge of various tools of data science that are used for a multitude of purposes. There are tools available for making the work of a data scientist easy. This includes tools for data cleaning, mining, analyzing, and even for data visualization. Each job and industry may require a different set of tools but it is easy to learn if your language skill is solid. Data is growing and frameworks and tools for data science are being created everyday to make the process easier on the data scientists. You can choose to work on a set of tools that are popular and should always keep yourself updated on the latest tools and trends
You should learn SQL and some SQL based databases to work with structured data. SQL based relational databases are the best option available for storing structured data. This type of data has to be collected, stored and analyzed with a distinct set of tools separate from the unstructured data. The structured data is considered synonymous to relation DBMS and this is unequivocally managed by SQL. So data scientist should be familiar with working in SQL to be able to work with structured data.
Learn to work with a few NoSQL databases to manage unstructured data. The data collected on the internet today is mostly unstructured data. And this type of data has, well, no structure. You need a different set of tools to collect, store and analyze unstructured data. The NoSQL databases work with different approaches that do not work like the traditional RDBMS and is useful to handle this type of data.
These are desirable skills and knowledge of algorithms of ML and Ai pertaining to data science will help data scientists a lot, especially in predictive analysis. These are the hot topics of learning in 2019 and is expected to be making advancements in leaps and bounds in the next few years. So getting on top of this and keeping yourself updated on its development will open new horizons to you.
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