Introduction to Data Science Skills
Data Science, ah such a beautiful profession in the words and deeds of those who love to do the job! As an important point to every job, love for the profession is essential. To love the job, one should have the necessary skills to do the same, whether inbuilt or acquired. We have seen many business people who acquire the business from their family and build it into an empire. And other business strata, who prepare themselves to face the worst, acquire the skills and become the best in the slot. Now, let’s see data science skills.
Types of Data Science Skills
Following are the Types of Data Science Skills:
1. Technical Skills
How many of us have hated mathematics as a subject during our school days? Almost all of us right. Here I am going to tell you a heartbreaking revelation. Mathematics is very much important for data science, be it statistics, probability or algebra or whatever. Statistics show us that the data which we collected has a pattern or not. It makes us say that there should be a mean and variation for every data. Probability shows us the future of data, whether it is going to happen or not. Also, it says about the past as well.
Linear algebra is the basis of data science as data revolves around functions and equations. Also, we could get vectors and matrices from data, which is a crucial part of linear algebra. If you want to be a master in data science, you must know how linear algebra works. Start loving mathematics, and it will take you to great heights.
2. Programming Skills
Gone are the days where statisticians worked with pen and paper or with a calculator to analyze a company’s sales or benchmark the competitor company’s sales. Now we could do all these things with programming, not all these but more than these. We could see how far the data takes us in the long run, whether the data was consistent in the past and how we are doing in the present.
The best programming languages that work for data science in Python and R programming language. If you learn Python once, there is no turning back to other programming languages because Python is straightforward and simple. Consider two people talking to each other in a language known to both of them. And when needed, drawing sketches to show exactly what one meant. That’s what we are doing with Python. No header files interactions for the programs. For the problems which you feel complicated, there are assigned libraries to do the job for you. Import them and consider it is done. R programming language is said to be for those who don’t know the programme at all. But believe me; it is easy than you think. R is mostly used when you need more sketches. It is good to know both the hand of the language in hand, but one language can take you to a higher level in the beginning.
3. Visualization Skills
When we read the newspaper, we skim and skip the most important news, but the ones we read are mostly sketches. It is a human notion to see anything and to be registered about the same in mind. So is visualization skill indispensable in Data science? I would answer it with a big Yes. The entire data of maybe 100 pages can be minimized to two or three graphs or plots. Don’t you feel it be cool? I feel so.
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To draw the graphs, one must visualize the patterns of the data. So are there some tools which help us to do so? I am glad to say yes to this question as well. Excel is a great tool which draws the necessary charts and graphs based on our need. Some other tools for data visualization include Tableau, Infogram, and Datawrapper and so on. There are many tools to help us when we are lost in the big sea of data. Big or small, data is essential for us to draw into our conclusions and present to our management. What else could a data visualization tool do rather than helping us to do the charts?
4. Communication Skills
It is paramount to convey our findings either to a group of teammates or senior management. Communication helps us to reach a level higher than what we really fight for. Being a good communicator helps us share our ideas and to find discrepancies, if any, in the data. Presentation skill is most important in a project to showcase data findings and plan the future. Looking at each other’s eyes to convey a message is important during the presentation.
However, there is a trend to avoid this skill while preparing to be in data science. Folks, this is not the last skill to be acquired but a skill to be walked through while going through other skills. After doing the mathematics calculations, it looks beautiful if the problem is ended with a blowing summary. While programming, it is advised to add comments between codes so that those who go through the code understand it better. Visualization tools get a completion touch only when it is decorated with proper titles and given proper explanations. Hence written and verbal skills are unavoidable in data science.
Conclusion
So did I miss any skill to be acquired so that you can be in the field of data science? Analytical skills are equally important though I haven’t stressed it because mathematics covers all those hot topics. Curiosity about data and leadership skills to make the team work together makes you great in data science. I want to conclude this writing by saying that no skills are underrated. And all the skills can be acquired to become a professional data scientist. Hard work to focus on what you are doing; a little patience to do data cleaning is not to be avoided in the long run.
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This is a guide to Data Science Skills. Here we discuss the introduction and different types of data science skills you need to acquire to become a professional data scientist. You can also go through our other related articles to learn more-
