A data scientist explains…
To be a data scientist, you need tech/programming skills, some business skills, math skills and statistics skills. But what math and statistics skills do you really need to learn? Today in this article, I am going to share with you three important math and statistics skills that you must master if you want to be a good data scientist. So, keep reading.
1. Descriptive Statistics
You cannot skip this. I have never worked on any data analytics project without looking at descriptive statistics of the data first (irrespective of the data analytics tool). Because this is the original idea of handling data.
When countries collect census data, they are interested in knowing averages and variations in the data. Alright, that looks complicated – I know. Here is the thing. When you are handling any kind of data, you are interested in the mean, the standard deviation, range etc. This in statistics is what we call descriptive statistics. And it is number one concept that you should always learn if you want to be a rational data scientist. Descriptive statistics basically helps you to describe data. It is your tool to derive insights and tell stories from data.
2. Probability
When you are investigating data, you want to find trends and patterns in the data. Governments and businesses are interested in data so that they can project what the future might look like using the previous (or present) data. And because governments and businesses depend on data scientists to look at their data, probability is an important concept for every data scientist.
Therefore, probability is no longer a concept for statisticians and mathematicians only. In fact, probability is now the main engine behind artificial intelligence. To be friendly, it is what is driving the machine learning industry. And if you want to be a data scientist that commands respect in the today’s fast growing tech and business environment – you must learn probability concepts.
3. Basic Math Operations
I know this sounds crazy, but – Basic math operations is the most important skill for data science. Think of addition, subtraction, multiplication, division. Programs that you engineer and automate to do your desired solutions are always a product of simple additions, subtraction, multiplication and division. That’s why the original idea of programming starts with simple math.
As a data scientist (or an aspiring one), you must have heard of the concepts of regression. When you break it down further, you require an understanding of basic math operation to understand the concept of regression. Other advanced math concepts that require basic math operations are topics like calculus, analytical geometry etc. These are concepts that you will not always be dealing with when you are a beginner. However, as you handle different and more advanced data science projects, you will come across these concepts . Most machine learning projects require these concepts.
Note this however
You will not learn all these skills once. Math is a wide and huge field. So is statistics – as well as programming. What is going to build you is working on as many and different projects as possible. When you work on different projects, there is always a new programming or statistics or math concept that you interact with. If you come across a new concept that you have never met before, go and do your research and learn on that concept. When this becomes a habit, you find that you have learnt a lot by working on the projects.
What’s your experience learning any of these skills for data science? Share in the comment section below.
If you are new to programming and data analytics, I recommend checking out these articles:
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