Mathematics: An essential skill for aspiring data science professional!
Strong foundation in mathematics is essential for data science professional seeking an entry into the data science industry.
“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
As Josh Wills — a data scientist once said that a data scientist is a person who is better at statistics than any programmer/software engineer and better at any programming/software engineering than any statistician. It becomes quite clear that statistics is one of the core skills required for a data science professional.
Understand this — amid other skills that a data scientist should possess in order to succeed in the highly competitive 🔗data science industry — a strong foundation in mathematics is a must.
Reason?
A solid background in mathematics would help the aspiring data science professionals to not only learn the existing and new algorithms of machine learning but would also help in segregating themselves from the crowd of aspiring data scientists.
Result: You need to master linear algebra, calculus, probability, and statistics. In fact, if you search on the internet on how much mathematics would you need for data science you would be surprised to see the result. The result includes — Linear algebra, statistics and calculus.
No doubt proficiency in mathematics is essential for data science professional, it is important to note that statistics is one of the fields that you need to focus on more if you wish to make it big in the data science industry.
Are you thinking about all the disinterest you showed in mathematics in your school years? Yes, then know this that while you could still become a data scientist but without the foundation in mathematics you would not be able to fine tune data science models.
There are numerous computational tools available to a data science professional that would help to perform their task better. Sorry to disappoint you, because despite all the computational tools available you still need to have strong base in math.
A simple reason for that is — without a solid foundation in mathematics you won’t be able to ask any core analytical questions — something that is essential to all data science projects. Talking about the core mathematical skills that you would need include — Linear Algebra, Calculus, and Statistics.
In fact, while the other two are also important, it is the third skill that you need to master and become a pro at. You should be aware about the two main categories of statistics that are descriptive and inferential statistics.
👉Descriptive Statistics is the category of statistics that uses the data to provide descriptions of the population either through numerical calculations or graphs and tables.
On the other hand, inferential statistics makes inferences and predictions about a population that is based on a sample data taken from the population in question. This type of statistics generalizes a large data set and applies probability to arrive at a conclusion.
👉Coming to Calculus — does the prospect of relearning calculus makes you break into cold sweat? Yes, don’t worry, because as a data science professional, you just need to strengthen the basic principles of calculus and you would be good to go.
👉Linear Algebra is another branch of mathematics that an 🔗aspiring data science professional needs to master.
Remember: When you are doing data science, your machine would use linear algebra to perform numerous calculations efficiently. For instance: when you perform a Principal Component Analysis to reduce the dimensionality of the given data, you would use linear algebra.
Now that you know the importance of mathematics as one of the most important skills for a data science professional to master, it is also important to know that you could boost your skills by upgrading and upskilling.
Why? The data science industry is booming and there is a demand for skilled data science professionals. So while linear algebra, statistics, and calculus — the branches of mathematics are important for a data science professional, it is imperative to know that in order to market yourself to the right employer you need to keep the learning curve ever-growing.
How do you do that? Through certification from the top credible certification bodies. This would be another challenge, because to select the 🔗best data science certifications could be a tedious task. So when choosing data science certifications remember it should be vendor-neutral, and platform-agnostic.
