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How To Become A Data Scientist

How To Become A Data Scientist  

Do you only consider things analytically? Are you always ahead, or want to be ahead, when it comes to the new advancements in technology? Do you seek intellectual stimulation at all times? Are you enamoured by the idea of Big Data?

Data Science might be the right field for you.

What is Data Science?

Data Science is the art of turning disorganised and unstructured data into cohesive, comprehensive and understandable information. Information of this sort not only provides workable insights into the data, but also enables analysis of its applications for the purpose of solving issues pertaining to various fields around the world.

Essentially, Data Science gives us the ability to collect information from a variety of sources, declutter and eliminate the noise, structure and organise the information, and draw out insights that can be used to solve problems related to various spheres of the modern world.

Why Data Science?

Look closely, and you’ll see that in this world where technology is the machine that is constantly evolving, upgrading and becoming smarter, data is what is driving it. Data, the fuel of the modern world, unlike our other fuels, is increasing at an exponential rate on a daily basis. And when data increases, the problems pertaining to it that require modern, targeted solutions, also increase. Therefore, the big firms, across industries, are constantly on the lookout for people who would give them deeper insight into the workings and the behaviour of the data, and make it easier for them to utilise it in their company’s future plans. This is exactly where data scientists come in.

What does a Data Scientist do?

A professional with the innate ability to merge the tools of statistics with a programming code in order to produce targeted and workable insights, is a Data Scientist. A Data Scientist is someone who is responsible for analysing the data collected by them, spotting trends and problems and then interpreting it to suit a company’s requirements. They are professionals who are fluent in technological as well as statistical sciences.

The job of a Data Scientist consists of combining their statistical knowledge with their computer skills, using it to extract information and interpreting them to the company’s advantage.

The key roles and responsibilities of a Data Scientist include

  • collecting the data
  • cleaning the data
  • processing the data
  • combining the data for storage purposes
  • performing analysis on the data
  • using techniques such as machine learning or statistical modelling to make it easy to interpret the data
  • fixing mistakes
  • presenting to the company or the stakeholder.

How Much is a Data Scientist’s Salary?

On an average in India the salary of a Data Scientist is approximately 6,98,000 per annum. Over cities, and with experience it varies across India. As a fresher with zero experience, a Data Scientist can easily earn a package of upto 5,00,000 per annum. It increases with experience and has the capability of touching even 20,00,000 per annum.

Steps To Become A Data Scientist

Now that you know what Data Science is and what it is that a Data Scientist does, here is a roadmap for you on how to become a data scientist in 2022. Everything that you’ll need to know, from where to start to where it leads to, keep reading to find out.

First and foremost, even though anyone can pursue a career in data science, one needs to have a basic knowledge of mathematics, computer science, and statistics. Becoming a data scientist requires a certain level of technical skill and formal training. The following steps will give you a slight idea of how to go on about your career journey.

1. Graduate with a Data Science Related Degree

The basic qualification for Data Scientists in order to get a headstart in data science, or to get an entry access into the field, you would need an undergraduate degree in a subject matter-related field. The best subject options to get an undergraduate degree in if you want to pursue this career are:

  1. Statistics
  2. Mathematics
  3. Computer Science
  4. Economics
  5. Physics
  6. Applied Mathematics

However, if that stage has passed, you can always have a specialisation in a different field and improve upon your data science-related skills through online courses and certifications. These certifications don’t always need a technical background for you to start your Data Science journey.

2. Sharpen Your Skills

Data science requires specific, high-level, technical skills that you would need to hone to start, as well as progress, in this particular field. Certain skills that you are required to work on and master are:

A. Programming Languages:  In order to organise and analyse data, data scientists spend a significant amount of time on programming languages such as Python, SQL, R, SAS, etc. Even though any of these languages would be beneficial for you, learning Python would give you an added advantage as it is one of the most used programming languages in today’s world, along with R.

When learning Python, it is necessary for you to focus on the details of the data types in Python such as Strings, Booleans, Numbers, Lists, Dictionaries, Tuples, for basics. As you keep learning you would need to hone your Python statements, function, recursion, and more. You would also have to understand Objective Oriented Programming, also known as OOPs in Python, to get a further grasp on the language. As you advance to a higher level, you could learn about modules, packages, debugging in Python, and so much more.

You do not need to be an expert, but since Python is a fairly beginner-friendly programming language, it is suggested that you become fluent in it. Learn and become a master in Python and get a certificate in a python programming course.

B. Statistics and Mathematics: Machine Learning is the skill that combines your statistical and technical knowledge. It deals with employing the tools of statistics and probability such as mean, standard deviation, minima or maxima, etc. Linear Models, Regression Models, Probability-based Models, Tree models, or tools such as K-Means, Hypertuning, Clustering, and more are a part of the statistical side of Machine Learning tools that you should be familiar with too. As a data scientist, you would be exposed to tools that would be able to perform all these analyses for you but it is imperative that you yourself have a strong grasp on these subjects.

Furthermore, you are required to be proficient in certain topics of mathematics as well. Calculus and Linear algebra being the major topics that you would require in your everyday life as a data scientist. A programming library that would be of immense help while performing mathematical operations would be NumPy, short for Numerical Python.

C. Machine learning: Now that you’ve perfected your hands on the basic topics of mathematics and statistics required for Data Science, it is time to dive into machine learning. As a data scientist, you would be constantly looking to improve your current data set that allows you to predict future data sets more accurately. This is the work of Machine Learning algorithms.

Machine learning, a sub-part of Artificial Intelligence, can be further divided into supervised and unsupervised. Supervised machine learning is wherein the program is given a labelled input with an expected outcome and runs accordingly, while unsupervised machine learning involves an unspecified target. As a newbie in the data science world, it would be an immense advantage for you to have fluency in Machine learning and Artificial Intelligence.

A Data Scientist would be required to extract from huge datasets and bring out information that is more structured and informative. An important skill thereby would be being able to create a program that allows machines to use texts, or images, etc., to create content. A course in the in-demand skill of machine learning and artificial intelligence would put you a step ahead of the crowd.

D. Data Visualisation: When it comes to data, it can be quite cumbersome just looking at it and trying to interpret it. This is where data visualisation comes in. A visually impressive data is the source of understanding for everyone who isn’t deeply involved in the technical side of things. It is also a foolproof strategy of making sure the correct insight was passed on, and it is definitely far more interesting than reading technical jargon

Dealing with tables, charts and graphs, analysing them, and extracting worthwhile information from them is an important job of a data scientist and therefore it is imperative to have the creativity and the technical knowledge to be able to showcase data in a visually appealing manner.

Getting familiar with tools such as Excel, Tableau, etc. would make it easier for you in the later stages when you need to be able to present data and maintain the interest of the ones being presented with it.

E. Communication skills: It doesn’t end at learning the technicalities of Data Science. As a data scientist, you’ll be required to communicate your findings, share your ideas, be a team member and a team leader. You need great public speaking skills to stay ahead of the competition. Not only should you be able to communicate things regarding the data, you’ll also be expected to motivate and lead your team.

Soft skills like knowing when to speak, what to speak, office etiquettes, body language, following the dress code, everything will matter to make an impression and stick around with the firm. Someone with good communication skills will always be preferred over someone without them. So, go, it’s time to brush up on your soft skills too!

3. Work on Projects

As a newbie, you need to have something that you’ve worked on to showcase a potential employer. In order to do that, and gain some practice and a certain level of experience in your skillset, you need to work on live projects. Start off with simple projects for beginners, a few examples of data science project ideas for you can be found here. Build your skill by constantly challenging yourself by trying on more complex projects and levelling up.

4. Find a Niche

After working on projects and understanding your strengths, and gaining a better understanding of the industry, you’d have a fair idea of the area that you want to specialise in, if you didn’t already. Work towards bettering yourself in that area, gain more experience, develop stronger skills, and you’ll have multiple and better opportunities lining up for you. Keep trying to upskill yourself and find new projects for yourself to work on.

Get Started!

Now that you have all the information, why wait? Get started on your career in Data Science with Board Infinity’s Data Science Online Course with Placement! Understand the basics of data science and build your foundation about topics like Business Analytics with Excel, Data visualisation, storytelling using Tableau, SQL & Maths for Data Science and get certified! All the best!

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