How to Become a Data Scientist?

9 min readApr 19, 2021

~ A Complete Career Path

Are you someone who is crazy like me in exploring Data Science and finally wants to fulfill your dream of becoming a data scientist? If so, then you have chosen the right link to fulfill your dream. In this particular blog, we will learn the basic fundamental steps that are needed to follow to become a successful data scientist.

Introduction

Data science has always maintained the same reputation since its discovery from the years back. Few decades back, Harvard University Review titled Data Science and its respective domains as the sexiest job to work with in the 21st century. And to our huge surprise, till now it has represented one of the fastest growing technology and most profitable career paths for the upcoming generation. When conceiving about what it costs to become a data scientist, it often seems a tough task to unburden the various forms of complex and analytical problems that data scientists need to deal with in their work schedule. By profession, the duty of a data scientist is to clean and interpret massive amounts of unstructured, unorganized data keeping in mind the goal to discover opportunities or solve problems for a detailed, in-depth solution.

Almost all companies and enterprises hire data scientists today for a myriad of crucial reasons, some of which include to help them garner actionable insights from big data, or discovering product or user experience gaps, or sometimes analyzing potential growth opportunities, developing a greater understanding of customer pain points. It’s really a matter of concern to understand how do Data scientists do your job? Normally, they utilize various data visualization tools to help draw, formulate, and present conclusions or trends they identify in their day-to-day work.

How Data Science as an Career

There are different paths which one can follow and excel in this career. So if you are thinking about what to study and how to start approaching the desired subjects to become a data scientist, then there are a few different options which we will discuss in our upcoming sections.

From years back, we have observed that Data scientists have traditionally come from backgrounds possessing good technical skills in programming and statistics. And yes, mathematics is one of the most important aspects required to become a data scientist, because the trend speaks that most data science job roles require a good knowledge of statistics and linear algebra above all other required subjects.

But that doesn’t mean you need to be a pro in programming languages to excel in Data Science. Basic understanding and experience will help you much. There are many cases where people from various backgrounds such as an individual with an undergraduate degree in foreign language and literature are working as a Data Engineer with 1.5 years of intense self study.

To draw a conclusion, we can see there are many paths to a successful career in data science. Bit in every practical sense, it is completely impossible to start your first step in data science without a good college education.

Coming to the next important section is that Data science should not be considered having the same concept as statistics. Although these two subjects depict the same level of skills and share almost similar goals, they are unique in their own aspect of concept. Data science, which is a recent discovery, is completely based on the wide use of computers and its logical computation. On the other hand, Statistics is based on established theories and focuses more on hypothesis testing rather than computational concepts.

Wrangling Data in a Holistic Approach

The Defining Terms

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Data Science and becoming a Data Scientist

So, what does the term Data Science mean by and who is a Data Scientist basically? For most folks , Data science seems to be a posh and sometimes a quite confusing field, which actually involves dozens of various skills that make defining the data scientist profession an enormous constant struggle. And essentially, a data scientist is someone who gathers and analyzes with the goal of reaching an answer to our daily problems through implementing different techniques or more precisely, machine learning algorithms.

Case Study: Now, let’s look at an industry based example of a data scientist which is in the current scenario. Suppose in a vast laptop selling business, the company wants to know what current customers are more likely to buy so that they can compete with their competitor.

In such a case, they may like to hire a data analyst who possesses the ability to analyze billions of data points or more specifically, and then create an algorithm to look at these related to former customers.
They may draw the conclusion that customers who are using a certain brand of motherboard laptops are more likely to leave their use.
They may also find that customers who are employed in IT sectors and between the ages of 20 and 45 are the most likely to switch to some better brands.
The MNC company will accordingly make changes in their business plan or marketing strategies so that they don’t lose these customers and retain them in the further years.

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Requirements to become a Data Scientist

Academic Requirements: Are you willing to Become a Data Scientist? If you want to have some good degree certificates and develop yourself towards the research domain, then there are three general steps to becoming a data scientist.

First and fore mostly, once you are done with your 10+2 examinations, then it’s the time to earn a bachelor’s degree maybe in IT, computer science, math, physics, or any other related field from some recognized university. The field of Data science requires an advanced level of mathematics understanding. If you have a basic knowledge of programming languages such as SQL, Python, R, then it would really turn out to be an added advantage for better learning.
Once you have completed your graduation, now it’s time to move to an upper level and earn a master’s degree in data science or some of its related fields such as data analysis, data visualization, warehouse. It turns out to be quite beneficial if an individual completes his masters specializing in Machine learning algorithms, AI architecture and Statistics too.
Finally, now the final step has arrived. Learning the basics of identifying various trends with the datasets and then gaining experience in finding better solutions really helps a lot to specialize ourselves in the field that we intend to work in (ex: healthcare, agriculture, physics, business).

Skills Requirements: On following the above steps, you will be officially certified as a Data Scientist. But is it really enough to limit your knowledge only in certificates? A big NO. Because data science is a long journey of learning a checklist of skills and focusing on building various projects around real data. The journey won’t be that tough neither too easy, but it will definitely be more exciting and interesting than moving in the traditional path.

The main motive of a data scientist is that he builds the capability to answer all those complex questions using actual dataset and effective code. These questions can vary anything…to start with “can I predict whether any product will be good enough to buy based on the reviews?” to “how do we detect disease in plants?”. In order to answer such questions, we need to develop a sophisticated system which can possess an analytical mindset along with continuous learning.
Once we’ve learnt all the different approaches to handle such forms of analytical questions, now we are now ready to start learning the technical skills in order to find better answers that suit them. And if you are a beginner, the best way is to start learning data science is by learning the basics of programming languages with the fun of Python preferably.
Once we have started learning the basics of coding, it’s time to start building real-time projects that provide interesting solutions and showcase our data science skills. At the early stage of our career, projects need not be extremely complex. For example, cracking in JEE is a huge competition among students nowadays. So, we can collect past datasets and start analyzing the all India rank holders to find patterns and predict the cutoff for the current year. With the help of such interesting datasets, we can answer questions about the data, such as which subject has the highest weight age or which coaching institute is best.
Done with some good projects? Now, let’s share them with others, maybe by uploading them to GitHub, where others can view and comment for further improvements. Uploading projects benefits a lot such as allowing our peers and mentors to view our projects and comment, opening our mindset to think about how to best present a project, which is what the most important aspect in a data science role.
After we’ve started building some good projects and showcasing them on our CV or GitHub profile, then it’s a good idea to start engaging with other data scientists using some good online data science communities some of which are Kaggle, Quora, Reddit or Dataquest. I personally feel being active on sites such as Quora and Kaggle will surely help immensely because in one way it helps to find other researchers to learn with, it enhances our profile, and find opportunities to strengthen our knowledge by learning from others.
To summarize it, we can say companies want to hire data scientists who are able to find the critical insights that help them save money and make their customers happier. The process of learning involves the same principle. We aim to keep searching for some higher level of questions to answer, and try finding better solutions for those complex questions.

Is Data Science Multidisciplinary?

The word Data Science is a broad term which covers multiple disciplines such as data analytics, machine learning, software engineering, data engineering, predictive analysis. Among all these, the biggest part is dominated by machine learning uses various techniques, such as regression, classification and supervised clustering to solve problems. It mainly focuses on various algorithms and statistics.

The Summary

Data science, being one among the foremost buzzed fields in today’s world , is required by nearly every enterprise, business, organization, and agency within the country and across the world. So there’s certainly extreme demand for data scientists and their specialization. Many data scientists are highly specialized in business, and sometimes to specific domains of the economy like automotive, insurance or business-related fields like marketing and pricing. Some data scientists also get hired for the Department of Defense , specializing within the analysis of threat levels, while others concentrate on helping small startup businesses get older, find and retain customers.

For instance, we may consider a situation where a data scientist may find happiness in helping furniture dealerships analyze their customer choices and make attractive marketing campaigns to draw in more customers for the organizations. Another data scientist may like to specialize to assist large retail chains and help them determine the right price range for his or her products.

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