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Live Master Class

Getting Data Science Jobs During COVID-19


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About this session

This LIVE session will help you understand why should one pursue Data Science? How did Runav make transition from Sales to Data Science during COVID-19 Pandemic? Importance of Domain in Data Science and some tips on how to grow/learn in the field of Data Science. You will also understand the key challenges he faced during job search in the COVID-19 Pandemic. Types of interview rounds for a data science role and how to network with people in the Industry? What roles should you choose based on your profile? Join in to know answers for many more questions on how to pursue your career in Analytics.

About the Speaker

Mr. Runav Desai

Executive - Data Scientist, Nielsen


Certified Data Science Professional from Great Learning with an MBA in Marketing and Bachelors in Computer Engineering. Previously worked at Info Edge India Ltd. ( in Corporate Sales, now placed at Nielsen as Executive - Data Scientist.

What is Data Science?

Data science involves gathering, editing, saving and processing data. It encourages information solutions to decision-making, thereby promoting a sustainable growth culture. Data science, covers analytics, artificial learning, data processing, and computational science, incorporating many more fields. If you're new to data science, this may be overwhelming, but bear in mind that multiple positions and industries can prioritise certain talents and skills over others, so you don't have to be an expert at everything. For lots of various types of expertise, we have  data scientists.

It is feasible to use data science to construct a better system for tomorrow. From enhancing diagnosis precision in the medical sector to changing treatment with wearable devices, revolutionary applications may solve a number of important problems. Data science will also help farmers and food producers raise productivity, reduce organic waste, and potentially gain more.

One aspect is a Data Science job description, and the real position which is another aspect. You can see these more basic positions illustrated in pointers, depending on the organization.

One significant bit of wisdom for your career hunt is to closely read job descriptions for data science. This would encourage you to apply to positions for which you are already eligible, or to build new sets of data skills to fit the positions you choose to perform. 

How Much Math Do You Need to Become a Data Scientist?

Data scientists are apparently highly trained and have at least a master's degree, and many of them have PhDs, a very good educational experience is typically needed to establish the level of expertise needed to be a data scientist. If you are interested to know types of data science jobs then scroll down.

Data Science Jobs

  1. Machine Learning Engineer

Usually, machine learning engineers require good statistics and programming abilities, as well as software development skills. They are also responsible for conducting testing and trials to track the performance and functionality of such applications, in addition to developing and constructing machine learning systems.

Today, machine learning engineers are in great demand. The job profile comes with its obstacles, though. Machine learning engineers are also required to conduct A/B testing, develop data pipelines and incorporate basic machine learning algorithms such as classification, clustering in addition to providing in-depth awareness of technologies like REST APIs, Sql, etc.

     2.Applications Architect

Applications Architect monitors the behaviour of software used in an organisation and how they deal with each other or with customers. Architects of software are also based on developing the framework architecture, and constructing elements such as the user interface and infrastructure.

     3. Enterprise Architect

An enterprise architect is accountable for integrating the vision of a company with the technologies necessary to achieve its goals. To do just that, they have to have a thorough understanding of the demands of the enterprise and its technologies in order to develop the layout and the structures used to satisfy those needs.

    4. Data Scientist

Using data mining and data modeling, data scientists have to consider the problems of industry and provide the innovative strategy.  For instance, to have valuable intelligence, they are required to conduct predictive analysis and run a fine-toothed comb through "unstructured data (unorganized data). They will also achieve this by recognising relationships and correlations that can help enterprises make smart choices.

Data scientists are much more technically sound in contrast to data analysts.

   5. Data Architect

Facilitate rapid strategies for multiple networks are designed for efficiency and design research applications.

A data engineer designs data processing blueprints such that with the appropriate protection procedures, the databases can be conveniently incorporated, centralised, and secured.

Career in data architecture needs skills in  data modelling, data storage, ETL, etc. In Spark, Hive and Pig you will have to be well versed.


Statisticians work to compile, evaluate and classify data in order to detect patterns and interactions that can be used to guide corporate decision-making. In addition, the day-to-day duties of statisticians also include designing data collection procedures, presenting conclusions to clients, and consulting on an operational plan.

They not only collect and provide useful information from data clusters, but also help to develop innovative techniques for engineers to implement. A statistician must have a zeal for logic.

    7. business analysts 

The position of market analysts varies marginally from that of other data science workers. Although they have a strong understanding of how data-driven systems operate and how to manage massive data volumes, they often distinguish high-value data from low-value data. In other words, they describe how Big Data can be connected to workable market insights for business development.

Market analysts serve as a liaison between data engineers and managers.

    8. Infrastructure Architect

Ensure that all enterprise processes run optimally and can accommodate the advancement of emerging technology and system specifications. A related job description is Cloud Architecture Architect, which manages the cloud storage policy of an organisation.


Is data science a good career?

You can feel frazzled and frustrated just thinking about the career and its choices.

Don’t worry I’ve got you covered if you want to know about “is data science a good career”. Before you jump on that, question yourself about why you are interested in this career and could you handle some cons that you are going to face whilst working as a data scientist, if yes then scroll down. 

Reasons for Data Science being a good career

1) As data science is in great demand right now there are many openings for prospective job seekers. On Linkedin, it is one of the fastest growing career. Data Science is therefore a highly employable field of careers.

2)  Data Science is one of the most well-paying jobs in the world.  Data scientists earn an average of rupees 85,26,732 a year according to Glassdoor. This makes Data Science a very good career opportunity.

3) Data Science has many uses. It is commonly used in the sectors of health care, finance, consulting services and e-commerce. Data Science is an area that is very scalable. You would, however have the ability to work in multiple areas.

4) Data scientists enable organisations to make better business choices. Companies focus on data scientists to use their experience to give their consumers improved outcomes. This offers a major role in the market to Data Scientists.

5) Data Science has enabled the outsourcing of repetitive functions by multiple areas. In order to execute routine operations, businesses use historical evidence to train computers. This has eased the arduous tasks traditionally worked out by humans.


Do data scientists work from home?

With the COVID-19 shutdowns, though working from home has become a modern standard, many firms recruiting data scientists have long-standing flexible work practises and attractive incentives for their workers.

Data scientists are currently among the best occupations that can be conducted remotely.

In my view, when all the work occurs on your machine or on a distributed system that you can control directly, you can work completely from home or remotely as a data scientist. 

Cases when working from home is likely:

1. Start-ups with inadequate office room for all workers or seeking to keep expenses down during bootstrapping. 

2. Big businesses (such as IBM) that have a global presence and innovative technology help their employees to do this.

3. When someone recruits you as a freelancer or consultant, your success assessment is connected to a very well specified production and timetable.

4. Big businesses that have a global presence and innovative technology help their employees to do this.


Is data science a stressful job?

Often stress and work go hand in hand. It can be quite tricky to completely get over stress at work. Generally speaking, a traditional data scientist will work on simulation, analysis, report generation, and certain other processes about 16-24 hours a week.

Below are some examples that will give you a glimpse of how stress can arise through various tasks.


  1. Collecting Data - You need to gather data before you can delve into the most exciting facets of the work, and this move can be especially stressful. The Data Scientist may have to negotiate with many product departments to get the necessary approvals in order to even access the data.  

       2. Data scientists are expected to consider many different viewpoints from many audiences and it can be difficult to explain the impact of the research to them.   

      3.Data scientists need to invest a lot of time, particularly when working to tackle a major problem. But in the last few decades, the sector has become quite competitive and the sheer intensity of competition can be overwhelming.

4.  Data scientists usually focus on data for a whole organisation, which ensures that they dig for thousands of transactions at once. Therefore, it can be pretty stressful.

Can data scientists become CEO?

To this question, the straight forward answer will be that it depends. There are openings, though very low, as CEO roles are still limited. But if you belong to the data background, it would be a benefit. CEOs should also have a particular range of qualities, such as teamwork, financial intuition, common sense, creative analysis, communication, etc. If you believe most of them are covered by you, and you can create them with time, go for it. I have stated below some data scientists that have turned into CEOs.


Data Scientists-Turned-CEOs

The founder of edtech's startup Udacity is Sebastian Thrun, who is also the founder of Google X. 

 Brad Peters, who developed Birst, a business intelligence startup. Brad led analytics at Siebel Framework prior to beginning his own company.

Since its establishment in 1976, Jim Goodnight, the CEO of SAS, the world's leading business analytics software provider, has led the organisation.

Thomas Thurston is Growth Science's founder and CEO, who uses information to determine whether enterprises can succeed or fail.

Instead of selecting a well-paid job in a big corporation, these data scientists moved into CEO positions, supporting most effective data start-ups.


What is future of data science?

The size of big data is absolutely incredible, and in crucial areas of personal and corporate life, it has already interconnected itself. Consumers are getting more conscious of their entitlement to data protection and data preferences, while enterprises have massively utilised that information.

Big data is primed in the future to play an important part. In governance, energy and engineering, modern medical care, banking, corporate administration and marketing, data is profoundly involved. In order to face the demands of data analytics and help reinvent enhancements in products, services and culture, professional talent would be necessary across these sectors. Implying how data scientists are going to be in demand.

According to an article on LinkedIn, data scientist positions have risen more than 650 percent since 2012. The work has since become one of the most in-demand positions, ranking No. 2 among all the top new occupations behind machine learning engineer.

Do data scientists code?

Yes, they do code for the most part or we can say they are capable of coding but it solely depends on the work profile. The software they use, how much they code, will ultimately depend on the position they undertake.

There are 3 types of code that data scientists usually write:


  1. Production code. 
  2. Analysis scripts
  3. Prototypes. 

How much math is enough to become a Data Scientist? 

Have you always contemplated a data science profession but been overwhelmed by the demands of math? Although data science is based on a lot of mathematics, it might be less than you expect the amount of math needed to become a professional data scientist.

Therefore, focus on the parts where you are required to depending on the space you are going to work in.

The most used math concepts would be:

  1. Probability and statistics
  2. Linear algebra
  3. Calculus
  4. Graph theory
  5. Discrete math


Degrees required to become a data scientist

As a beginner data scientist, you would need at least a bachelor's degree in data science or computer-related field to get your foot in the door, while most jobs in data science would require a master's degree. Degrees also comprise your resume structure, internships, networking, and acknowledged academic credentials. However, if you have earned a bachelor's degree in a particular area, through online short courses or trainings, you can need to concentrate on learning the skills necessary for the job. All things considering, if you have a Master's degree, it is easier to break into Data Science.


 How to Become a Data Scientist from Scratch

Below is the complete learning journey guide that outlines all the skills, experience and preparation you need to become a data scientist from scratch:

Measures from scratch to become a Data Scientist 

  1. Gain Qualifications

First of all, you're going to need some professional credentials. The most popular path is to prepare for an undergraduate or postgraduate degree.    You can study for a degree in Mathematics and Analytics, Computer Science or Engineering to learn more of the expertise and knowledge necessary for the work. From the total 88percent of data scientists hold a master's degree, and 46percent have a Doctorate degree.

Instead, as there is a lack of data analysts, yet more organisations are taking in individuals who do not have educational achievements. Instead in a related role (computer programmer, engineer), you'll need to have a decent amount of expertise or be able to show good mathematics and coding skills. You're still going to need to take several advanced classes.

Now a  days, one may find fully accredited online courses which are taught by data science specialists. Online  learning sites have been the ideal way to develop specialized expertise at an inexpensive price, and as the number 1 way to develop in-depth knowledge and abilities, they are overtaking traditional educational institutions.

2. Skill and Knowledge Development

  • You'll need to be able to show unique abilities and expert experience, as well as credentials.
  • Many individuals are seeking a master's degree in data science, but there are other avenues to gain the required expertise, such as e-learning courses. You will need to know, depending on the demands of the role:
  • to use SQL
  • Machine Learning/AI Experience
  • Code in a language like Python or C#
  • Hadoop experience or related platforms
  • Software or frameworks such as d3.js or tableau display and view info.

3. Gain Work Experience

It's a smart thing to get some job experience through your studies and afterwards.

For any number of organisations that require data scientists, you might be fortunate enough to find paying jobs. These companies work in all economic fields, including banking, retail, construction, technology, etc. If you're looking to find job experience, non-profit and charitable organisations are a decent place to look, but you will have to compromise for unpaid labour.

As part of the programme, another way to obtain useful knowledge in the area of data science is to participate in classes that offer seminars.

There are too many types of specialist ventures to mention in full depth, but here are a few suggestions:

  • Machine Learning – Different degrees of difficulty of machine learning projects exists. Stick to linear and logistic regression ventures as a Data Scientist fresher, as they are optimal. These types of projects are also used to create templates for management to view knowledge and share insights.
  • Creating Immersive Data Visualizations-This kind of project would suit you if you love viewing data in creative and fascinating ways. You'll need some sort of dashboard software, for example Dash b Map, to generate visualisations of data insight for organisations.
  • Brushing Data- Regular maintenance, reshaping and archiving of databases would be needed for massive, complicated database systems. Projects for cleaning the data require a clear understanding of Python or R.
  • Exploratory Data Analysis (EDA)-It includes analysing the information, asking specific questions about it that may reveal market insights, then using SQL, Python or other programming language to address the questions.


Building a career portfolio that involves a few different forms of popular ventures is beneficial, so don't be shy to begin by testing out a few different areas of expertise. Unless you're not certain which field of expertise to concentrate on first, this is particularly valid.

  1. Specialized E-learning Classes in Data Science

To become a professional and effective Data Scientist, you must learn specialist skills. Your skills and talents will need to be constantly refreshed and updated.


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