Data Scientists Are the Way To Go in the Tech-Driven World

The omnipresence of artificial intelligence and internet connectivity facilitates businesses the unparalleled potential to gain value, learn, and uncover novel solutions for real-world issues. But how is this achieved? Skilled data scientists can transform such prospects into real possibilities.

Data scientists ride the IT and business worlds alike because they are both a computer scientist and a mathematician who use their skills to transform advanced technology and churn gigabytes of data into actionable business insights. It is through their efforts that data can be transformed into real possibilities.

One such excellent big data science business model is from, which collects data from mobile users to review their feelings. By leveraging machine learning, artificial intelligence, and the collected data, therapists and psychiatrists can offer high behavioral healthcare treatments.

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What Are Data Scientists?

Jeff Hammerbacher and DJ Patil neologized this immensely trendy term.

A data scientist collects, analyzes, and interprets massive chunks of data using machine learning and predictive modeling. Their foremost challenge is to recognize the most crucial business problems and their solutions.

For instance, data scientists at Tendril – an energy software company, implemented a hybrid approach by merging collaborative and content filtering to offer analytics, energy product recommendation, and consumer-based solutions.

So, as you can see, these guys can decipher complicated data problems with their rich expertise in specific scientific disciplines related to statistics, mathematics, and computer science.

What Does a Data Scientist Do?

A data scientist might do the following tasks on a day-to-day basis:

  • Find patterns and trends in datasets to uncover insights.
  • Create algorithms and data models to forecast outcomes.
  • Use machine learning techniques to improve the quality of data or product offerings.
  • Communicate recommendations to other teams and senior staff.
  • Deploy data tools such as Python, R, SAS, or SQL in data analysis.
  • Stay on top of innovations in the data science field.

Responsibilities & Job Titles of Data Scientists

A data scientist’s principal responsibility starts with data collection and ends with business decisions, which is in short called data analysis. They collaborate with subject matter experts to create, maintain, and analyze data to deliver business insights. By recognizing data analytics problems, they can provide the most significant opportunities for the company.

Data science process performed by the data scientists. Credit:

Typical career opportunities available in the data science field are:

Data Scientists

They are usually expert-level, experienced professionals who can make data-driven business analytics and solutions by collaborating with other functional teams and using statistical modeling. A data scientist uses predictive modeling to improve customer experiences, marketing, sales, revenue generation, and much more.

From Netflix recommendations and Facebook targeted ads to optimizing shipping routes – almost every industry involves data science experts.

Data Analysts

These professionals use statistical techniques to maintain databases, classify data, and analyze results. A data analyst helps management prioritize business needs by creating various reports and dashboards that provide hindsight into the business. So, sharp quantitative and technical skills with strong business aptitude are necessary to be a data analyst.

Data analysts add value to the company by making data easy to understand that supports companies in decision-making. For example, data analytics at Amazon Fresh and Whole Foods analyze buyers and suppliers for further improvement in the sale process.

Data Analysts vs Data Scientists

Data analysts and data scientists do similar work — they both find trends or patterns in data to reveal new ways for organisations to make informed decisions. However, data scientists tend to have more responsibilities and are thus considered more senior than data analysts.

While data analysts generally support teams with an already defined goal, data scientists are expected to formulate their own questions about the data and spend more time creating models and using machine learning to find and analyse it.

Data Engineers

They assemble complex and large data sets to solve problems and drive business value by creating robust infrastructure and applications. Data engineers make data pipelines and switch them from one system to another to ensure data is available for a data scientist for further assessment. Hence, they are in constant contact with data science professionals.

Developing end-to-end data engineering pipelines, data preparation, data integration, cataloging, streaming, and masking for faster insights are their core duties.

How to Become a Data Scientist?


Famous data scientist Drew Conway drew this diagram on data science known as the Conway Venn Diagram. Data science consolidates maths/statistics, technical skills (like hacking coding), and substantive expertise (i.e., business acumen).

Here are the data scientist qualification criteria to become a highly-skilled professional.

Education & Certification

Employers generally like to see some academic credentials to ensure that you have to know-how to tackle a data science job, though it is not required.

That said, having a bachelor’s or advanced degree in mathematics, data science, statistics, or computer science will be immensely useful in getting a leg up in the field.

Several universities globally, such as the Institute for Advanced Analytics at North Carolina State University, recognized that employers today want data scientists who can serve as programmers and team players. Hence, data science got its origin in academics as universities revised some programs to bridge this industry’s skill-gap.

North Carolina State University. Credit:

Nowadays, numerous certification opportunities are also available, such as:

  • Microsoft MCSE Data Management and Analytics
  • Dell EMC DECA-DS
  • University of California, Berkeley: Machine Learning, Statistics for Data Science, Deep Learning in the Cloud and at the Edge
  • Coursera: Data Science: Foundations using R Specialization, Johns Hopkins University
  • Coursera: IBM Data Science Professional Certificate
  • Datacamp: Data Scientist with Python
  • Youtube: Sentdex by Harrison Kinsley
  • Youtube: Python Programmer by Giles McMullen-Klein

Technical Skills

Here are some of the relevant technical skills you’ll want to have under your belt:

  • Advanced programming: Covers demonstrated experience in languages like Python, Scala, and R to handle complex data analysis and ML algorithms.
  • Data visualisation:Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with tools such as Tableau, PowerBI, and Excel should help you prepare.
  • Statistical analysis: Statistical analysis skills include in-depth knowledge of multivariable calculus, linear algebra, and statistical techniques to comprehend data and drive insights.
  • Machine learning:Incorporating machine learning and deep learning into your work as a data scientist will improve the quality of the data you gather and may even help you predict the outcomes of future datasets.
  • Big data tools: Learn big data visualization tools and frameworks to work on big data like Tableau, Plotly, QlikView, Hadoop, Hive, Pig, and others.
  • Cloud computing: Get hands-on experience on cloud platforms data tools and cloud computing knowledge, including Windows Azure, Amazon Web Services, Google Cloud, or IBM Cloud.
  • Communication:The ability to share ideas and information with others verbally and in writing is a vital skill in data science.

Business Acumen

Lastly, a strong industry understanding is also essential for useful business insights. So, along with data analysis, a data science professional must also prioritize business to identify problems. This way, they can understand the core goals, needs, limitations, and datasets of an organization to simplify the decision-making process and suggest relevant solutions.

Scope of Data Scientist Jobs

Graph showing an increase in data scientist postings. Credit:

Did you know that the data scientist profession topped the list of “50 Best Jobs in America” by Glassdoor continuously from 2016 to 2019?

The tremendous growth of data has changed how data analytics is performed. Traditional BI tools are incompetent to analyze the massive and unstructured datasets. Smarter and more advanced analytical tools for collecting and processing data became the need of the hour as they took over outdated BI tools.

This whole process is moreover possible only with the help of data science professionals. The dominant capability to collect data from the physical world is one of the driving elements of the ever-growing demand for data science professionals. Harvard Business Review even acclaimed a “Data Scientist’s job to be the Sexiest Job of the 21st century.”

By employing data scientists, many long-standing companies can sustain and grow in the period of invariably emerging technologies. For instance, data science experts at Philips implemented an array of machine learning methods to enhance its performance and the healthcare industry.

Philips uses data science and AI technologies in various segments, including marketing, sales, IT, supply chain, HR, finance, and others. One such use case is its ML-powered OncoSignal, which helps diagnose the type of cancer and its predicted therapy response by mapping signaling pathway movement in tumor samples.

Even IBM proclaimed that the data science segment would grow exponentially by 364,000 vacancies in the U.S. As more and more companies keep adopting Big Data, ML, and AI, the scope for skilled data scientist jobs will undoubtedly amplify.

Thus, the Data Scientist job title will evolve hugely to introduce a plethora of diverse roles. Since big data, ML, and AI keep developing, job prospects will also have to transform to cater to the progressive learning curve of Data Science.

Expected Salary

A data scientist’s salary depends on various factors, including education, job title, experience, industry, and region. In the United States, the average salary in 2020 is $113,309 per year, as per Glassdoor. The lowest pay is estimated at around $83K and the highest at $154K annually.

It won’t come as a surprise that giant tech corporations offer the highest salary in this profession. Below is the average data scientist salary package offered at these high-profile companies as per the Glassdoor report:

  • PayPal: $145,000
  • Apple: $144,960
  • Twitter: $146,000
  • Google: $144,960
  • Microsoft: $130,068
  • Facebook: $113,156

Furthermore, O’Reilly’s 2016 Data Science Salary Survey reported that experience plays a significant role in estimating their salary. It also implies data scientists can get an increment of $2,000 – $2,500 on average every year.

Advantages of Working as a Data Scientist

As one of the most demanded jobs today, it’s no wonder that there are so many advantages to working as a data scientist.

Here are some of the benefits of getting a data science career:

  • A highly valuable job: Since data science is a high-demand job, you will have many career options working as a data scientist. Losing your job won’t be too much of an issue, as data science and analytics are essential in running many different careers today.
  • High salary: As mentioned above, being a data scientist pays well, as many industries and businesses are in high demand for data science experts.
  • Offers various career opportunities: Working as a data scientist will give you a chance to work in different industries, with endless possibilities for career advancement.
  • You can work with big companies: One of the best perks of being a data scientist is that you have the chance to work with some of the biggest companies and brands on Earth, including Amazon, Apple, Microsoft and many more. Landing a job in some of the world’s biggest companies is a great way to advance your career.

Which Industries Need Data Scientists

Whether a financial institution aims to analyze its creditworthiness of its customers before granting loans, or an agricultural scientist aspires to gauge and compare the rate of increase in the current year’s wheat yield to last year, or an e-store decision to offer deals to its loyal shoppers – data scientists are the necessities in every field to process a significant amount of structured and unstructured data.

Hence, the emergence of data science has generated job opportunities in diverse industries for aspiring professionals. Let’s have a look at the sectors where data scientists are in high demand


The volume of customer data, website traffic, and accommodation information requires data science experts to process massive amounts of data. By using demographic analytics to examine bounce rates from travel websites, a data scientist can optimise search results for the visitors.

For example, Airbnb found out that its users from specific regions would click the neighbourhood link and browse the page but leave without booking. So, as a test, the company replaced neighbourhood links with the top travel destinations for those regions’ users. It ultimately led to a 10% improvement in the lift rate. Besides, Airbnb offers better results to its customers by optimizing their search engine, enabling them to locate the right hosts.

The data science process at Airbnb. Credit:


Several organizations, such as Danske Bank, leveraged AI and predictive models for fraud detection in payments and customer information. Data scientists apply their quantitative knowledge and use algorithms like clustering and classification to identify frauds faster. This system decreases the costs of manual human hours and boosts the possibility of reclaiming stolen dollars from false claims.


The retail industry can leverage data analytics to build hyper-personalized shopping experiences to suffice consumers and make better purchase decisions. Personalization gives a competitive edge to the retailers.

Furthermore, Augmented Reality (AR), Virtual Reality (VR), and advanced chatbots are revolutionizing the whole product marketing scene in the retail sector. Renowned fashion brands like Dior and Gucci have already set their foot in this cutting-edge trend. Soon, you will get a personalized customer experience that incorporates interactive demos and live simulations.

Customer having VR experience at Dior studio. Credit:

Is Data Science a Safe Career?

Despite fears of automation taking over data science, various studies have proven its stability as a career.

Here are 3 reasons why data science is a stable career choice:

  1. Automation empowers data scientists to do more. It might take over some areas, but this transition will also open up new career opportunities.
  2. Automation might provide better and faster work than humans, but it’s also prone to error. As such, data scientists serve a vital role in verifying data and making sure automated devices are working properly.
  3. Automation can’t perform human judgements, thus, data scientists play an important role in making certain decisions.

Is Data Scientist an IT Job?

Data science and information technology (IT) use many of the same equipment and software, including programming languages, data mining programmes, and cloud computing infrastructure. The issue becomes more confusing with data scientists working in IT roles. Generally, however, data scientists are required to be well-versed in mathematical skills such as statistics and machine learning.

Here are some distinctions between data science and IT:

  1. Primary goals:
    • Data science aims to draw conclusions, patterns and useful information from data.
    • IT professionals, on the other hand, are more focused on the administration, maintenance, and security of technology infrastructure
  2. Skill sets:
    • Data scientists require expertise in statistics, mathematics, programming, and domain-specific knowledge.
    • IT professionals are skilled in network administration, system maintenance, cybersecurity, and database management.
  3. Process:
    • The entire data analysis process, from data collection and cleaning to model creation and interpretation, is conducted by data scientists.
    • IT experts are primarily responsible for ensuring that network, software and hardware systems are running smoothly through system optimization, maintenance, and troubleshooting
  4. Business applications
    • Data science is utilised in a wide variety of industries, including healthcare, banking, and retail.
    • Although IT is commonplace and even essential in many businesses, it primarily manages technological infrastructure and support.

Bottom Line

The data science field is continuously evolving, and data scientists have all of the resources needed to kickstart their careers. This profession will continue to see high demand in almost every sector, from dating apps, social media channels, e-commerce, and healthcare to government departments. With millions of private and public organisations alike depending on big data scientists to thrive and better serve their users, this profession isn’t slowing down anytime soon.

A data science career may be in your cards if you are passionate about utilising data to obtain unexpected insights or get curious about analysing human behaviour.