Data Science exists everywhere, to be honest, every exchange and interaction on any technological domain includes a certain set of data, be it Amazon purchases, Facebook/Instagram feed,Paytm, Netflix suggestions or even finger and facial recognition facility provided by phones.

Today in 2021, most companies are adopting a data science strategy to make more revenue by automating different scenarios and replacing dozens of IT people with a single data scientist who can automate the task of those IT people using various automating tools like BluePrism, UI Path, Python and machine learning algorithms.



Amazon is a key example of how data influences all our lives and shoppers particularly. Its data sets store every buyer’s data; what you have bought, the amount paid and your search history is all remembered in Amazon’s system by virtue of data collection. 

This greatly enables Amazon to personalize and customize its homepage according to your preferences and shopping history.Data Science encompasses many breakthrough tech concepts like Artificial Intelligence, Internet of Things, Deep Learning to name a few. With its progress and technological developments, data science’s impact has increased drastically.

We are constantly being faced with unpredictable situations — like the Covid pandemic — which has called for businesses to do what they can to minimize human-to-human contact. Data science and rapidly changing technology have helped drive these changes and prove that a bright future exists. This will, however, depend on the quality and the extent of data that organizations can acquire.


“If you look at the next five years, AI is coming up in a big way across multiple industries. An ideal candidate should know the algorithms, mathematics, code and technical skills. Surely, these are a must. But, in addition to this, candidates should work on their problem-solving skills, develop innovative solutions, and out-of-the-box thinking,” said Sambasivam


Will, there be a shortage of jobs or will there be fewer hiring?

Well, things become easier when we think differently.It is true that companies will keep focusing on the automated workflow of machine learning.

But, remember, no company wants to depend on another company for their work. Each company aims to build their product so that instead of depending on others, they can build their automated system and then sell them in the market to earn more revenue.

So, yes, there will be a need for data scientists who can help industries build automation systems
that can automate the task of machine learning and deep learning.

Why Data Science Will Continue To Be the Most Desirable Job ?

Data science talent shortage

The demand for skilled professionals in the field of data science has grown remarkably owing to the urgent need for strategic decision-making tailored for specific regions.

Data has become the backbone of business decision making.

Data science has proven to be a powerful tool to extract meaningful insights from this large chunk of data. These insights help organizations in determining any prominent changes that are to be made basis the changing consumer behavior, shortcomings of previous solutions, forthcoming challenges and competition analysis.

Shortage of Talented Pool:

While the demand for professionals adept in data science skills is at an all-time high, there is a major demand-supply gap due to the non-availability of skilled talent.

Highly lucrative career

According to Michael Page’s 2021 India Talent Trends report, professionals with 3-10 years of experience receive an annual salary ranging between INR 25-65 lakh and those with more experience can command packages upwards of INR 1 crore.

A large selection of roles within the field

One may choose to opt for a job role based on their interest as well as experience level. Some of the job roles that are high in demand include data scientist, data architect, BI engineer, business analyst, data engineer, database administrator, data, and analytics manager.

Is there really a shortage of data scientists?

Despite an large numbers of people or things arriving suddenlyof junior level candidates, high pay data science skills are still in shortage. The highest-paid Data Scientists have highly specialized skills that set them apart from others in their field. These roles are in high demand but cannot be filled by undergraduates with no experience.

Impact of data sceince on 5G Technology

Low latency and its high speed will immensely benefit data analytics. These features make it possible for analysts to collect, clean, and analyse large volumes of data quickly. This will spur new analytics technologies soon. For example, autonomous cars – earlier autonomous car production was limited and a pipedream because data analytics was restricted by the high latency offered by 2G, 3G, and even 4G. But now, 5G offers low latency, better information processing, and does it in real-time.

One of the most significant opportunities 5G offers analytics is real-time data exchange or insights.


On one hand, Data science centers around data representation and a superior show, while AI zeros in additional on the taking in calculations and gaining from ongoing information and experience.

Continuously recollect – data is the primary concentration for data science and learning is the fundamental concentration for AI and that is the place where the distinction lies.

To see the value in this distinction more, let us take a utilization case and perceive how the two data science and AI can be utilized to accomplish the outcomes we need –

Allow us to say you need to buy a telephone on xyz.com. This is whenever you first are visiting xyz.com and you are perusing telephones, all things considered. You utilize different channels to limit your inclinations and out of the outcomes you get, you pick 4-5 of the telephones and think about those. When you select a telephone model, you will see a proposal beneath the item – for a comparable item in a lesser cost or with more elements, or related adornments for the telephone you have picked, etc. How does the site suggest you these things? It has no set of experiences about you!

That is through the data from a large number of other who might have attempted to buy a similar telephone, and looked/purchased different adornments along. This makes the framework naturally prescribe something similar to you.

The whole course of assortment of information from the clients, clearing and sifting through the necessary data for assessment, assessment of the separated information for building designs, tracking down comparative patterns and building a model for a proposal of exactly the same thing to different clients lastly the streamlining – is information science.

Where is AI in this? Indeed, how would you construct a model? Through AI calculations. In view of the information gathered and drifts produced, the machine comprehends that these are the extras that are typically purchased by different clients with a specific telephone. Henceforth, it recommends you exactly the same thing dependent on what it has ‘encountered’ previously.

A Step-by-Step Guide to Becoming a Data Scientist

Develop the Right Data Skill

You can still become a Data Scientist if you have no prior expertise with data, but you will need to build the necessary background to pursue a data science profession. Data Scientist is a high-level employment, therefore you’ll want to establish a broad base of knowledge in a related field before advancing to that level of specialisation. Mathematics, engineering, statistics, data analysis, programming, or IT are all possibilities; some Data Scientists have even worked in banking and baseball scouting.

However, whatever subject you choose to start with, you should learn the essentials of Python, SQL, and Excel. Working with and organising raw data will necessitate these abilities. It also helps to be familiar with Tableau, a programme you’ll use frequently to build data visualisations.

Skills for data scientist

There is a massive shortage of skilled data scientists! Yes, that’s right! Even though the jobs in the field of data science are seeing growth, there remains a scarcity of data scientists with the right skills.

Fundamentals of Data Science.

The first and foremost important skill you require is to understand the fundamentals of data science, machine learning, and artificial intelligence as a whole.

Statistics and Probability

statistics is an essential concept before you can produce high-quality models. Machine Learning starts out as statistics and then advances. Even the concept of linear regression is an age-old statistical analysis concept. 

The knowledge of the concept of descriptive statistics like mean, median, mode, variance, the standard deviation is a must. Then come the various probability distributions, sample and population, CLT, skewness and kurtosis, inferential statistics – hypothesis testing, confidence intervals, and so on.

Statistics is a MUST concept to become a data scientist.

Analytics and Modeling

Data is only as good as the people performing the analytics and modeling on it, so a skilled Data Scientist is expected to have high proficiency in this area. Based on a foundation of both critical thinking and communication, a Data Scientist should be able to analyze data, run tests, and create models to gather new insights and predict possible outcomes.

 Data Visualization

Data visualization is an art.Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Deep Learning

Deep learning is the most hyped branch of machine learning that uses complex algorithms of deep neural networks that are inspired by the way the human brain works. DL models can draw accurate results from large volumes of input data without being told which data characteristics to look atIn a nutshell, data science represents the entire process of finding meaning in data. Machine learning algorithms are often used to assist in this search because they are capable of learning from data. Deep learning is a sub-field of machine learning but has improved capabilities.

Data Wrangling

Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis.

Intellectual Curiosity

At the heart of the data science role is a deep curiosity to solve problems and find solutions — especially ones that require some out of the box thinking. Data on its own doesn’t mean a whole lot, so a great Data Scientist is fueled by a desire to understand more about what the data is telling them, and how that information can be used on a broader scale. 

Create visualisations and practise presenting them to others.

Practice creating your own visualisations from start using programmes like Tableau, PowerBI, Bokeh, Plotly, or Infogram, and find the best method to let the data speak for itself. Although the underlying assumption of spreadsheets is clear — creating calculations or graphs by correlating the information in their columns – Excel comes into play even during this step.

However, developing attractive visualisations is only the first step. You’ll also need to be able to use these visualisations to convey your findings to a live audience as a Data Scientist. These communication skills may come naturally to you, but if they don’t, know that with practise, everyone can improve. If necessary, start small, giving presentations to a single buddy or even your pet before progressing to a group environment.


Create a portfolio to demonstrate your data science abilities.

Consider showing your work on GitHub in addition to (or instead of) your own website when applying for a Data Scientist post. GitHub allows you to effortlessly display your method, work, and results while also raising your profile on a public network. Don’t stop there, though. Your portfolio is your opportunity to display your communication abilities as well as your ability to do more than simply crunch numbers. Because data science is such a large field, it’s beneficial to demonstrate a number of strategies. There are numerous ways to tackle an issue, and you can bring a variety of ideas to the table.

So that the employer understands your worth, accompany your data with a compelling narrative and demonstrate the problems you’re working to solve. GitHub is a platform that allows you to collaborate with others


Difference between data science and data engineering?

When it comes to data science and data engineering, what’s the difference?
It’s easier to identify the differences between the two disciplines now that you know what a Data Scientist and Data Engineer do on a daily basis. The following are the main distinctions:

Data Scientists prepare data for machine learning and utilise it to develop machine learning models, while Data Engineers collect, move, and transform data into pipelines for Data Scientists.

A data engineering process produces data that is simple to use and process, whereas data science produces reports and insights that are given to business stakeholders.

Data Engineers apply programming languages to transfer, manipulate, and clean data, whereas Data Scientists develop machine learning models using programming languages.

A Comparison between Data Analytics and Data Science

Data science vs. data analytics While both data analysts and data scientists deal with data, there is a significant distinction.
Working in the field of data analytics Data analysts’ responsibilities vary depending on the industry and company, but.
Data Analysts’ Characteristics Data analysts can have a mathematical or statistical background, or they can work in data science. Data scientists, on the other hand, use inquiries, writing, and other methods to estimate the unknown.

Difference Between Data Science and Machine Learning

As previously stated, Data Science systems cover the whole data lifecycle and often include the following components:

Data Science


  • ETL (Extract Transform Load) pipelines and profiling processes are used to collect and profile data.
  • Distributed computing refers to data distribution and processing that is horizontally scalable.
  • Automated ML models for online replies (prediction, suggestions) and fraud detection are being developed.
  • Data Visualization – Visualize data to gain a better understanding of it.
     machine learning models.
  • Dashboards and BI — For higher-level stakeholders, predefined dashboards with slice and dice capability are available.


Data engineering

  • Data engineering entails ensuring that both hot and cold data is continually available.
  • Backup, security, and catastrophe recovery are all covered.
  • Deployment in production mode — Follow industry best practises to migrate the system into production.
  • Automated judgments – This can include applying business logic to data or performing a sophisticated mathematical calculation.

JOB TITLES of Data Scientist

Data Scientist

Let’s start with the most general role, data scientist. Being a data scientist entails, you will deal with all aspects of the project. Starting from the business side to data collecting and analyzing, and finally visualizing and presting.

A data scientist knows a bit of everything; every step of the project, because of that, they can offer better insights on the best solutions for a specific project and uncover patterns and trends. Moreover, they will be in charge of researching and developing new algorithms and approaches.

Data Analyst

Data Analyst

The second most known role is a data analyst. Data scientist and data analysis and somewhat sometimes overlapped a company will hire you, and you will be called a “data scientist” when most of the job you will be doing is data analytics.

Data analysts are responsible for different tasks such as visualizing, transforming, and manipulating the data. Sometimes they are also responsible for web analytics tracking and A/B testing analysis.

Data engineers

Data engineers are in charge of creating, constructing, and managing data pipelines. They must test business ecosystems and make them ready for data scientists to run their algorithms.
Data engineers also work on batch processing of collected data and ensuring that it is formatted correctly for storage. In other words, they ensure that the data is prepared for processing and analysis.

Data architects

Data architects and data engineers share some tasks. They must both ensure that data is well-formatted and accessible to data scientists and analysts, as well as increase the performance of data pipelines.
Furthermore, data architects must design and develop new database systems that meet the needs of a certain business model and job function.
They must keep these database systems up to date, both in terms of functionality and administration. As a result, they must maintain track of the data and determine who has access to see, utilise, and alter different areas of it.

 Machine Learning Scientist

When you encounter the term “scientist” in a job title, it usually means that the position entails conducting research and developing new algorithms and insights.
A machine learning scientist studies novel data manipulation techniques and develops new algorithms. They are frequently employed by R&D departments and produce research papers as a result of their efforts. Their work is more academic in nature, yet it is done in a business atmosphere. Furthermore, data architects must design and develop new database systems that meet the needs of a certain business model and job function.
They must keep these database systems up to date, both in terms of functionality and administration. As a result, they must maintain track of the data.

A Data Science Life Cycle

Data Science Life Cycle is method that consists of five key parts, beginning with data extraction and ending with analysis. 
Each of these five phases indicates job that data scientists must complete in order to achieve the best potential outcomes. 

Data Extraction 

Data Extraction is a process to gather or extract all the data information from data sources for subsequent processing or analysis.

Scrubbing Data:

Scrubbing Data is the process of cleansing data and removing duplicate and extraneous data. This procedure is necessary since the data contains a variety of secondary information that must be removed.

Data exploration:

Data exploration is the primary step for data analysis, and it involves exploring and visualising data in order to discover insights right away or to indicate regions or trends to investigate further.

Data exploration is the first phase in data analysis, and it entails studying and visualising data in order to get immediate insights or to identify locations or trends that should be investigated further.


Model Building:

In the model building process, data scientists understand the data and produce meaningful outputs. Setting up data collection methods, digesting, detecting what’s relevant in the data, and choosing a statistical, mathematical, or simulation model to gain insight and make predictions are all part of the process. Data interpretation: This entails developing a reasonable scientific argument to comprehend the data and applying those inferences to get a conclusion.

Data interpretation requires constructing a sound scientific argument to understand the data and then applying those conclusions to get a conclusion.


Data Science is changing the world in each and every aspect. It is now a fact that ‘Data is the new oil’ from the end of the last decade. From manufacturing, communication, Insurance, heavy engineering, defense to healthcare, artificial intelligence is driving the business and Innovation.

 Learning never stops in this field. You master the tool one day and it gets run over by an advanced tool the next day. A data scientist needs to be curious and always learning.

We have seen how there will be a lack of data science jobs in the next five years because companies will be adopting the automated pipelines of data science. But, there will also be high demands for data scientists who can automate data science pipelines.

As per my thought to automate those pipelines, we first need to understand machine learning algorithms to build a better-automated system, which will eventually lead to more jobs.

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