Data Science

Expert View on The Future of Data Science

Data science solutions have been widely adopted by leaders and starters covering various business fields. It is a combination of math, art, and logic used for bettering and accelerating the development of industries. Moreover, the data scientist career path has been one of the highest-paid, most attractive, and respected. In this article, we will describe what the future holds for data science based on the Light IT data engineering company’s opinion. The experts of this company shared their views on six key transformations to expect, so let’s get started.

1. The amount of data will only be increasing

Technological progress will only encourage people to consume more information and spread more information about themselves. In 2020, a user generates about 2 megabytes of data per second. For this reason, specialists find new ways to store large amounts of information. The currently used methods of storing information look like this now:

Hybrid storage. Cloud isn’t reliable enough to store confidential information about companies, so the most private information would be kept on portable drives, safes, lockers, etc. New ways to improve password practices and restricted access have yet to come.

Multi-cloud storage. Some companies must consider keeping data in multiple cloud environments, both public and private.

2. Data science will become automated

Specialists find new methods of automating data science. Even though it is meant to automate and optimize various business areas, integration and maintenance of its developments are not automated just yet. Almost everything in this field is on its way to automation, from storing and refining data to final modeling.

Experts focus on making neural networks self-learn and improve feature engineering to save time on further developments. As an example, Google is actively investing in Cloud AutoML that automates the training and designing of models. Some companies make data management more convenient for data scientists to let them work faster and easier.

3. Machine learning will stay the most wanted for business

Machine learning will be one of the trendiest implementations in the years to come. It covers a great variety of progress-driven tasks such as video, audio, image recognition, neural networks, text analysis, and natural language processing. Specialists put a lot of effort into reinforcement learning, which allows algorithms to work along the sequence of actions successfully. We can see it from how AI plays computer and board games.

Business process automation and forecasts using machine learning are other trends, from which industry players get strategically-important benefits. These benefits let companies reduce the costs of warehousing, logistics, staff, and eliminate human-generated mistakes. Predictive modeling will improve to provide more accurate insights on customers and competitors. Specialists also focus on conversational analytics to make chatbots and voice assistants more efficient to make customer service better.

4. Demand for skilled data scientists will only be growing

Data science is a popular career path throughout the world because specialists can work on exciting projects, implement the most incredible ideas, solve problems, and get paid well. Still, even the most developed countries worldwide have a significant shortage of employees specialized in data science. In 2019, the US companies were seeking over 2 million specialists in this field of knowledge.

Although the IT and digital sectors have most data engineers hired, they also work in other industries such as retail, healthcare, entertainment, transportation, manufacturing, and many more. Apart from data science or machine learning departments, scientists  in this field are engaged in the product, marketing, content, and game development teams.

5. Data security and privacy are expected to improve

Since massive amounts of data are generated throughout the world every year, some of it can be vulnerable to various types of security and privacy threats. Currently, there are three more specific reasons why data security has yet to be improved:

Lack of well-educated cybersecurity talents;

More elaborate and devastating cyberattacks;

Ignoring or poor awareness of regulatory standards.

However, the number of security-aware users is small. People don’t want to invent complicated and reliable passwords and read privacy policies. As data science evolves, we can expect security and privacy protocols to change according to the applicable laws at locations where they operate. Still, users must also show higher carefulness and awareness of the law to avoid data theft.

6. Fast and actionable data will outshine big data

Event-driven applications help digital enterprises instantly identify and respond to new opportunities or threats. Many businesses focus on fast data, which is far ahead of stream-based analytics. It allows specialists to analyze both current and historical data simultaneously, thus helping to navigate and respond to a particular situation effectively.

Fast data can improve the quality of business analytics. Combined with an integrated development environment, built-in intelligent data processing, and machine learning, fast data simplifies various processes and also starts a new era of business intelligence.


The critical areas of data science transformations include its automation, the creation of reliable storage for growing amounts of information, and keeping up machine learning best practices. Experts also realize the need to create more skillful talents, enhance privacy and security, and shift focus from big data to fast data. The sector’s changes are about to come gradually, and the hardest part of it would be dealing with security matters in different countries. As data science improves, cyber threats become harder to prevent, so companies must think about making faster progress with data.

The Scary Part Of Big Data And Data Science

These scientists have become one of the most prominent problem solvers in this digital error. Combining advanced statistics, math and computer science, they are experts in such management, mining, and visualization. It is safe to say that they are conversant with statistics analysis, statistics or modeling, and engineering.

Without it, big data could still be an impossible task to solve since it refers to structured and unstructured one that inundates an organization daily. The size of big data usually is not the matter in this kind of  science, but what an organization is planning to do with this is what matters.

It is also important to mention that any person interested in becoming a data scientist can do so by adopting a suitable learning path online. They are on a constant high demand because organizations today rely so much on technology; hence, volumes of data need to be broken down and put into good use. Wondering what this science can do, here is an in look with the best examples to showcase the real power of big data and its science;

Electoral processes

Cambridge Analytica was a British political consulting firm that combined data science-driven strategies during elective operations. The organization collected material on voters including internet activities, voters’ behavior online, demographics, and other sources both private and public. This information which existed in the form of big data was broken down by special scientists and used to influence voters psychologically.

Although it is was shut down in 2018, reports have it that Cambridge Analytica acquired information from millions of voters on Facebook users without their consent. It is said that it was the most aggressive invasion of privacy that Facebook had to oppose the operations of the organization.

Other than collecting info on Facebook, Cambridge Analytica was also responsible for the development of the Cruz Crew app. The app tracked the physical location and contacts, and it goes down in history as one of the presidential campaign apps that invaded many personal data.


When it was voted that the UK gets to leave the EU, no one could believe it. However, the truth is, data science proved the opinion polls wrong, but how did they do it? According to reports, Benedict Cumberbatch had single-handedly changed the history of the UK through data science.

Although it was expected that more votes would favor the UK to stay part of the EU, Benedict proved the opinion polls wrong. He used information collected from social media users to serve various kinds of adverts that had psychological impacts. These adverts were dished out to different consumers concerning their stand on the Brexit matter.

The voters who supported the UK to maintain in the EU, for example, received ads and messages like Turkey going to join the EU with its vast population of 76 million people. Such information quickly influenced people to reconsider their stand on the matter with the national resources in mind.

The 2016 US elections

The psychological power of big data and data science is also evident in the 2016 US elections. According to sources, the Cambridge Analytics had their hand is the electoral process that shocked the world. It is, however, not known by most that  analysts from both teams were working to expose the candidates on free media and cable TV.

Data scientists managed to serve political campaigns to the public after conducting emotional research about their opinion concerning various matters. This made it easy for the candidates to brainwash the public quickly by manipulating their psychology.

How to become a data scientist

There are different ways that someone can become a professional. Traditionally, you can enroll in a  school and take a couple of years to complete the course. Alternatively, you can decide to take up the course online and enjoy a self-paced learning path method if you visit here.

Data science is composed of various sections, and no one is restricted to learn everything. In a real school, however, this can be impossible because you will be required to specialize at some point. Not that it is a bad thing but compared to using various learning paths, you stand a better chance of becoming an expert faster.

Power of data science and big data in the business world

The power of data science and big data are not only felt in the political arena. Manufactures, retailers, and professionals today are spending a lot of money for these services because they need it to;

Identifying threats

As long as you are using technology in your profession or organization, you are under constant risk of being attacked by cybercriminals. With the continuous exchange of statistics with your system, a regular person cannot detect even when they have been hacked. Data scientists tend to have a third eye for analyzing big data and can quickly point out essential in your systems.

Using statistics, significant methodology, path, and networking, a data scientist can create a system that will predict any unusual activity. These include things like fraud and generate an alert in good time for a swift response. Without such scientist in your organizational team, you are at risk of losing so much to the relentless hackers.

Deliver relevant products to the right consumers

Data science goes deep into analyzing market behavior. From information received in your daily activities, you can find out which product sells best in which area. Scientists, therefore, are capable of breaking down the market for you and even allow you to make the right kind of product available to the right market.

This will save you lots of time and money because you will be able to channel your production and supply as demanded by the market. You will also be in an excellent position to come up with new products as needed by consumers.


If you have ever fancied having the power of influence in your hands, then this is the kind, of course, that is suitable for you. The best part is, you can become one even if you have no background in this course. All thanks to the online courses that are tailored for passionate people.

Significance of Data Science as an Interdisciplinary Program

The time when we talk about data science then without any second thoughts, we can say that it isn’t restricted to a single field and area of interest. The importance of data science has increasing day by day because of its manifold connections with other areas and disciplines.

Data Science as an Interdisciplinary Program

Data Science is developing as a troublesome significance of the digital revolt. Depends on the mixture of big data obtainability, refined data analysis methods, and ascendable computing infrastructures, Data Science is swiftly altering the way we do corporate, meet people, conduct research, and rule society.

Img Source:

It is also varying the way scientific research is achieved. Model-driven methods are augmented with data-driven tactics. A new model appeared, where philosophies and models and the bottom up encounter of knowledge from data jointly support each other.

Experiments and examines over huge datasets are useful not only to the authentication of current philosophies and models, but also to the data-driven detection of patterns developing from data, which can aid scientists design better philosophies and models, yielding deeper comprehension of the intricacy of social, financial, natural, technical, ethnic and natural singularities.

Value of Data science Now a Day

Data science as we all know is becoming popular day by day and has a major role in various academic as well as practical fields. By learning data science and gaining an online master’s degree in data science, you can click here and one can learn a lot of about related a field that seem to be different but has a deep connection with data science in one way or another.

Img Source:

All over the world data science is considered as an interdisciplinary and prevalent paradigm directing to turn data into knowledge, born at the juncture of a variety of scientific and technical areas such as databases and data mining, machine learning and artificial intelligence, text mining, complex systems and network science, statistics and statistical physics, natural language comprehension, applied mathematics. Remarkable advances are happening in data-driven pattern discovery, in automatic learning of prognostic models and in the analysis of multifaceted networks.

Nowadays, data scientists are initiating new grounds for investigation and research. They are investigating with intelligence meeting technologies and emerging refined models and algorithms, for the sake of supporting brands answer some of the biggest trials that they face.

Img Source:

Data Science and its Growing Importance

As an interdisciplinary field, data science deals with courses and systems that are utilized to excerpt information or visions from large amounts of data. Here data that is taken out can be structured as well as unstructured. Apart from that data science is an extension of data analysis grounds such as data mining, statistics, predictive analysis.

It is a vast field; data science utilizes a lot of philosophies and methods that are a part of other grounds including information science, mathematics, statics, chemo metrics, computer science, and many others.

Img Source:

Following are some reasons that are significant for Data Science as an Interdisciplinary Program:

  • Data science benefits brands to get their consumers in a much boosted and authorized manner. There is no doubt about it that consumers are the soul and foundation of any brand and have a huge role to play in their achievement and failure. With the utilization of data science, brands can attach to their consumers in a personalized manner, thus safeguarding better brand power and commitment.
  • Another main and foremost reason why data science is attaining so much of attention is that it permits brands to convey their story in such an amazing and controlling manner. The time when brands and corporations use this data in an inclusive manner, they can share their story with their boarder audience, so making better brand connect. As we all know that nothing connects with trades like an operative and influential story, which can instruct all human sentiments.
  • There is another new area named as Big Data that is regularly improving and developing. With so many tools being settled, almost on a daily basis, big data is serving brands and organizations to fix multifaceted problems in IT, human resource, and resource management in an actual and planned manner. This means to say that an operational utilization of resources, both substantial and non-material.
  • Another significant aspect of data science is its findings and outcomes that can be implemented to nearly any sector such as travel, healthcare, and education between others. Comprehending the inferences of data science can go a long way in serving sectors to examine their trials and address them in an operative way.
  • Data science is available to almost all areas. There is a huge amount of information that is obtainable in the world today and using them in the best possible way can spell success and failure for products and administrations. By using data in the right manner you will be able to hold the key for attaining goals for products, particularly in the future.
Img Source:


Without any doubt, it can be said that data science has a vast role in various areas and these subjects, programs, and courses that look different are deeply connected with data science.