With the rapid technology upgradation in the Data Science field, every learner needs to update his technical skills to align with current and future needs in the industry. Along with this, the learners need to be regularly updated about various facts, trends, technology upgrades, or innovations in the field of Data Science.

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Let us see a few key points that enhance your knowledge about Data Science:

  1. Data Science is one of the fastest-growing fields in technology, with a high demand for skilled professionals.
  2. Machine Learning and Artificial Intelligence are in high demand and widely used in various Data Science Applications.
  3. Data privacy and security are major concerns in Data Science, with new regulations and technologies being developed to address these issues.
  4. Big Data Cloud Computing is growing in Data Science, allowing for analyzing larger and more complex datasets.

Data Science is booming nowadays with lots of result-oriented applications in various sectors where data is an important resource for smooth operations in several processes, information extraction, and updation of information.  The most important is the security and vitality of information.

In various sectors, such as healthcare, banking, insurance, and retail – data serves as an important and useful resource.  Secured, genuine, and accurate data or information plays a key role. Data is regularly updated and maintained for future reference. Thus, there is a need for professionals who can handle and analyze the bulk of data from different sources and results into processed and vital information.

Since then, the Data Science Course has gained great momentum with certain myths and facts associated with it. To understand the methodology, technology, tools, and applications in Data Science, several educational institutes such as Henry Harvin No.1 Ranked Data Science Institute offer various upskill Data Science Courses for students or working professionals.

These courses provide in-depth knowledge of various domains, tools, technology, and methodology of Data Science. These courses upgrade your data science skills and boost your career in the respective field. 

We shall now discuss some of the Data Science facts in this blog to enhance our know-how about the significance of Data science in various sectors.

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.

First, let’s explore what is Data Science.

Data science is the scientific approach to handling data and making the most out of it to drive a business. Now from handling data to making the most out of it comes the real heroic deal to manifest in the world of crooked and prodigious data. 

The scientific approach mentioned above encompasses various tools, machine learning, algorithms, and a fair bit of analytical skills that wind the sailing ship of a business. The data collected is cleaned and analyzed with the help of effective tools and techniques.

Check out the most recommended Online Machine Learning Course for a better understanding of the concepts.

Data – The Game Changer

Data Science was born out of data. The volume of any type of data today cannot be underrated. Data is truly the game changer to have produced this entirely new field of data science. It is therefore customary for anyone interested in data science to know some facts first about the data itself before steering for the data science facts. Reckoning that, let us now check out a couple of knowledge bytes around data.

What is the Biggest Data Unit You know?

From our childhood we have been reading about the different units of digital data starting from bit, byte to even gigabyte or terabyte. But it is exciting to know the measuring units of all the big data floating around and driving the engines of big and small businesses. Check out the facts behind Big Data and Data Science.

Here is a tabular representation of some data units worth looking at. 

Abbreviation Unit Value Size (in bytes)
b bit 0 or 1 1/8 of a byte
B bytes 8 bits 1 byte
KB kilobytes 1,000 bytes 1,000 bytes
MB megabyte 1,000² bytes 1,000,000 bytes
GB gigabyte 1,000³ bytes 1,000,000,000 bytes
TB terabyte 1,000⁴ bytes 1,000,000,000,000 bytes
PB petabyte 1,000⁵ bytes 1,000,000,000,000,000 bytes
xxEB exabyte 1,000⁶ bytes 1,000,000,000,000,000,000 bytes
ZB zettabyte 1,000⁷ bytes 1,000,000,000,000,000,000,000 bytes
YB yottabyte 1,000⁸ bytes 1,000,000,000,000,000,000,000,000 bytes

  • If someone asks the fundamental – “How much data exists in the world today?”, there is probably no definite answer to this. However, it is estimated that by the year 2025, 463 exabytes of data will be created daily. 
  • We create new data every millisecond. We make 40k search queries on Google each second, which amounts to 1.2 trillion searches per year.
  • Data coming into any field or business is now no longer just data, it is Big Data. 

Since data science revolves around data and is gaining a remarkable gravity, there are many interesting trivia trails in the data science arenas too, just like data, that deserve heed. If you are a data science enthusiast, this article is sure to enamor you more towards it.

Here I list out the top intriguing facts about data science that will give you a closer view of the subject… Let us know in your comments below, which one is your favorite trivia bite!

Interesting facts about Data Science 

With the advancement in technology, Data Science is booming globally. By understanding these aspects of Data Science, students can gain insight into the field’s importance, challenges, and opportunities, motivating them to pursue further study and exploration in the Data Science field. One must know some interesting facts about Data Science and its application in various sectors as follows:

  1. Inter-Disciplinary Nature: Data Science is an interdisciplinary field that combines knowledge from computer science, mathematics, statistics, and domain-specific areas like business, healthcare, manufacturing, banking, retail, and many more.
  2. Demand for Data Scientists: The demand for data scientists is consistently high across various industrial sectors. Organizations are looking for genuine, well-organized, and accurate data for the smooth flow of SOPS.  
  3. Versatility: Data Science techniques can be applied to a wide range of problems, from predicting customer behavior to optimizing supply chain logistics and many more applications where data handling is more critical.
  4. Big Data: With the advent of big data, Data Science has become even more relevant. Data Scientists work with large amounts of structured and unstructured data to derive insights and patterns that were previously inaccessible.
  5. Machine Learning and AI: Data Science heavily leverages machine learning and artificial intelligence algorithms to analyze data and make accurate decisions. With the increasing demand for Machine Learning and AI technologies, every learner must keep in touch with the latest trends and updates in the field of Data Science.
  6. Data-driven Decision Making Data science helps organizations make informed decisions by analyzing large volumes of data to identify patterns, trends, and correlations.
  7. Data Lifecycle: Data science encompasses the entire data lifecycle, including data collection, cleaning, analysis, interpretation, visualization, and communication of results.
  8. Big Data: Data science deals with large and complex datasets, often referred to as big data, which may be structured, semi-structured, or unstructured.
  9. Tools and Technologies: Data scientists use a variety of tools and technologies such as programming languages (e.g., Python, R), statistical software (e.g., SAS, SPSS), data visualization tools (e.g., Tableau), and big data frameworks (e.g., Hadoop, Spark).
  10. Ethical Considerations: Data scientists must consider ethical implications such as privacy, security, bias, and fairness when collecting, analyzing, and interpreting data.
  11. Applications: Data science has applications in various fields including healthcare, finance, marketing, cybersecurity, social media analysis, recommendation systems, and more.
  12. Continuous Learning: Data science is an evolving field, and data scientists must continuously update their skills and knowledge to keep pace with new tools, techniques, and methodologies.
  13. Career Opportunities: There is a high demand for data scientists across industries, and the field offers lucrative career opportunities with competitive salaries and diverse job roles such as data analyst, data engineer, machine learning engineer, and data scientist.

# DATA SCIENCES FACTS

1. Data Scientists and Data Analysts are not the Same

Data Scientists and Data Analysts are NOT the Same

This is a common myth among people having a superficial idea about data science. The reality is, that the work of data scientists and data analysts is different. Whereas data analysts work on finding the trends and analyzing the data, data scientists work on finding the cause of the trend and forecasting the upcoming trends.  As data science is a new field, popping up of certain misconceptions is inevitable.  

However, it is worth noting that the two work in tandem. They complement each other and work toward a common goal. Now let us check out some of the basic differences between the two.

Data Scientist Data Analyst
Discovers unexplored questions that may need an answer. Uses existing information to get workable data on existing questions
Skillset: Algorithms, data mining, programming, database management, data analysis, machine learning, predictive analysis Skillset: Data mining, modeling, programming, statistical analysis, database management, data analysis
They estimate the unknown data They work with a known data set
They choose to address business problems that would have maximum effect  They address the business problem assigned to them
They work at a macro level They work at a micro level

2. Data is Never Clean

data cleaning logo

That’s true. Data is nasty. Even when data is collected and cleaned with an eagle eye, some or the other data discrepancy does creep in at some point. And data scientists know how to work with data chaos and noise while cleaning it on the way.

About Dirty Data

Dirty data is of one or more of the following forms-

  • Incomplete
  • Duplicate
  • Irrelevant
  • Inaccurate 
  • Incorrect
  • Misspelled

“It’s way more than just errors. It can break your data science project.”

– towardsdatascience.com

The collected data being dirty is one problem. However, the bigger problem is joining multiple datasets into a single entity. Now data can be collected from different sources by different people, software, devices, etc. There is a huge possibility of them being non-coherent. The join key may not be consistent or the format may be different for different systems. Data scientists clean the entire data by re-formatting, screening, organizing, and so on. 

“You will spend most of your time cleaning and preparing data.”

– Kamil Bartocha, head of data science R&D

The question now is, if data is so unclean, how then analysis is done out of it? Well, at this point it would be good to paraphrase, in the end, data is clean enough to reach a desired outcome. 

Several data cleaning techniques are implemented at every step to reach the least dirty form of data. And this becomes the basis of the final analysis.

3. Do you need to be a Tech Savvy or hold a PhD to learn Data Science

Data science sounds like a field of tech-savvy professionals and this leads to the common misbelief that to be eligible to learn data science, one needs to have a super brain or hold a Ph.D. degree. 

This is incorrect. Anyone with an average intelligence can learn data science. 

Data science learning involves upskilling in the following fields – 

  • Statistical modeling
  • Predictive modeling
  • Machine learning
  • Programming
  • Algorithm
  • Analytics

This is the theory behind learning data science. But it would be interesting to hear a few words about data scientists, straight from the horse’s mouth.

Joma, a famous YouTuber and an experienced data scientist at a GAFA(Google/Amazon/Facebook/Apple) company describes in a video, what it takes to learn and become a data scientist. I’ll summarize his view in the below points- 

  1. One does not need a degree from a very high-profile university.
  2. Data scientists come from different backgrounds like – electrical, economics, etc. Some even don’t have a science degree.
  3. To learn data science one can do an internship or study basic stats. 
  4. Other things like programming, algorithms, and statistics – can be easily picked up along the way.  
  5. One needs to have empathy to ask the right questions related to data.
  6. One needs to learn to apply the correct SQL queries and also learn a bit of Python language that anyone can, with the correct approach.
  7. Data scientists work a lot with data, sequel queries, and presentations. 

In a nutshell, data science is not as heavy as it may look. Just an empathy towards possibilities is the requisite. Rest falls in place along the learning. 

4. Data Science is not Just Excel Sheets

Contrary to the aforementioned belief, this one can seem surprising but many people think that the life of a data scientist revolves around Excel sheets.

This is anything but true. As mentioned before, data science is a vast field with a basic focus on the correct and intended outcome. And to get that outcome, the data science professionals fight tooth and nail.  They use different data analytics techniques, SQL queries, statistical analysis, predictive analysis, and whatnot. 

They do work on Excel sheets, but that is just a small unit within their work periphery. 

There was once a time when Excel sheets played a major role in arriving at a conclusion and making an analysis using formulae and calculations. At present with the easy availability of programming tools like Python and R, most data scientists spend a great portion of their time coding rather than on Excel sheets.

5. Data Science Competitions and Real Life Projects are Different

real-life-data-science-projects

Getting success in a data science competition(eg. through an online platform like Kaggle)  may give a boost to one’s confidence so much that one starts thinking of landing a data science career. But it is here to understand that there is quite a lot of difference between a competition and a real-life scenario.

“ I remember I was a little bit overwhelmed when on my first real-life project all the models that typically worked well on Kaggle, miserably failed. I wish I was prepared for this.”

– Sergii Makarevych, data scientist

Here is a listing of a few differences between the two-

Data science competitions Real-life projects
The number of datasets is limited There is no limit on data and datasets. It’s the data that matters.
In online competition platforms, a warning is given when you have made an error There is no warning. You only learn after you have committed a mistake and borne the consequences. You go back all over again and do some data cleaning and rework. 
You need to write the code just once You need to rewrite the code every 5-15 minutes. 
You do not need to deploy your model.  You deploy your model
There is no authentication or security Authentication and security are equally important as the data itself. 

So, it would be safe to say that competitions do give a fair practice for data science. But it is not enough. You need to make your hands dirty and work in live real-time projects to know the correct essence of data science. 

6. More Data Does Not Always Mean More Accuracy

I am tempted to use a cliche here – Quantity does not always mean Quality. 

Let us understand this point regarding data through the bottom-up approach. 

Suppose we have a dataset with the exact number of minimum data that is needed to make a correct analysis. This would be an ideal dataset. Now if we add some more data, the entire dataset will need to be reconstructed considering the new set of data as well. While reconstructing, there will be a need to clean the new data and spend time to understand their deviation from the existing set, if any. 

Now even after the new data is cleaned and merged into the existing ideal dataset, there is a possibility that some new element is still dirty but unidentified. This will lead to an overall degradation of the final result or analysis. 

In this case, less data was surely better than more data. 

Hence, more data doesn’t mean more insight or more value addition. Using smart data is the key.

7. The Data Science field has different roles, not just Data Scientists

Many people associate data science with data scientists only, ignoring the other prominent roles belonging to the field.

Data science includes all of these – 

  • Data engineers – They are responsible for managing data infrastructure throughout the data science lifecycle. Basic skills include – programming tools like Python, database tools like NoSQL, and big data tools like Hadoop. 

  • Data analyst – They find answers to questions by working through the data available, using appropriate tools. Basic skills include – programming, data visualization, statistics, mathematics, and of course data analysis.

  • Data scientist – Data scientists work on big data, analyze it, and then communicate the findings through reports and presentations. Basic skills include – statistics, mathematics, programming, data visualization, SQL, Hadoop, and machine learning. 

Apart from these too you can make your career in data science through various other roles.

8. Data Science is not meant only for Large Organizations 

Many businesses believe that data science is meant only for big organizations having high-class infrastructure. 

Such belief pops out from a wrong notion about data science. Data science is not made up of machines, heavy tools, or the size of working resources. It perhaps is made up of big data, statistics, analysis, programming, presentation, and some smart people who know how to make the best out of data and add value to the organization. It has nothing to do with big or small organizations.

A data scientist needs to arrive at a result that benefits the company. And no one cares as to what tools and techniques have been used to achieve that result. 

Coming to infrastructure, all that is needed is a computing device, the internet, and some tools that help through the data science life cycle. There are several open-source tools available online that can be downloaded to get the ball rolling. 

9. Data Science needs great Communication Skills

Communication and presentation play a key role in data science.

Communication here refers to two areas – 

  • Coordinating within and among the teams during the different stages of the data science life cycle. 
  • Presenting the outcome most comprehensively and lucidly. 

Without proper communication, the entire exercise may fall futile. It may not project into any substantial product. It is important to learn public speaking as there are a lot of presentations involved. 

Also, learning to do better and crisp writing enhances one’s visibility in and around the organization.

Writing involves –

  • Powerpoint 
  • Blog
  • Email
  • Report 

An analysis without proper communication in writing or otherwise is just a placeholder with no significance.

10. Data Science is not for Everyone

Let me first throw some light on what a data science interview smells like.

Again, I have taken this information from the famous YouTuber and data science expert, Joma. 

The data science job interview questions spin around the below – 

  • SQL or a simple coding such as Python, as they want to make sure you know these because you would be doing a lot of it on the job
  • A quantitative analysis or a math question including statistics, probability, or linear algebra.
  • Some graduation-level math theorems like Bayes’ theorem, distribution, law of large numbers,  linear regression, etc. 
  • Product interview: they give some hypothetical product and ask you how you can improve it. 

So did the questions smell sweet or sour? 

The topics may seem a bit overwhelming for those who are an absolute novice in this area. But those who are prepared and ready to jump into the pool of data science would find it interesting to glimpse over such interview topics. 

However enchanting it may look to a beholder, the data science field is no cakewalk. Even preparing for the field needs a good amount of data affinity. 

There are lots of videos and articles on the web suggesting anyone can be a data scientist. It’s true with certain conditions. It is always a good idea to ask yourself first, why do you want to be in this field? It is good to do some reality check before taking a blind leap. 

Introspection at the start is a great virtue for a successful stint in any field.

Conclusion:

Data science is becoming inevitable with data explosion in almost every field. It offers a good career opportunity. Thinking of data science as a career option can be a wise decision for anyone who enjoys problem-solving and has data empathy.  

As cool as it sounds, it has immense potential for both businesses as well as for job seekers. But it is advisable not to fall for any wrong information about the field. 

With its growing popularity, data science has some myths associated we saw along with some interesting facts. Let me know in the comments if I missed any point.

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