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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.

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 updating 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.

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.

Recommended Reads:

• Top 15 Best Data Science Courses in Mumbai
• Top 10 Data Science Courses in Pune
• Top 10 Data Science Courses in Bangalore
• Top 10 Data Science Courses in Nagpur
• Top 20 Data Science courses in Delhi NCR
• Top 10 Data Science Courses in India

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