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Tag: Data Scientist

Pursue a Career in Data Science: Why Is This The Perfect Time (COVID – Pandemic)?

Posted on by Imarticus Learning
Reading Time: 2 minutes

In a recent article published by LinkedIn, the organization reported a 25% increase in the number of data science professionals in India alone. On a global scale, this number is close to 37%. If you have been wanting to pursue a career in data science, then now is the right time to chase that dream, and in today’s article, we will tell you why?

Let’s get started.

Why Should You Pursue a Career in Data Science in 2020?

Post the COVID-19 crisis, the world has shifted to a completely remote work environment, and as predicted, the amount of data that is available now for collection has increased rapidly. As companies keep collecting a variety of different data sets, the need for expert data scientists are swiftly on the rise.

Career in Data Science in COVID 19 PandemicThe key concept behind this rise being, companies, need experts to analyze the data that is being collected and conclude decisions which not only contribute to short term gains but also long term business advances for the business.

Along with this, since the demand for such roles is on the rise, companies are willing to spend more to hire the best talent in the market, thus increasing the overall pay of the profession.

 

Some of the most common designations you can explore in this field include the following:

  1. Data Engineer
  2. Data Analyst
  3. AI Product Manager
  4. Data and Analytics Manager
  5. Database Administrator
  6. Business Analyst

How to Get Started With a Career in Data Science?

Now that you know the why of why you should pursue a career in data science, along with a few of the designations you should pursue, let us explore how you can kick start your career.

21st century is one of the hottest times to pursue a career in data science since millions of job openings are being posted on the regular. While having a degree in science or engineering is a good foundation to pursue a career in data science, if you truly want to stand out, one of the best things to do is to get a professional certification from any of the top recognized companies.

While one of the most obvious advantages of having a certification in data science is the edge it gives you over thousands of applications; the underrated advantage is making it easy for recruiters to spot your talent and choose for the right role.

Conclusion

2020 is a cornerstone in shaping how big data analytics will be used in the future, and thus the decisions you make today on how to pursue and shape your career in data science will determine your success in the future. With technologies such as big data, machine learning and artificial intelligence being readily used by the small to medium scale businesses around the world to increase their capabilities, the need for skilled professionals, who can swiftly analyze this data and extract meaningful insights will be on a constant rise.

In 2020, if you choose to pursue a career in data science, it can easily be estimated that your future will be secure for the next generation.

We offer data science courses at our centers in Mumbai, Thane, Pune, Ahmedabad, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.

Why Data Scientists Should Follow Software Development Standards?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Introduction

Technology has become the flagbearer of changes to which we are subjected to daily. Therefore, it impacts us in every possible way. How technology comes to us should mostly positively affect us. Therefore, it becomes important for the people driving this change to adhere to some pre-defined standards for improved quality of work and standardization of the same.

Data Science has come a long way. It has become one of the most popular subjects giving people the best in class in jobs and putting them in a position of the drivers of change. A Data Science course in Chennai would help you in becoming employment ready.

Data Science has enabled handling the bulk of data with ease. With Data Science you can drive different conclusions from the same set of data. You just need to change the algorithm.

Who is a data scientist?

Your Data Science career can bring a lot to the table. Initially, the word ‘Data Scientist’ was used for people who used to organize and analyze a huge amount of data. However, the role of a data scientist has drastically evolved in its due time course.

Today, data scientists develop algorithms that make sorting, compiling, and analyzing the sets of data a cakewalk. Effective data scientists have standardized the processes and have developed a standard procedure to work things out. These data scientists are technically well-equipped and can build complex algorithms which can be repeatedly used to make a task easy.

They have a strong quantitative background and are usually result oriented. Also, they have extensive knowledge of different programming languages like R, Python, Tableau, SQL, etc. As the demand for automatization is increasing, data scientists can access more and more jobs.

The need for data scientists to follow Software Development Standards

Standardization is important everywhere irrespective of the field. Therefore, these data scientists need to adhere to a specific set of software development standards that are already in place.

In the times where cybersecurity is a major issue, it is really important to have some software development standards in place. This would ensure that the new software is being designed keeping in mind these standards which will consider the safety and security of data and information of the end-users of that particular person.

Development standards have been also designed to keep uniformity across the organization. These standards ensure that the work output is generated at a certain level. Also, with software development standards, a set of consistent rules are laid down which makes the job of a data scientist quite easy.

With Software Development standards, you can use the same algorithm for different purposes with slight modifications. Also, it ensures that the program written by a data scientist is clear and understandable and adheres to the statistical principals. With standardization, codes will be written in a language that is understood by all.

Having simple rules is important. Software development standards follow a structured approach when it comes to writing a code or designing software. It bridges the gap between your research and the final product which you want to build.

These standards are up to date and are formulated keeping in mind different quality assurance standards. This would ensure that a quality product in the form of codes is delivered. With the implementation of these practices, it would be really easy for the data scientists to meet the requirements of their customers and deliver quality results.

Conclusion

Following a set of standard procedures can make the work of data scientists’ error-free to a great extent. Also, it enables easy quality checks ensuring good delivery of an end product.

 

A Day In The Life Of A Data Scientist!

Posted on by Imarticus Learning
Reading Time: 3 minutes

The data science field holds immense career potential, yet you must be thinking, what actually do data scientists do the entire day?

To provide you deep insights into data scientists’ usual tasks so you can imagine yourself in that role and decide if the time is ripe to get trained for it, we have gathered some insights for you.

No Such Typical Day

If you ask somebody working as a data scientist about their typical working day, he/she may burst into laughter after listening “typical”. For those who are adaptable and flexible, and love to do various responsibilities, then a typical day of data scientists should fit them just fine. While these workdays are subject to changes, some essence of the day stays as it is – working with people, working with data, and working to stay abreast of the field.

Data is Everywhere

Given the job role, it is no surprise that data scientists’ regular tasks hover around data. A major portion of their time is consumed in collecting data, analyzing data, processing data, yet in several ways and for several reasons. Data-centric responsibilities that data scientists may come across include:

  • Pulling, merging and assessing data
  • Searching for trends or patterns
  • Leveraging numerous tools such as Hadoop, R, MATLAB, Hive, PySpark, Python, Excel, and/or SQL
  • Developing predictive models
  • Striving to streamline data issues
  • Developing and testing new algorithms
  • Creating data visualizations
  • Gathering proofs of concepts
  • Noting down outcomes to share with colleagues

Interacting With a Broad Range of Shareholders

This may appear as if it has a minor role in data scientists’ day, yet the otherwise is true as eventually, your job is to ward off issues, not create models.

It is paramount to remember that even though data scientists are playing with data and figures, the reason for this is fueled by a business requirement. Having the ability to view the larger picture from a department’s perspective is vital. So is being able to comprehend the tactic behind the requirement, and to assist people comprehend the consequences of their decisions.

Data scientists dedicate their time in meetings and replying to emails, just like most people do in the corporate sphere. Yet, communication skills may carry greater importance for data scientists. While attending those meetings and responding to those emails, as a data scientist, you should be able to elucidate the science behind the data in layman terms, as well as able to comprehend their issues as they view them, not from data scientists’ viewpoint.

Staying Updated with Changes

Both, working with data as well as with others will account for a notable portion of the day if you decide to pursue a career in the field of data science. The remaining of your day will be captured staying updated with the data science industry. New insights arrive on a daily basis as other data scientists craft a solution to fix an issue, and then extend their new finding.

Data scientists, thus, normally dedicate a portion of the day going through industry-centric articles, newsletters, blogs, and discussion boards. They may attend conferences or connect online with various data scientists. Moreover, occasionally, they may be the ones to extend new insights.

As data scientists, you do not wish to waste time starting from scratch. If anyone else has a better solution to fix an issue, you would like to know. Staying updated with changes is the sole way you will have the ability to do that.

Now the question arises, how to become a data scientist? Well, the good news is you do not have to worry much about it. There are loads of resources available at your doorstep in the form of online courses and e-books. So, if you want to pursue a career as a data scientist, grab these resources and get yourselves enlightened.

Do Data Scientist Use Object Oriented Programming?

Posted on by Imarticus Learning
Reading Time: 2 minutes

It is estimated that there are 2.5 quintillion bytes of data produced every day in our world. In this data-driven world, the career opportunities for a skilled data scientist are endless. With the data production rate predicted to go higher than of now, the career opportunities for those who can manage data are not going anywhere. This article discusses whether data scientists are using Object-Oriented Programming. Let’s find out.

What is Object-Oriented Programming
Object-Oriented Programming or OOP is a model of the programming language organized around objects rather than the actions. It also emphasizes data rather than the logic. Traditionally, a program is considered to be a logical procedure that converts input data into output.

In such cases, the challenge was to come up with a logic that works. The OOP model redefined that concept. It takes the view that we should care more about the objects we are trying to manipulate rather than the logic we use. These objects could be anything from humans defined by names and addresses to little widgets such as buttons on the desktop.

The main advantages of OOP are:
• Programs with a clearer modular structure.
• Codes are reusable through inheritance.
• Flexibility through polymorphism.
• Very effective problem-solving.

Object-Oriented Programming in Data Science
Using Object-Oriented Programming for data science may not always be the best choice. As we said, the OOP model cares more about the objects than the logic. This type of approach is most suited for GUI, interactive application, and APIs exposing mutable situations. When it comes to data science, functional programming is preferred more due to superior performance than compared to the OOP model. The advantage of better maintainability offered by OOP is sacrificed in the data science for the sake of performance.

Polymorphism is an important feature of OOP. It allows a loosely coupled architecture, where the same interface can be easily substituted for different implementations. This feature is very helpful when dealing with applications of large size. However, data scientists seldom use large codebase. They always use small scripts and prototypes. So, OOP would be far too much overhead with no significant benefits.

Although, machine learning libraries are a must needed thing for data scientists. Most of these libraries make use of object-oriented programming, at least the ones in Python. Machine learning libraries such as Scikit-learn heavily make use of OPP. Data scientists who work with R and SQL will never have to use OOP.

Conclusion
It is clear that even though Object-Oriented Programming Offers a lot of benefits, it is not exactly what data science need. So in general, object-oriented programming is seldom used by the data scientists.

If the data science career seems to suit you, wait no more. Imarticus is offering courses on data science prodegree, which will provide you with all the skills and knowledge to excel in your career. This Genpact data science course allows you to start your journey on the right foot with placement assistance at so much more.

Pokémon Go – Why is it so popular?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Pokémon was originally an animation show by The Pokémon Company and was aired in the year the year 1995. It began as a game on the ‘Gameboy’ and was soon turned into an animated franchise by the combined efforts of Nintendo, Creatures and Game Freak. Since day 1, this show had garnered immense popularity, and at the height of its fame, had scores of teenagers as its viewers.
Recently, Nintendo launched Pokémon Go as a mobile application, thus bringing together a clever marketing ploy, a health benefiting agenda and made optimum use of the nostalgia attached to the franchise.
pokemon-go-office
Pokémon Go is fast becoming the most sought after application today. Here is some fascinating data we have found so far:

  • In U.S.A alone there have been more than 60% of people downloading the app.
  • This proves that more than 3% of Android users in USA are using this application on a daily basis, nearing the usage of Twitter.
  • People have no problem with the data usage of this app, which is believed to be a lot owing to the graphics. Surprisingly this is not the case, as data analytic experts claim it does even use data as much as Google Maps.
  • Pokémon Go has more than 10 million users already and the number is definitely bound to increase!
  • This application has achieved the most amount of data retention, in just weeks of its launch and has clearly stood out at the standard time a user takes to dislike an app; which in this case, is almost negligible.
  • The fact that there are a number of data analytics in terms of the audience targeted, the age groups, the trends and the fact that this app is literally bringing people out of their houses.
  • Pokémon Go seems to have taken over the world with being downloaded in countries where the app hasn’t even been officially released yet. Appkmirror which a secondary site to download the app has had almost 4 million visits since the launch.

If the stats are to be believed, then Pokémon Go certainly seems to be taking over the world. This presents an incredible opportunity to various firms to invest in this application, as well as to generate more sales, through luring customers to their outlets.

Apart from the primary glitches like the server rundowns, people getting hurt, trespassing or disrespecting a solemn site, this application still seems to be going strong.
Data Analytics will play a major role in further development of the app, thus adding more impetus to career prospects of a Data Scientist.
Imarticus Learning is a reputed education institute, providing a host of courses in the renowned field of Data Analytics and the tools used here, like SAS, R programming, Hadoop and others.

Written by Imarticus Team