Contributed by: Gaurav Mehta
LinkedIn Profile: https://www.linkedin.com/in/gaurav-mehta-42bb9a8/
In these tough times of COVID-19, businesses have been working towards creating a predictive model of everything possible. To speak about a few, where will our economy be in the next few quarters? What will happen to retail trade? Will the price of petrol reduce? Will I land a Data Science Internship? etc. Some have started predicting the next move of their respective Head of States.
Everyone gives opinions based on the learning system they have gone through. They will create their independent and dependent variables accordingly. Most of us base our judgments on second-hand thoughts and lack of relevant experience.
Rather than getting into an Analysis Paralysis situation, a data science internship might come in handy. A doctor undergoes a mandatory internship to be able to make decisions whenever required. The internship does not make you perfect but provides you with professional guidance. Business analysts or Data science learners have been building various apps and dashboards to keep us posted about data from across the world.
“A picture speaks a thousand words” is a very well said idiom that proves the work done (dashboard) of a business analyst.
A business analyst tries to understand the following-
- What is the situation?
- Why did it happen?
- What would happen if it continues?
- What could be the possible solutions or remedies?
In today’s world, an internship is becoming buzz-worthy in every field of study. Students are becoming more and more inquisitive. Let us first understand what exactly an internship is.
What is an Internship in literal terms?—“Supervised practical training”.
Now another question may arise, what is the difference between data science internship and training? In professional terms, on the job training is an internship. Internship is a subset of training, which is targeted with specifications and is not open-ended.
What is the need for internships?
1. Setting the expectations right
When you are in the learning phase of your career, each one of us believes in making the world a better place with innovations. This enthusiasm is necessary to do a job. However, our expectations must be set right. This can only happen only if you go through the real-time experience of a job. Understanding the process and limitations that come with it are important. This would further help in the right communication and setting the right expectations for a business analyst.
2. Business takes the precedence
For every organization, running a business is their top priority. Data science learners may have many path-breaking ideas, algorithms, products, however, in the end, if it does not help the business, it may not be of any use. This may happen because of a lack of communication between departments. Business analysts have to work with various business process owners, thus, they must be professionally qualified to work their way through the process. They need to communicate well and ensure that the stakeholder understands him/her while giving due consideration to their viewpoints.
Also Read: Business Analyst Job trends – 2020
3. Learning from the experience of others
A data science internship is another way to learn from other’s experiences. Mentors or seniors assigned during the internship might help in making the interns realize the best practices, provide inputs to improve the existing process, and eliminate the redundant process. It can also help in overcoming shortcomings, thus bringing in a drastic positive change in the business process.
Business users can be apprehensive. They may pose friction to business analysts. So one can learn from other’s experience as to how to handle such situations.
4. Professional experience is necessary
Before getting into mainstream work, an internship helps business analysts/Data science learners get ready with all the required knowledge. All internal and external processes. This would help them optimally deliver their tasks, and fulfill expectations.
Also Read: Top Tools used by Business Analysts
5. Know what is right
Business analysts are more about data and inference. However, the basic premise of the business lies with internal business users. Data analysts/business analysts can understand what is right during their internship process.
Types of Internships
Logically there can be only two types of internships.
1. Unpaid internships
2. Paid internships
These kinds of analytics and data science internships are meant for students undergoing their Graduate or Post-Graduate professional programs. The students here are exposed to the working environment for a short duration and are normally not paid any stipend.
Unpaid internships provide a learning experience, keep the domain restricted, unstructured, and limit the exposure to business processes. These are subject to a limited subject in consideration. Like most of us do summer internships during our Graduation to get professional certification.
Business analyst/data science interns have a huge demand these days. Every organization, while performing its core business activities, is becoming more and more inquisitive of predictive behavior of data to align their strategies.
Companies are looking beyond traditional ways to increase their margins and sales, reduce overheads, and are now focusing their energies on new avenues that could help them diversify their business portfolio.
New divisions are created in bigger organizations for this purpose. Paid interns are hired to learn from the data.
These internships are-
- Very well structured
- Have a defined Scope of work
- Offer a decent pay structure
- Professional data sources
- Licensed analytical products used
- Real-time data monitoring and access
This has brought in a new wave of start-ups, which are immensely working towards servicing organizations that cannot afford or don’t want a captive model of paid internships.
Data science and Business analytics has opened a plethora of opportunities in various fields that are not just specific to job opportunities, but entrepreneurial work as well. Be it commercial organizations or government, everyone is now focusing immensely on analytics and data science. Internships are evident and mandatory. Business analytics and data science are evolutionary and need continuous research.
Some new opportunity areas in Analytics are-
- Forensic Accounting
- Fraud Management and Analytics
- Dashboards using Voice operated technologies, messaging tools, etc.
- Self-driving cars
- Smart Homes
- Smart Card (credit or Debit)
- Face and voice recognition
Data science is becoming a cornerstone in every field, so it becomes evident that there would be lots of requirements for trained human assets. Well trained resources would be required.
Another question comes in mind to address the above statement. How to get the right kind of resources?
The answer is that organizations need to step up their efforts in setting up their analytics department. If captivity can be managed there is nothing better. However, the outsourced models are working equally well and in turn creating more jobs in the industry.
A learner can get professional experience, on the job training, structured work environment, and get paid for his/ her efforts. Internships have given a boost to many people and they have even started various start-ups. Every bigger organization has ignited minds and new organizations were built.
Amazon inspired Flipkart, Google got its inspiration from earlier built search engines (ASK JEEVES, ALTAVISTA, etc.)
BigBasket Inspired Daily Ninja and Milk basket, Amazon Echo inspired Google mini and SIRI.
Traditional taxi (AVIS, Hertz, etc.) service inspired UBER.
There are many such examples. But one last question remains unanswered which needs introspection.
What should I do to get an internship in Data Science?
The learner must do a self SWOT analysis and a SWOT analysis of the field of study he wants to get into. Once he is aware of his abilities, and the kind of work that excites him/her, it becomes easier to choose your path.
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