Data and Analytics Weekly Round-up: July 9, 2019

Here are a few Data and Analytics updates from last week to keep you informed.

4 Challenges with Leveraging Analytics — and How to Overcome Them

To fully capitalize on the potential of modern analytics, enterprises must balance a complex mix of technical, organizational and cultural requirements. With this complexity come possible roadblocks that can hinder efforts to gain competitive advantage and also dilute returns on investments. Read along how to combat them.

Revenues from Big Data and Business Analytics to Hit $260 bn in 2022: IDC

Worldwide revenues for Big Data and Business Analytics (BDA) solutions will reach $260 billion in 2022 with a compound annual growth rate (CAGR) of 11.9 percent over the 2017-2022 period, according to a new forecast from International Data Corporation (IDC)…. [Read More]

What Matters Most in Business Intelligence, 2019

Improving revenues using BI is now the most popular objective enterprises are pursuing in 2019. Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today…. [Read More]

The Coolest Business Analytics Companies of the 2019 Big Data 100

As part of the 2019 Big Data 100, CRN has put together a list of business analytics software companies offering everything from simple-to-use reporting and visualization tools to highly sophisticated software for tackling the most complex data analysis problems…. [Read More]

Top Five Business Analytics Intelligence Trends for 2019

From explainable AI to natural language humanizing data analytics, James Eiloart from Tableau gives his take on the top trends in business analytics intelligence as we head into 2019…. [Read More]

 

Happy Reading!

 

With Career support, I got to interview with many companies – Sai Ramya Machavarapu, Data Analyst at Mercedes Benz.

A career transition can be a daunting experience for many. But given the right direction, learning, and support, it is more like a cakewalk. That’s why here at Great Learning, we strive to provide the right learning, practical exposure, and complete career support. 

What has your professional journey been like?

I completed my graduation in Electronics and Communications Engineering from Amrita College, Bangalore. Then I moved to the USA to pursue my Masters in Electrical Engineering from the University of Missouri, Kansas-City in the year 2014. I got placed in Reliable Software Resources as a QA Tester and worked until May 2017. I will be joining Mercedes Benz very soon as a Data Analyst.

How did you develop an interest in Data Science? Why did you choose GL to pursue it?

Previously, I was working as a manual tester for a Consulting firm. The job profile involved manual testing for a project of Banking. The role was very limited and monotonous, so I decided not to go deeper into testing. I left my job and moved back to India. As I was from a non-programming background, I was very sceptical to get into coding and related fields. I was looking into various technologies and was suggested by a friend to consider Data Science as an option. I attended many seminars and workshops on Data Science organized by various companies. I developed an interest in this field and was looking for a classroom course. On the recommendation of the same friend, I joined Great learning to pursue PGP-DSE.

Coming from a non-programming background, was it difficult for you to understand the subjects?

Not at all. As most of the students in the batch were from the non-IT background, the course is designed keeping them in mind. The faculties ensured that the basics were covered. I understood that the course is based on Logics, so I slowly developed pace and contrary to my presumptions, I didn’t find it difficult. The faculty put in a lot of time and attention towards us and even repeated the sessions whenever required.

How was your experience of the academic and career support given by GL?

The team was always available, especially Akhila as she helped us thoroughly in preparing for the interviews and gave regular suggestions and feedback for us to improve at the same. Whenever we had any issues, Akhila and the team resolved them at priority.

With Career support, I got to interview with many companies like CTS, Mercedes, etc. Based on my experience, I realized that the curriculum is self-sufficient to crack any interview. The entire course is designed in a way to help us understand the concepts, crack interviews, and guide during the projects. 

What did you like the most in the program?

We were assigned mini projects on the completion of every topic. This gave a lot of hands-on experience of every topic in terms of understanding and its practical application. This hands-on experience on mini projects gave me a lot of confidence and helped me in exams as it gave a recap of all that we had learned in the course. After the completion of the course, during the capstone project, there were many remedial sessions to clear doubts. 

Share your experience of the interview with Mercedes.

The interview was organized by GL at Mercedes’s Bangalore office. The interview included a total of 3 rounds; 2-Technical and 1-HR. The first round included questions based on whatever I had mentioned in my resume and basic questions over Coding, ML, SQL, etc. 2nd round involved questions related to the Business aspect. The final round was an HR round, where they gave me a confirmation after the interview.

Any advice to aspirants who wish to take this course?

They should be confident in sharing what they know and admit to what they don’t know. Give your 100% to every interview thinking that this is the last opportunity as there is a huge competition in the market. There focus should be in developing a strong foundation of whatever they are learning. The interviews are based on basics and focus to test you in your understanding of the field. So, have a stronghold of basics and you will be good enough to crack through it.

 

Upskill with Great Learning’s PG program in Data Science Engineering and unlock your dream career.

 

The placement assistance was excellent – Debashis Gogoi, Data Analyst at Indegene.

There lies a big challenge among engineering students to pick the right field of specialization and build a successful career within the same. Once you understand the core area of interest, upskilling in the same with a relevant course could be a key to unlock your dream career. Here’s how Debashis did it. 

What is your professional background?

I completed my graduation in Civil Engineering from Royal School of Engineering & Technology. After graduation, I worked for 3 months in National Highway project and Gammon India Pvt. Ltd., Guwahati. Then, I moved to Bangalore to pursue the course in Data Science Engineering. Currently, I am working with Indegene as a Data Analyst.

How did you develop an interest in Data Science and How did you choose GL?

I wanted to pursue graduation in Computer Science Engineering but could not as there was no scope for IT in Assam. Based on the available opportunities, I took a course in Civil Engineering. While working during my internship, I realised that I have a passion for Analytics. So I moved from Assam to Bangalore and took some certification courses from Coursera. Meanwhile, I was looking for Data Science courses and got to know BABI is the No.1 course in India for Analytics. Since the only full-time course was of DSE and it was designed for Freshers like me, I took this course.

How was the overall experience with Great Learning?

It was a very nice experience. Before joining the course, I checked the curriculum and found it was very extensive. DSE is a 5-month program and I believe GL did justice in delivering the basics and in-depth understanding. The faculty members were industry experts and they spent a good amount of time with almost all topics. The management was very supportive and the placement assistance was excellent. From CV reviews to Mock interviews, everything made the students really comfortable and industry-ready.

How was your experience at the interview with Indegene?

I got to participate in the placement drives of 6 companies. I got to interview with 3 companies, namely, Kargil Solutions, Evive, and Indegene. With Indegene, there were 2 interview sessions; 1st was a Case Study and 2nd was a Technical Round where they tested me with my basic ML concepts. After the interviews, they offered me the role of Data Analyst. 

Coming from a non-programming background, how easy was it for you to understand the course?

The course is designed with the first week dedicated to Python. Initially, it was a bit tough but then eventually things got easier as we got acquainted with it. The course and the curriculum are very well designed keeping the diversity of the batch in mind.

Any advice for our future aspirants?

My father always quoted me with “Patience and Perseverance always pay”. Along with it, working hard, being focused, and believing in oneself will help anyone achieve the best out of the program. I will suggest them to practice more and participate in a lot of competitions.

 

Upskill with Great Learning’s PG program in Data Science Engineering and unlock your dream career.

GL helped me to kick-start my career – Yeknath Merwade, Associate Analyst at Ugam Solutions

One needs career support the most when they are a fresh graduate. The right direction and support at the right time help multifold in shaping a successful career. What kind of support did Yeknath get? Read on:

What has your professional background been?

I completed my Graduation in Electrical, Electronics & Communications Engineering in 2018 from Belagavi, Karnataka. I then took a course in Data Science at Great Learning, Bangalore and currently, I am working in Ugam Solutions as an Associate Analyst.

How did you develop an interest in Data Science?

I finished my graduation with 58% aggregate score. With this score, I was not eligible to attend interviews for any good role or company. I understood the need to upskill myself as my father suggested me to read about Data Science which has created a lot of buzz. After researching online, I developed an interest in it and got fascinated with what this field can do.

Why did you choose GL to pursue a course in Data Science Engineering?

After viewing the scope and growth opportunities, I immediately started to search for courses. But to choose the best out of them was a task in itself. All I wanted was to take a classroom program as for a fresher it was better compared to online training. I visited GL’s website for weeks and saw it was regularly updated with relevant data and testimonials. I checked the reviews on Google and LinkedIn as well. Finally, I looked at the faculty profiles on LinkedIn and saw their experience. I understood that GL is the best institute in India to study Data Science, so I took up the course here. 

What did you like the most in the program?

There were many things that I loved about the program.

  1. The Faculty: Since I looked at the LinkedIn profiles of almost all the teaching professionals, I got to know that they all were Industry experts and had a great experience in their respective fields. When I enrolled myself for the course, I was surprised to see how grounded and friendly they were. Also, they taught us everything from scratch. 
  2. Course-Curriculum: The course is well designed and well structured. The curriculum is exhaustive and gave me a good understanding of the domain. The course includes what is needed by the industry and everything is accommodated in the syllabus.
  3. Career Assistance: I got to sit on campus drive of 7 companies and got shortlisted in all of them. Apart from this, the CV reviews and Mock Interviews helped me develop confidence and crack interviews. Also, they organized Bootcamps for the students and helped us in all aspects. There were ample opportunities and it got us placed.

Overall it was a nice experience as I got good friends and faculty with whom I learned a lot and I am still in touch with them. I feel very grateful to GL, that helped me to kick start my career.

Being from a non-programming background, did you face any issues with the course or the transition?

Initially, it was very hard for me to adjust to the syllabus as I was not at all familiar with Coding or programming. The first week of the course started with Python, which was a new thing for me. Here, I would like to mention that the teaching faculty boosted my confidence by mentioning that “It is not rocket science and is easy to learn”. After the EDA session, I felt self-motivated and realised that irrespective of any branch, one can achieve success in their ventures. Slowly things started to fall in place. I was in regular sync with sessions, and the regular exams and quizzes kept us in constant touch of the topics. In the end, everything was good and great.

Share your experience of interviewing with Ugam?

I had 4 rounds of the interview; An SQL Test of 30 minutes duration, followed by a Case Study of 30 minutes duration again, a Technical round and finally an interview session with Vice President and HR. The technical round involved questions around my Project mentioned in the Resume and general technical questions to check my understanding of algorithms. With the VP, the interview was to check how my understanding can contribute to the Analytics team of Ugam and general questions from the HR. After the interview, I received a job confirmation from them. 

Any advice to our future aspirants of this course.

I would like to suggest to prepare well on Stats and SQL. The material is self-sufficient and includes in-depth content and curriculum. The placement assistance is superb and helps everyone in getting placed. So there is no need to panic for anything. Also, focus on your project as all my interview questions revolved around my Capstone Project.

 

Upskill with Great Learning’s PG program in Data Science Engineering and unlock your dream career.

I got to interview with 3 companies – Pushpendra Nathawat, Programmer Analyst at Cognizant

Finance has evolved to position itself as an important business function. Given the nature of this domain, it overlaps with analytics in many areas. Finance professionals and executives are finding new ways to leverage from this overlap and increase the value of this vertical in their organizations. 

What is your professional background?

I had completed my MBA from Tapmi School of Business in the year 2015. I then joined Vodafone and worked as a Relationship Manager for 10 months. I switched to HDFC and worked for over 1.75 yrs as an Assistant Manager. Currently, I am working with Cognizant as a Programmer Analyst.

Why did you think of upskilling? Why did you choose Great Learning?

I did an MBA with Finance as my specialization and while working with HDFC, I enrolled myself in Financial Risk Course with IIM Kashipur. Though I had good knowledge in Finance Domain, I had no understanding of Coding or Data Science. I felt the need to upskill and checked for the courses. While searching I found high recommendations for GL. So I left my job in Jaipur and moved to Bangalore to pursue a full-time program in Data Science Engineering with Great Learning.

What did you like most about the program?

The management, staff, and the faculty, everyone was very helpful. The faculty took a great deal of interest in teaching students and gave a good explanation of every topic. The management was very supportive in providing any assistance whenever the batch needed extra sessions or special classes for having a better understanding of the programming subjects. 

How was your overall experience at Great Learning?

Since I was from a non-programming background, initially it was a bit difficult to follow the specific modules. But later with the help of faculty, I could cope up with the subjects and it became easier to understand and manage. The faculty was very helpful in providing material and guidance, especially in my lacuna. They took extra effort in organizing classes over those areas during the weekends. Since I was very new to Data Science, I had to improvise a lot in terms of my CV & Interview performance. The Career assistance provided by GL helped me prepare an impressive CV & mock interviews prepped me to crack interviews.

Share your experience of Career fair organized by GL?

I got to interview with 3 companies; Kinara Capital, Credi India, and Cognizant. I cleared the interview with CTS which involved 3 rounds; 2 in Technical of 45 minutes duration each and 1 HR round of interview on the same day. The technical interviews involved testing my knowledge of Machine learning. I got the job confirmation on the same day.

 

Upskill with Great Learning’s PG program in Data Science Engineering and unlock your dream career.

5 Non-Technical Skills You Must Have to Become a Data Scientist

While you can find plenty of stuff on the internet about the skills required to become a data scientist, chances are you have already realized that getting started in data science is as hard as pulling off a 2-minute plank after being inactive for 2 years. But if you have the perseverance of a polar bear standing still with baited breath for 18 hours at a stretch to hunt or the resolve of Bruce Lee, chances are that you will make it.

Now, the skillset of a successful data scientist will comprise both technical and non-technical skills. While technical skills like programming and quantitative analysis are highlighted, it is easy to undervalue the impact of the non-technical skills. So, before we go on to the technical stuff, here is a list of 5 non-technical skills that you must possess:

  1. Communication – One of the most important skills to have is effective business communication. Whether it is understanding the business requirements or the problem at hand, probing stakeholders for more data, or communicating insights, a data scientist needs to be persuasive. “Storytelling,” as the data scientists call it means that analytical solutions are communicated in a clear, concise, and to-the-point manner so that both technical and non-technical people can benefit from it. Data visualization and presentation tools are widely employed by data scientists for their graphic appeal and easy absorption by all teams in the organization. Often underestimated, this is one of the most important skills for the simple reason that all statistical computation is useless if the teams can’t act upon it.
  2. Data-Driven Decision Making – A data scientist will not conclude, judge, or decide without adequate data. Scientists need to decide their approach to a business problem in addition to deciding several other things like where to look, what tools and techniques to use, and how to visualize and communicate it in the most effective possible way. The most important thing for them is to ask relevant questions, even if they seem far-fetched. Think of it as a child exploring all his surroundings to draw conclusions. A data scientist is pretty much the same.
  3. Mathematical and Statistical Acumen – A data scientist will never thrive if he/she doesn’t understand what test to run when and how to interpret their findings. They need a solid understanding of algebra and calculus. In good old days, Math was a subject based on common sense and the need to resolve basic problems based on logic. This hasn’t changed much, though the scale has blown up exponentially. A statistical sensibility provides a solid foundation for several analysis tools and techniques, which are used by a data scientist to build their models and analytic routines.
  4. Teamwork – Another feather in the cap that data scientists can’t do without is teamwork. While it may seem they can work in isolation, they are deeply involved in the organization at different levels. On one hand, they will have to collaborate with the teams to understand their requirements, gather feedback to reach benefiting solutions, on the other hands they will have to work with fellow data scientists, data architects, and data engineers to perform their tasks well. The culture in a data-driven organization will never be that of the data science team working in isolation; rather the team will have to inculcate the same characteristics across the organization for the best utilization of insights they draw for various departments.
  5. Intellectual Curiosity and Passion – This is a tad-bit cliched but true. Data scientists are passionate about their work and have an inconsolable itch to use data to find patterns and provide solutions to business problems. They often have to work with unstructured data and rarely know the exact steps they need to take to find valuable insights that lead to business growth. Sometimes, they don’t even have a clear problem to work with, just signs that there is something wrong. That’s where their intellectual curiosity guides them to look in areas no one else has looked in. You don’t need to read “How to think like Sherlock,” just ask a data scientist!