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Data Engineer to Data Scientist : Career Switch

Data Engineer to Data Scientist : Career Switch

Success Story: Switching from Data Engineer to Data Scientist

I believe anyone with patience, passion and guidance can learn Data Science.

– Kranthi Bandi

Background

Education: M. Tech Mobile and Satellite Communications

Previous Profile  >>> Current Profile
Designation: 
Profile: Data Engineer
Domain: Enterprise Software
Experience: 3 years

Designation: 
Profile: Data Scientist
Domain: Enterprise Software

My journey into Data Science

Why Data Science?

Being a Data Engineer, I always felt like I belonged to the field of Data. I could see how the tech was moving. All the businesses are becoming Data-oriented and automation is the need of the hour. Once Cloud Technology is stable, Artificial Intelligence is going to dominate the trend. During my Masters, I had Statistics as a subject and used it heavily in a project. My Masters’ thesis was with MATLAB, using concepts and fundamentals of Data Science.

So, I was sure of getting into Data Science. 

Why Dimensionless?

While looking for a program, the only challenge was finding a class with a well-balanced curriculum. I tried understanding the curriculum of a lot of classes, some of them had a very high-level curriculum while others were not covering any relevant knowledge. Also, I did not want to go to any well-known classes because teachers aren’t able to give personalized attention.

Since this is a serious subject, the only way I could be sure about any course would be if a credible source vouched for it. A friend (an ex-student of Dimensionless) strongly recommended the Data Science course from Dimensionless. Their curriculum was balanced for anyone who wanted to start in Data Science. In fact, the first demo I attended was on Statistics. It is essential to start with Statistics and Mathematics to grasp Data Science fully.

If you see the progression, going from being a Data Engineer to being Data Scientist was an obvious step forward.

Experience with Dimensionless?

Good course structure and in-depth teaching were 2 key factors that impressed me at Dimensionless. Learning Data Science takes time and effort from both the teacher and the students. We got that at Dimensionless. 

Also, people coming from a Data background are usually weak at programming. The teachers made it easy for us to understand and learn Python. 

The teachers covered a lot of ground for all the subjects and they were always available for clearing our doubts. I was satisfied with the course structure and the teaching method.

Career Transition to Data Science

Luckily, in my previous company, they were building an AI team and testing various projects. At the end of the course, I got support from Dimensionless to prepare with Mock Interviews.

I applied to be a part of the AI Team at my company and got selected through a written test and interview. The exposure was immense. I got to work on multiple projects from scratch. We discussed Use Cases and projects in-depth, covering even the business aspects of it. 

After that, I knew I could comfortably face any Data Science or AI interview. 

Now, if anyone asks me how much time it takes to become a Data Scientist, I first ask them “How dedicated are you?”.

 

There are many more like Kranthi who have switched to Data Science from different domains. Read their success stories here.

Want to know whether such a Career Transition is possible for you?
Follow this link, and make it possible with Dimensionless Techademy!

Furthermore, if you want to read more about data science, you can read our blogs here.

 

Data Science Trend Forecast: 2020

Data Science Trend Forecast: 2020

Taking into consideration the positive trends of Data Science from previous years, there lies an immense well of possibilities that awaits us in the future, that is, the upcoming year 2020. Some of these Data Science Trend Forecast for 2020 can be foreseen as follows:

  • Augmented Analysis

Complicated code and extensions will no longer be required to get deep insights from data. The augmented analysis helps layman users/analysts (in machine learning/data science) to make use of AI to analyze data. This will change the way data is consumed, created and shared across all data-intensive fields. Already several BI and analytics tools are trying to implement AI assistance full force in their platforms.

  • Continuous/ Real-time Intelligence

There is intensive activity ongoing every second in real-time platforms. If through some method, one can plug into this data, real-time user experience can be enhanced manifold. Continuous or real-time intelligence aims to do just that by analyzing data in real-time so that instant results can be provided to the user while he is still surfing the platform. It can also help increase profit margins by re-aligning the platform as per the observed interaction of the user.

  • NLP

Natural Language Processing is a very important segment of Artificial Intelligence since most real-world data are in text or voice format. To process such data, advanced NLP techniques are required which are being innovated with each passing data. Today, we can read, understand, classify and even create unique text documents with the help of machines. Further developments like intelligent summarization, entity recognition and task management using text input and much more are expected to happen, owing to the intense research and increasing data-science experts choosing NLP specialisation.

  • Conversational technology

There has already been a visible surge in the performance of voice assistants in 2019. In 2020, it is expected to further improve such that the conversational systems become more sensitive to the human language and also more humane in their response. By more humane, it will mean that the systems can keep track of previous responses and questions (which is not a very developed feature in any voice assistant in the market to this day). Also, most client interactions are expected to be taken over by conversational technology, thus, increasing response rate and efficiency.

  • Explainable AI

The last decade has seen massive growth in AI aided decisions for sure, but it has been a persistent problem to be able to explain these decisions or why the AI wants to go a certain way instead of another. Recently, however, a lot of research has increased the scope of explainable AI. 2020 can further be invested in understanding problems like say, how and why a certain neural network arrived at a certain decision. This will indefinitely increase the faith of clients on adolescent technology.

  • Persistent memory/ In-memory computation

In-memory computing or IMC can deliver extremely high-performance tasks due to optimized memory architecture. It also has become more feasible due to the decreasing expense of memory which owes credit to constantly emerging innovations.

  • Data Fabric

Data Fabric helps in the smooth access and sharing of data in distributed environments. It is usually custom made and helps in the transfer and store of data, data pipelines, APIs and previously used data services that have a chance of being re-invoked. Trusted and efficient data fabric can help to catalyze data science pipelines and reduce delays in customer-client interaction/iterations.

  • Advances in Quantum Computing

The research in Quantum Computing has a very high momentum at the moment. Even though the whole architecture of Quantum computing is at a very basic stage, increased investments and research are helping the field to grow by inches every passing day. A quantum computer is said to perform calculations which will take general computers a few years, in just a few seconds! As remarkable as it sounds, it can bestow superpowers to mankind! Imagine munching on years and years of historical data to arrive at conclusions about the future in just a few seconds. A whole lot of astonishing things await us, and we must be blessed to be a part of this century.

It is expected that India’s job openings in the analytics sector will double to about 200000 or two lakh jobs in 2020. Here is what 2020 for job seekers in data science will look like:

  • Fields like finance, IT, professional services and insurance will see a boom in demand for data science and analytics.
  • Having analytics skills like MapReduce, Apache Pig, Machine learning and Hadoop can provide an edge over other competitors in the field. The most fundamental in-demand skills will be Python and Machine Learning. Statistics is an added advantage.
  • Vacancies for roles like data developers, data engineers and data scientists will go over 700,000 by 2020.
  • The most promising sectors that will tend to create increasing opportunities include Aviation, Agriculture, Security, Healthcare and Automation.
  • The average salaries in India in development roles like Data Scientist or Data Engineer will range from 5 to 8 Lakh per annum.
  • The average salaries in India in management/strategizing roles like data architect or business intelligence manager will range from 10 to 20 Lakh per annum.

As exciting as all of it sounds, there is always a bag of unforeseen advancements that are bound to take us all by surprise, as has always happened with Data Science and AI in the past. So, hold tight for yet another mind-boggling ride through the lanes of technology this 2020!

Best of 2019 Trends in Data Science

Best of 2019 Trends in Data Science

Data Science has seen a massive boom in the past few years. It has also been claimed that it is indefinitely one of the fastest-growing fields in the IT/academic sector. One of the most hyped Trends in Data Science this year was that the sector saw a major hike in jobs as compared to the past years!

Such an unprecedented growth owes all its dues to the unimaginable benefits that artificial intelligence has brought to the plate of mankind for the very first time. It was never before imagined that external machines could aid us with such sophistication as is present today. Owing to this, it is imperative that an individual, irrespective of his/her calling, must have at least a superficial knowledge about the past advances and future possibilities of this field of study. Even if it is the job of scientists and engineers to figure out solutions using machine learning and data science, the solutions, undoubtedly is bound to affect all our lives in the upcoming years. Moreover, if you are planning to plug into the huge well of job openings in data science, exploring the past and upcoming trends in this field will surely take you a step ahead.

Looking back on the achievements of the year 2019, there is much which has happened. Here is a brief glimpse of what Trends in Data Science of 2019 looked like:

  • Accessible AI

The once-popular belief that AI technology was only meant for high-scale and high-tech industries, is now an old wives’ tale. AI has spread so rapidly across every phase of our lives, that sometimes we do not even realize that we are being aided by AI. For instance, recommendations that we get on online forums are something we have become very used to in recent times. However, very few have the conscious knowledge that the recommendations are regulated by AI technology. There are also several instances where a layman can use AI to get optimized outputs, like in automated machine learning pipelines. We even have improvised AI-aided security systems, music systems and voice assistants in our very homes! Overall, the impact of AI in everyday lives saw a massive boost in 2019, and it is only bound to increase.

  • The rapid growth of IoT products

As was already forecasted, the number of machines/devices which came online in 2019 was immense. Billions were invested in research to back the uprising IoT industry. Today it is nothing out of the ordinary to control home appliances like television and air conditioners with our smartphones or lock our and unlock our cars from even the opposite end of the globe. Bringing devices online not only makes the user experience far smoother but also generates crucial data for analysis. With such data, several unopened gates can be explored across several domains. The investments and count of IoT devices are expected to go up at an increasing rate in the upcoming years.

  • Evolution of Predictive Analysis

The concept of predictive analysis is to use past data to learn recurring patterns, such that it can predict outcomes of future events based on the patterns learnt. Today, with increasing data it becomes extensively important to make use of optimized predictive solutions. Big data comes into picture here and significant advancements have been made in 2019 about it. Tools like PySpark and MLLib have helped scale simple predictive solutions to extensive data.

  • Migration of Dark Data

Dark data is very old data which has probably been sitting in obsolete archives like old systems or even files in storage rooms! There is a general understanding that such unexplored data can show us the way to crucial insights about past trends which can help grab useful opportunities and even avoid unwanted loopholes. Therefore, there has been visible initiatives to make dark data more available to present-day systems with the help of efficient storage and migration tools.

  • Implementation of Regulations

In 2018, General Data Protection Regulation (GDPR) brought in a few data governance rules to emphasize the importance of data governance. The rules were laid down so fast that even at the year-end, several companies dealing with data are still trying to comply wholly with all the principles laid down. These principles have not only created a standard for data consumption and data handling domains but are also bound to shape the future of data handling with great impact.

  • DataOps

DataOps is an initiative to bring in some order in the way the data science pipeline functions. It is essentially a reflection of agile and DevOps methods in the field of data science. In 2019, it has been one of the major concerns of management in data science to integrate DataOps into their respective teams. Previously, such integration was not possible since the generic pipeline was still in making or under research. However, now, with a more robust structure, integrating DataOps can mean wonders for data science teams.

  • Edge Computing

As stated by Gartner, Inc. cloud computing and edge computing has evolved to become a complementary model in 2019. Edge computing goes by the concept of “more the proximity (or closeness to the source of computation), better is the efficiency”. Edge computing allows workloads to be located closer to the consumers and thus, reduces latency several-fold.

There is, however, a huge recurring gap when it comes to the need and availability of skilled people who can launch and contribute to these developments significantly. India contributed to 6% of job openings worldwide in 2019, which scales to around 97000 jobs!

The job trends of 2019 looked as follows:

  • BFSI sector had a massive demand for analytics professionals, followed by the e-commerce and telecom sectors. The banking and financial sectors continued to have high demand throughout.
  • Python served as a great skill to attract employers to skilled job seekers
  • A 2% increase in jobs offering over 15 Lakh per annum was observed
  • Also, 21% of jobs demanded young talent in data science, a great contrast to all previous years. 70% of job openings were for professionals with less than 5 years of experience.
  • The top in-demand designations were Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultants, Statistical Analyst and Hadoop Developer
  • Big data skills like Hadoop and Spark were extremely in demand due to the growing rate of data.
  • Telecom industry saw a fall in demand for data science professionals.
  • The median salary of analytics jobs was just over 11 Lakh per annum.

On to 2020 now!

Read more of our blogs here!

MBA to Data Science: Career Transition

Career Jump from MBA to Data Science Success Story

Knowledge is what matters the most in the industry.

– Karthik Sripati

Background

Education: Masters in Business Administration

Previous Profile  >>>Current Profile
Company: Amazon State Street Syntel
Profile: Operations
Designation: Deputy Manager
Domain: BFSI
Experience: 2.2 years
Company: Google
Profile: Data Analysis
Designation: Deputy Manager
Domain: KPO (IT) Software

My journey into Data Science

Why Data Science?

During my MBA days, or even when I was working at Amazon State Street Syntel, I kept hearing about AI & Data Science and the new projects that were coming up. Everyone always got excited when they spoke about Data Science. I was always good at Maths. Coming from a background of Business Studies, data crunching and data analysis always fascinated me. I wanted to have a holistic point of view of the business trends and future forecasting.

I understood that Data Science focuses on Problem Solving, Analytical Skills and Domain Knowledge. In my job also, I was working on Reporting and business analysis. So, that’s where my liking to Data Science began.

Why Dimensionless?

I did a lot of research to find the perfect classes. I checked their reviews and contacted a couple of students who had done their course to be doubly sure and they all verified that the teachers would deep dive into the subjects and give individual attention to all students.

After a discussion based on my profile with the mentors, I was able to get beyond my fears of programming.

In the Demo sessions, I liked their teaching method and the teacher’s knowledge. And, that’s what mattered the most for me.

Experience with Dimensionless?

I was never into any technical role, never even had any technical subjects. Really, I didn’t know how I was going to cope with the coding bit. It was difficult at the start. The classes progressed and I found it easier to understand Python and Machine Learning. By the end of the course, I got quite good at coding. I asked a lot of doubts in the classes and the teachers were always ready to help.

Career Transition to Data Science

With the help of project mentoring and resume-building activities at Dimensionless, I updated my profile. They suggested the apt projects and skills for adding to my profile, which made it a bit relevant to my domain knowledge too.
Frankly, I thought I will have to give a lot of interviews before I crack one. After a walk-in interview, I got the idea about the kind of questions asked in Data Science interviews. I nailed it in the next interview and got placed to a position similar to my previous one.

I’ve learnt from my personal experience that most businesses look for capable people and not certifications.

Do you also want a career transition like Karthik?
Follow this link, and make it possible with Dimensionless Techademy!

Furthermore, if you want to read more about data science, you can read our blogs here.


Database Management to Data Science: Career Switch

Career Transition from Team Lead, Database Management to Data Scientist

I believe it’s never too late to learn and move to a better technology.

– Vikash Kumar Prakash

Background

Education: B. Tech in Computer Science

Previous Profile  >>>Current Profile
Company: BNY Melon
Profile: Oracle, PL/SQL Developer
Designation: Team Lead, Database Management
Domain: Finance
Experience: 8 years
Company: Barclays
Profile: Data Analyst
Designation: Team Lead
Domain: Finance

My journey into Data Science

Why Data Science?

There wasn’t much growth in PL/SQL and Database Management after a certain level. After reaching that level and giving about 8 years to one technology, and it was scary to move to newer technology. So I started looking at how I could build on stuff I already knew. I knew how to use and store the data. Now, I was more interested in the business side of it. I was interested in predictive analytics to help businesses apply this data and get insights from it. Data Science would help me do that. Because of this interest, Machine Learning and Data Science was an obvious choice.

Why Dimensionless?

One of my trusted friends did his course from Dimensionless and recommended it to me. He too was able to transition from Database Management to Data Science.

After checking out the course myself, I found it to be good in every aspect. My main issue was timings, but they provided the flexibility of attending classes in the morning or evening. Plus, there were weekend classes which covered the most syllabus. The faculty looked promising since they were all from Machine Learning background, they had worked in this technology. After the Demo, I was convinced completely.

Experience with Dimensionless?

I had a very good experience with Dimensionless. One thing I really liked was that the faculty gave equal attention to all the students. Even the doubts were resolved quickly by the teachers. Any issues we had were discussed either in the lecture itself or personally too.
We got assignments and case studies that helped us get hands-on experience in the applications of Data Science, which was the most interesting part for me.

Career Transition to Data Science

After the course was done, I started giving interviews. The Placement Assistance really helped me here. Right from resume preparation to mock interviews, Dimensionless prepared us for everything. We were given a set of questions to prepare from and then mock interview would be held. I think these activities boosted my confidence more than anything else.

Within 2 months after the course, I got placed in Barclays as a Data Analyst with R and Python.

Dedicating time to learning new tech is the best way out of an outdated job, no matter how much experience you have. In Data Science, as well as every other advanced technology, the main thing that matters is practical knowledge.

Do you also want career transition like Vikash?
Follow this link, and make it possible with Dimensionless Techademy!

Furthermore, if you want to read more about data science, you can read our blogs here.