Company: Accenture Profile: Big Data Engineer Domain: Healthcare & Public Sector Location: Hyderabad Experience: 1.5 Years
Current Profile
Company: The Math Co. Profile: Data Scientist Domain: IT Location: Bangalore
My journey into Data Science
Why Data Science?
Everyone in the IT industry knows Machine Learning is the future. Everything will be automated soon and if you can’t catch up, your job will be outdated soon. I realized and accepted that I will have to buck up. Coming from Big Data background, Data Science was a simple choice.
Why Dimensionless?
Honestly, I thought I could learn through free courses, so for almost a year, I tried to learn by myself online. I know I wasted a year, but I guess every IT person must’ve tried learning that way while getting to know new technology. Though there was a lot of knowledge around, it’s not in-depth. Like they give you the algorithms to study, but no one tells you the maths and logic behind those algos. That’s why I wanted a proper coaching class where someone can go in a step deeper and teach me the logic.
Also, since I was working, I wanted something online and something that did not cost a lot.
After checking plenty of courses, I came across Dimensionless. The reviews were all-star, the fee was reasonable, and the course curriculum and duration looked justified. I attended the Demo, it was quite impressive. For me, the decision-making point was that they started the course with the basics of Stats and Programming, so I could start learning from scratch.
Experience with Dimensionless?
The program was very flexible and highly interactive. Every day I had the option to join the class in the morning or the evening. Later a recording of the class was also available even if I missed my class. And 3 hours of self-study on the weekend was more than enough to cover up the weekly syllabus.
Frankly, implementation of ML on data does not involve much coding, rather it is about the logic behind the algorithm. Dimensionless helped me understand what’s going on in the backend.
Career Transition to Data Science
Once I was comfortable in the course, the transition felt natural. And after solving case studies under the guidance of Dimensionless I was able to smoothly switch to a Data Science profile within the company itself.
To start my career transition, first I got a project with Dimensionless and got some hands-on experience along with my job.
Initially, I was trying to transition within my company to gain some experience and feel more confident but I was not getting a release from the project due to company policies. But I did not want to get stuck and decided to move out. So I started giving interviews outside and actually ended with almost 100% hike in my salary.
The interviewers mostly asked questions on understanding of these algorithms. There were 2-3 hours of theory at Dimensionless before covering the algorithms, this made it simpler to understand and implement the algorithms. It helped me a lot during the interviews.
Through Dimensionless, I got selected in Motilal Oswal as a Data Scientist in Mumbai. Later I got 2 more offers, one from ePay and other from The Math Co., and as you already know… I took the one at The Math Co. with a One Hundred Percent salary hike!
I strongly believe that as success comes to those who reach for it, similarly career-growth comes by constantly upgrading oneself.
Company: Syntel Profile: Associate Consultant Project: Reporting and BI Location: India
Current Profile
Company: TCS Profile: Business Analyst Project: Data Science Location: India
My journey into Data Science
Why Data Science?
I like to keep myself updated with the latest trends in the IT industry. Back in 2015, when I started my career, my choices were Data Science or Cloud Computing. After a bit of research about these domains, it was clear that with my analytical and logical skills I could do really well in Data Science.
Why Dimensionless?
One of my project-colleagues enrolled in the Data Science program at Dimensionless and he really liked the course. At first, I was a little sceptical about an online course since I am used to learning in a physical classroom. As my colleague was completing the course, he got transferred to a Data Science project internally!
That was an eye-opening moment for me. I attended a class along with him, all the students were asking doubts and getting it resolved. That helped me make the decision.
Experience with Dimensionless?
Even more comfortable than physical classes. The best part was that I could get my doubts resolved at any time. The teachers were helping and kept the classes very interactive. This always kept me motivated to self-study and practice, study Data Science projects on my own. I also got a lot of help from their alumni groups. Even to this day, I am in touch with my mentors at Dimensionless.
Career Transition to Data Science
Once I was comfortable in the course, the transition felt natural. And after solving case studies under the guidance of Dimensionless I was able to smoothly switch to a Data Science profile within the company itself.
I think if you follow the curriculum and course path along with gaining as much project-related knowledge as possible, you cannot go wrong with Data Science. Keep learning, keep upgrading.
Learning at par Industry-standards with Industry experts made my Career Transition possible
Background
Education: BE in Electrical
Previous Profile Company: CGI Profile: Software Engineer (Mainframe) Project: John Hancock (Manulife) Insurance Services Domain: Insurance Location: India
Current Profile Company: CGI Profile: Lead Business Analyst Project: ERP and BI Techno-functional SME Domain: Finance and HRM Location: India
My journey into Data Science
Why Data Science?
Early on in my career, I learnt that the IT industry is constantly upgrading. One of my projects was shut down because the client wanted to upgrade to newer tech, in another company the entire project-team was asked to shift or upgrade with the latest tech. Since then, upgrading with the industry has been my core mantra to stay ahead with the times. After 3 years in IT as a Mainframe developer, I switched to Data Warehousing. After that, progression to Data Science was the natural course.
Why Dimensionless?
I checked a lot of courses on other portals like Coursera and Udemy etc. But I knew that what I needed was a more personalized and interactive course that would give me practical knowledge. My colleague did a course through Dimensionless Techademy. He described the course and frankly, I found it hard to trust him since other institutes with similar features were priced comparatively high. After a lot of research and contemplation, I decided to take a Demo class. Looking at the teaching methodology and course content, by the end of the demo I was convinced to join them.
Experience with Dimensionless?
Teachers are highly qualified and industry-experienced
These were all met at Dimensionless, so I am completely satisfied. All these features are hard to find in any other online courses. The teachers then became my mentors and still help me even after the completion of my course.
I am planning to do the AI specialization course with them next.
Career Transition to Data Science
From a Mainframe Developer in Insurance domain to a Lead Business Analyst in ERP and BI domain, and now entering into the Data Science and Advanced Analytics field, my career has taken a complete 360-degree turn. I am applying whatever I learnt to my work in real-time. Apart from theory and practical hands-on, industry case-studies and domain-related use-cases helped me a lot. I’ve finally cleared an interview for an internal-shift (At the time of this interview.)
Disruption is the new norm. The only way to keep up progress is constant up-gradation with technology.
Reports suggest that around 2.5 quintillion bytes of data are generated every single day. As the online usage growth increases at a tremendous rate, there is a need for immediate Data Science professionals who can clean the data, obtain insights from it, visualize it, train model and eventually come up with solutions using Big data for the betterment of the world.
By 2020, experts predict that there will be more than 2.7 million data science and analytics jobs openings. Having a glimpse of the entire Data Science pipeline, it is definitely tiresome for a single human to perform and at the same time excel at all the levels. Hence, Data Science has a plethora of career options that require a spectrum set of skill sets.
Let us explore the top 5 data science career options in 2019 (In no particular order).
1. Data Scientist
Data Scientist is one of the ‘high demand’ job roles. The day to day responsibilities involves the examination of big data. As a result of the analysis of the big data, they also actively perform data cleaning and organize the big data. They are well aware of the machine learning algorithms and understand when to use the appropriate algorithm. During the due course of data analysis and the outcome of machine learning models, patterns are identified in order to solve the business statement.
The reason why this role is so crucial in any organisation is that the company tends to take business decisions with the help of the insights discovered by the Data Scientist to have an edge over the company’s competitors. It is to be noted that the Data Scientist role is inclined more towards the technical domain. As the role demands a wide range of skill set, Data Scientists are one among the highest paid jobs.
Core Skills of a Data Scientist
Communication
Business Awareness
Database and querying
Data warehousing solutions
Data visualization
Machine learning algorithms
2. Business Intelligence Developer
BI Developer is a job role inclined more towards the Non-Technical domain but has a fair share of Technical responsibilities as well (if required) as a part of their day to day responsibilities. BI developers are responsible for creating and implementing business policies as a result of the insights obtained from the Technical team.
Apart from being a policymaker involving the usage of dedicated (or custom) Business Intelligence analytics tools, they will also have a fair share of coding in order to explore the dataset, present the insights of the dataset in a non-verbal manner. They help in bridging the gap between the technical team that works with the deepest technical understanding and the clients that want the results in the most non-technical manner. They are expected to generate reports from the insights and make it ‘less technical’ for others in the organisation. It is noted that the BI Developers have a deep understanding of Business when compared to Data Scientist.
Core Skills of a Business Analytics Developer
Business model analysis
Data warehousing
Design of business workflow
Business Intelligence software integration
3. Machine Learning Engineer
Once the data is clean and ready for analysis, the machine learning engineers work on these big data to train a predictive model that predicts the target variable. These models are used to analyze the trends of the data in the future so that the organisation can take the right business decisions. As the dataset involved in a real-life scenario would involve a lot of dimensions, it is difficult for a human eye to interpret insights from it. This is one of the reasons for training machine learning algorithms as it easily deals with such complex dataset. These engineers carry out a number of tests and analyze the outcomes of the model.
The reason for conducting constant tests on the model using various samples is to test the accuracy of the developed model. Apart from the training models, they also perform exploratory data analysis sometimes in order to understand the dataset completely which will, in turn, help them in training better predictive models.
Core Skills of Machine Learning Engineers
Machine Learning Algorithms
Data Modelling and Evaluation
Software Engineering
4. Data Engineer
The pipeline of any data-oriented company begins with the collection of big data from numerous sources. That’s where the data engineers operate in any given project. These engineers integrate data from various sources and optimize them according to the problem statement. The work usually involves writing queries on big data for easy and smooth accessibility. Their day to day responsibility is to provide a streamlined flow of big data from various distributed systems. Data engineering differs from the other data science careers as in, it is concentrated on the system and hardware that aids the company’s data analysis, rather than the analysis of data itself. They provide the organisation with efficient warehousing methods as well.
Core Skills of Data Engineer
Database Knowledge
Data Warehousing
Machine Learning algorithm
5. Business Analyst
Business Analyst is one of the most essential roles in the Data Science field. These analysts are responsible for understanding the data and it’s related trend post the decision making about a particular product. They store a good amount of data about various domains of the organisation. These data are really important because if any product of the organisation fails, these analysts work on these big data to understand the reason behind the failure of the project. This type of analysis is vital for all the organisations as it makes them understand the loopholes in the company. The analysts not only backtrack the loophole and in turn provide solutions for the same making sure the organisation takes the right decision in the future. At times, the business analyst act as a bridge between the technical team and the rest of the working community.
Core skills of Business Analyst
Business awareness
Communication
Process Modelling
Conclusion
The data science career options mentioned above are in no particular order. In my opinion, every career option in Data Science field works complimentary with one another. In any data-driven organization, regardless of the salary, every career role is important at the respective stages in a project.
