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System Engineer to Data Science: Growth Journey

System Engineer to Data Science: Growth Journey

Career Transition from
System Engineer to Data Science Success Story

You are judged based on practical knowledge, regardless of past experience.

– Ruchi Saboo

Background

Education: B. Engineering in Computer Science

Previous Profile  >>> Current Profile
Company: Amdocs
Profile: SQL, UNIX Developer
(Ordering and Billing)
Designation: System Engineer
Domain: Telecom
Experience: 2 years
Company: TSystems
Designation: Consultant
Profile: Data Science
Designation: Data Scientist
Domain: Telecom

My journey into Data Science

Why Data Science?

Day by day, the technology is evolving. I didn’t see myself getting the career growth I wanted with the technology I was working on before (SQL/UNIX). As anyone in IT would know, having a job as a System Engineer in SQL/UNIX these days is very mundane. I thought I wouldn’t survive for long. That was the main motivation to keep myself updated with the latest tech.

When it came to choosing the new tech, I found myself being more keen towards Data Science. It’s very interesting and insightful. It called to my intellectual side. It’s like you’re creatively playing with data and getting business results.

Why Dimensionless?

It’s a funny story. When I decided to go with Data Science, I enrolled in a classroom course mainly because I was never comfortable with Online Classes. It was mostly theory and my experience was neutral.
Since I was already working as a System Engineer using SQL, I had the database part covered at my end.

It was only when I started giving interviews, that I realized that their course curriculum and faculty was sub-par. A lot of the interview questions asked were not even covered in the lectures. When I went back to them with these doubts, they said it was out of syllabus.
Then I tried to learn by myself. I checked out some free courses.

In one of my interviews, I met this guy. He was a fellow candidate and he seemed pretty confident. We got to talking and he told me that he did a Data Science specialization course from Dimensionless. He was so satisfied with the course that I could feel his genuineness. Obviously, I got very excited to know more about Dimensionless.

Next thing I did was I spoke to their counsellors and joined in the next batch itself.

Experience with Dimensionless?

TBH, before taking up this course with Dimensionless, I was convinced that I can only learn properly in a physical classroom. I thought physical classrooms provide more support and are more accessible. Now I am much more comfortable in online training. Online or offline, if the teachers are good and doubts are handled, it doesn’t matter. It was so comfortable to attend classes from anywhere.

I knew why that guy in the interview was so happy with Dimensionless. The doubt-solving was quick. Teachers were available on call too. The sessions had a lot of communication and they were interactive overall. The course content was practical and easy to follow too.

Career Transition to Data Science

After completing the course in 5 months, I did some more self-study because I thought why someone would select me over an experienced Data Science professional.  I delayed applying for jobs. Finally, after some moral support from their mentors and mock interviews with Dimensionless HR, I built up the courage to apply for jobs and give interviews. Among other companies, I applied at TSystems, Wipro and Capegemini, and I got selected in all three!!! Imagine my excitement.

When I started giving interviews I realized that interviewers judge you based on knowledge and not past experience.

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 Ruchi?
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.

 

Network Engineer to Machine Learning Engineer

Network Engineer to Machine Learning Engineer

Career Transition to Data Science Success Story

Hands-on practice is the key to a good start in Data Science!


-Ashish Anand

Background

Education: BE Electronics & Communications

Previous Profile >>> Transition Profile >>> Current Profile
Company: Ericsson
Profile: Network Engineer
Domain: Telecom
Company: Ericsson
Profile: Data Science Analyst
Domain: Telecom
Company: Affine Analytics
Designation: Data Science Associate
Profile: Sr. Business Analyst
Domain: Retail

My journey into Data Science

Why Data Science?

In my previous profile, the work was very manual and monotonous, doing the same thing time and again. I was not satisfied with my work. I knew I had to change something. At that time, I was considering Data Science as well as Big Data since these two have the maximum scope and good pay as well. Maths and Stats were always my strong points and I am technically strong too. Considering this, Data Science looked like an exciting journey.

Why Dimensionless?

I tried to learn by myself through other online learning classes. I also took a course through Udemy. The pre-recorded videos were a drag. It was not interactive and I had a lot of doubts. That is when I came across Dimensionless.

Compared to other courses, this one had a detailed syllabus with 200 hours Live and Interactive training. I was sure about joining this course as soon as I attended the Demo. I took a lot of other courses but there was something or the other missing in them. With Dimensionless, I found all of it in one place.

Experience with Dimensionless?

It was exactly as I wanted it, a good mix of theory and practical. The classes were very interactive and teachers were always available for doubt-solving. They also helped me with my additional self-studies. I went to them with topics that were not in the syllabus and still got support. There was ample pre-recorded content as well, that we could refer to after the live classes.

Career Transition to Data Science

I’ll be honest, I didn’t get through any interviews at first. With mentors at Dimensionless, I got feedback on my performance at the interviews. The career mentoring facility helped me understand what I was interested in and which jobs I should be applying to accordingly. The HR guided me about which companies and jobs I need to apply to, weigh the advantages and disadvantages of the profiles.

This started giving me confidence. Finally, I got shortlisted for multiple companies, one of them was through Dimensionless. I joined Affine Analytics with almost 70% hike from my previous job. I am thankful to Dimensionless for all of this.

I like my work, I can work for 10 hours straight and not feel stressed.

 

Electronics Engineer to Machine Learning Engineer

Electronics Engineer to Machine Learning Engineer

Career Transition to Data Science Success Story

Data Science and AI are career choices for everyone who want to be excited about their jobs and learn new things!


Jagrati Valecha

Background

Education: B. Tech Electronics & Communications

Previous Profile

Company: Honeywell Aerospace
Profile: Electronics Engineer
Domain: Aerospace
Experience: 3.4 years

Current Profile

Company: Honeywell Aerospace
Profile: Machine Learning Engineer
Domain: Aerospace

My journey into Data Science

Why Data Science?

I was an Electronics Engineer in Aerospace and I couldn’t see any growth in my domain. The opportunities to learn new things were limited, which lead to no growth and it became less and less exciting to me every day.

I realized I had to switch to software and upgrade my skills as per the demand to stay relevant throughout my career. I spoke with many of my peers, did some research, and found a few career choices viable as per the market right now. Data Science looked like an interesting career choice but I still remember having so many doubts!

Why Dimensionless?

As I was researching, I came across Dimensionless on Google and enrolled for a Demo session. I asked them all the questions and doubts I had about taking up Data Science. I literally bombarded them with questions like…
How difficult is it going to be without having much of programming knowledge?
Is having no previous work-experience okay? And does it count?
How does Data Science fit in my domain (Aerospace)?

They answered all of it with patience and logic. I also had a one-on-one career counselling session with their counsellor.

Then there were other things to consider, like if I can attend the classes regularly, if the fee is viable, if I can get back to studies after such a long break, etc.

So, I went for the Experience and Pay option. Attended the classes for 2 weeks, I liked their methodology, and since I could understand what I was learning, I found myself attending lectures regularly along with my work and without being too stressed. And then, I continued and completed the entire course.

Experience with Dimensionless?

The course structure and methodology is not too stressing. The teaching pace wasn’t too stressing. Doubt-solving was immediate. I could get my doubts solved during the class, in the doubt-solving sessions or even one-on-one with the respective teacher. And trust me, coming from a non-programming background, I had a lot of doubts. Mentors and teachers were always available answering doubts.

Career Transition to Data Science

Resume-building sessions made me understand how to steer my career towards Data Science in Aerospace. When mentors started giving us projects, I got to choose projects from my domain so I could build upon my experience. This helped me get ready for interviews more than anything. Knowing theory is one thing, but the interviewers ask very technical and practical questions.

About only 70% of the course was done when I applied for an internal-switch at my company and got accepted. In fact, I even got a promotion and didn’t have to apply anywhere else.

I went from an Electronics Engineer to Machine Learning Engineer, within just 4 months into the course. With sincere efforts, you too can become a Data Scientist.

Go beyond doubts, ask questions and keep learning.


Making a Career Transition: Business Intelligence to Data Science

Making a Career Transition: Business Intelligence to Data Science

Career Transition to Data Science Success Story

I realized the only thing stopping my career-growth was my own hesitancy


Kantesh Biswas

Background

Education: BE in IT

Previous Profile

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.

 

Top 5 Careers in Data Science You Need to Know About

Top 5 Careers in Data Science You Need to Know About

 

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

  1. Communication
  2. Business Awareness
  3. Database and querying
  4. Data warehousing solutions
  5. Data visualization
  6. 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

  1. Business model analysis
  2. Data warehousing
  3. Design of business workflow
  4. 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

  1. Machine Learning Algorithms
  2. Data Modelling and Evaluation
  3. 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

  1. Database Knowledge
  2. Data Warehousing
  3. 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

  1. Business awareness
  2. Communication
  3. 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.

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data courseThis course will equip you with the exact skills required. 

Additionally, if you are having an interest in learning Data Science, click here to start the Online Data Science Course

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