Learning at par Industry-standards with Industry experts made my Career Transition possible
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.
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.)
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
Database and querying
Data warehousing solutions
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
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
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
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
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.