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.
Data science is a booming industry, with potentially millions of job openings by 2020, according to the latest analyst’s business predictions. But what if you want to learn data science without the heavy cost of a postgraduate degree or the US university MOOC specialization? What is the best way to prepare for this upcoming wave of opportunity and maximize your chances for a 100K+ USD (annual) job? Well – there are many challenges that stand before you in such a case. Not only is the market saturated with an abundance of existing fresh talent, but most of the training you receive in college has no relationship to the actual type of work you get on the job. With so many engineering graduates passing out every year from so many established institutions such as the IITs, how can you hope to realistically compete? Well – there is one possibility you can choose if you wish to stand out from the rest of the competition – high-quality data science programs or courses. And in this article, we are going to list the top ten advantages of choosing such a course compared to other options, like a Ph.D., or an online MOOC Specialization from a US university (which are very tempting options, especially if you have the money for them).
Top Ten Advantages of Data Science Certification
1. Stick to Essentials, Cut the Fluff.
Now if you are a professional data scientist, no one expects you to derive any AI algorithms from first principles. You also don’t need to extensively dig into the (relatively) trivial history behind each algorithm, nor learn SVD (Singular Value Decomposition) or Gaussian Elimination on a real matrix without a computer to assist you. There is so much material that an academic degree covers that is never used on the job! Yes, you need to have an intuitive idea about the algorithms. But unless you’re going in for ML research, there’s not much use of knowing, say, Jacobians or Hessians in depth. Professional data scientists work in very different domains while compared to academic researchers or academic counterparts. Learn what you need on the job. If you try to cover everything mentioned in class, you’ve already lost the race. Focus on learning bare essentials thoroughly. You always have Google and StackOverflow to assist you as long as you’re not writing an exam!
2.Learning from Instructors with Work Experience, not PhD scientists!
Now from whom should you receive training? From PhD academics who’ve never worked on a real professional project but have published extensively, or instructors with real-life professional project experience? Very often, the teachers and instructors in colleges and universities belong to the former category, and you are remarkably fortunate if you have an instructor who has that invaluable component called industry experience. The latter category are rare and difficult to find, and you are lucky – even remarkably so – if you are studying under them. They will be able to teach you with context to the job experience in real-life, which is always exactly what you need the most.
3. Working with the Latest Technology Stacks.
Now, who would be better able to land you a job – teachers who teach what they studied ten years ago, or professionals who work with the latest tools available in the industry? It’s undoubtedly true that the people with industry experience can help you to choose what technologies you should learn and master. Academics, in comparison, could even be working with technology stacks over ten years old! Please try to stick with instructors who have work experience.
4. Individual Attention.
In a college or a MOOC with thousands of students, it’s simply not possible for each student to get individual attention. However, in data science programs, it is true that every student will receive individual attention tailored to their needs, which is exactly what you need. Every student is different and will have their own understanding of the projects available. This customized attention that is available when batch sizes are less than 30-odd is the greatest advantage such students have over college and MOOC students.
5. GitHub Project Portfolio Guidance.
Every college lecturer will advise you to develop a GitHub project portfolio, but they cannot give your individual profile genuine attention. The reason for that is that they have too many students and requirements upon their time to be able to spend time with individual project portfolios and actually mentor you in designing and establishing your own project portfolio. However, data science programs are different and it is genuinely possible for the instructors to mentor you individually in designing your project portfolios. Experienced industry professionals can even help you identify ‘niches’ within your fieldin which you can shine and carve out a special brand for your own project specialties so that you can really distinguish yourself and be a class apart from the rest of your competition.
6. Mentoring even After Getting Placed in a Company and Working by Yourself.
Trust me, no college professor will be able or even available to help you once you get placed within the industry since your domains will be so different. However, its a very different story with industry professionals who become instructors. You can even go to them or contact them for guidance even after placement, which is, simply not something most academic professors will be able to do unless they too have industry experience, which is very rare.
7. Placement Assistance.
People who have worked in the industry will know the importance of having company referrals in the placement process. It is one thing to have a cold call with a company with no internal referrals. Having someone already established within the company you apply to can be the difference between a successful and unsuccessful recruitment process. Every industry professional will have contacts in many companies, which puts them in a unique position to aid you at the time of placement opportunities.
8. Learn Critical but Non-Technical Job Skills, such as Networking, Communication, and Teamwork
teamwork in data science
While it is important to know the basics, one reason why brilliant students do badly in the industry after they get a job is the lack of soft skills like communication and teamwork. A job in the industry is so much more than bare skills studied in class. You need to be able to communicate effectively and to work well in teams, which can be guided by industry professionals but not by professors since they will have no experience in this area because they have never worked in the industry. Professionals will know who to guide you with regard to this aspect of your expertise, since its a case of being in that position and having learnt the necessary skills in the industry through their job experiences and work capacities.
9. Reduced Cost Requirements
It is one thing to be able to sponsor your own PhD doctoral fees. It is quite another thing to learn the very same skills for less than 1% of the cost of a PhD degree in, say, the USA. Not only is it financially less demanding, but you also don’t have to worry about being able to pay off massive student loans through industry work and fat paychecks, often at the cost of compromising your health or your family needs. Why take a Rs. 75 lakh student loan, when you can get the same outcome from a course less than 0.5% of the price? The takeaways will still be the same! In most cases, you will even receive better training through the data science program than an academic qualification because your instructors will have job experience.
10. Highly Reduced Time Requirements
A PhD degree takes, on average, 5 years. A data science program gets you job-ready in a few months time. Why don’t you decide which is better for you? This is especially true when you already have job experience in another domain or you are more than 23-25 years old, and doing a full PhD program could put you on the wrong side of 30 with almost no job experience. Please go for the data science program, since the time spent working in your 20s is critical for most companies who are hiring today since they consider you to a be a good ‘çultural fit’ for the company environment, especially when you have less than 3-4 years experience.
Summary
Thus, its easy to see that in so many ways, a data science program can be much better for you than a data science degree. So, the critical takeaway for this article is that there is no need to spend Rs. 75,000,000+ for skills which you can acquire for Rs. 35,000 max. It really is a no-brainer. These data science programs really offer true value for money. In case you’re interested, please do check out the following data science programs, each of which have every one of the advantages listed above:
A career in data science is hyped as the hottest job of the 21st century, but how do you become a data scientist? How should you, as an aspiring data scientist, or a student who aims at a data science job, prepare? What are the skills you need? What must you do? Fret not – this article will answer all your questions and give you links with which you can jump-start a new career in data science!
Data science as a field is a cross-disciplinary topic. By this, we mean that the data scientist has to know multiple fields and be an expert in many different things. A data scientist must have a strong foundation in the following subjects:
Computer Science
Statistical Research (solid foundation required)
Linear Algebra
Data Processing (data analyst expertise)
Machine Learning
Software Engineering
Python Programming
R Programming
Business Domain Knowledge
The following diagram shows a little bit of the subjects you will need to master to become a high-quality data scientist:
Now unless you have been focused like a laser beam and have deliberately focused your studies in these areas, it is likely that you will not know one or more of the topics given above. Or you may know two or three really well but may not be solid in the rest. For example, you could be a computer science student who knows mathematics but not statistics to the in-depth level that Analysis of Statistical Research requires. Or you could be a statistician who has a little foundation in programming.
But there are ways to get past that crucial job interview. The five things you must do are:
Learn Python and R from quality trainers with years of industry experience
Build a portfolio of data science projects on GitHub
Join Kaggle and participate in data science competitions
Practice Interview Questions
Do basic Online Reputation Management to improve your online presence.
1. Learn Python and R from the best trainers available
There is no substitute for industry experience. If your instructor is not just an enthusiastic amateur (as in the case of many courses available online) but someone with 5+ years of industry experience working in the data science industry, you have the best possible trainers in the field. It is one thing to learn Python and R. It is quite a completely different thing to master Python and R. If you want to do well in the industry, mastery is required, not just basic abilities. Make sure your faculty members have verified industry experience. Because that experience is what will count in finally landing you a job in a top-notch data science company. You will always learn the most from experts who have industry experience rather than academics who have a Ph.D. even in the subject but have not worked in the field.
2. Build a GitHub Portfolio of Data Science Projects
Having an online portfolio in GitHub is critical!
All the best training in the field will take you nowhere if you don’t code what you learn and apply the lessons to real-life datasets and scenarios. You need to do data science projects. Try to make your projects as attractive as possible. As much as you can, your GitHub project portfolio should be built with these guidelines in mind:
Use libraries, languages, and tools that your target companies work with.
Use datasets that are used by your companies, and always use real-world data. (no academic datasets like the ones supplied with scikit-learn. Use Kaggle to get practice datasets.) The best datasets are programmatically constructed with APIs from Twitter, Facebook, Wikipedia, and similar real-world scenarios.
Choose problems that have market value. Don’t choose an academic project, but solve a real-world industry problem.
Extra marks for creativity and originality in the problem definitions and the questions answered by the portfolio projects.
3. Join Kaggle or TopCoder and participate in Competitions
Kaggle.com is your training arena.
If you are into data science, become a Kaggler immediately! Or, if your taste leans more towards development, join TopCoder (they also have data science tracks). Kaggle is widely touted as the home of data science and for good reason, since Kaggle has been hosting data science competitions for many years and is the international location of all the best data science competitions. One of the simplest ways to get a call from a reputed company is to rank as high as possible on Kaggle. What is more, you will be able to compare your performance with the top competition in the industry.
4. Practice Interview Questions
There are plenty of sites available online that have excellent collections of industry questions used in data science interviews. Now, no one expects you to mug up 200 interview questions, but they do expect you to be able to solve basic data science and algorithm questions in code (Python preferably) or in pseudocode. You also need to know basic concepts like what cross-validation is, the curse of dimensionality, and the problem of overfitting and how you deal with it in practice in real-world scenarios. You should also be able to explain the internal details of most data science algorithms, for example, AdaBoost. Knowledge of linear algebra, statistics, and some basic multivariable calculus is also required to possess that extra edge over the competition.
5. Manage your Online Search Reputation
This may not seem connected with data science, but it is a fundamental component in any job search. What is the first thing that a prospective employer looks for while hunting for job candidates, when given a name? That’s right – he’ll Google it first. What comes up when you Google your name? Is your online profile safe under scrutiny? That is:
Is your name when searched on Google free of red flags like negative reports of any type (offensive material, controversies)?
Does the search engine entry for your name represent your profile with accuracy?
Are your public Facebook, Twitter and Google profiles free of any automatic red flags? (e.g. intimate pictures)?
Does the Google visibility of your name depict your skill levels correctly?
If the answers to any of these questions are no, you may need to adjust or tweak your online profile. You can do this by blog posts, informed mature comments online, or even creating a blog for yourself and speaking about yourself to the world in a positive manner. This is critical for any job applicant today, in this online, digital, connected world.
You are a Product to be Marketed!
You are trying to sell yourself and your credibility online to people who have never seen you, and not even heard your name. Your Internet profile will make the key crucial difference here, to make sure you stand out from the competition. Many training sites are available that offer courses by amateurs or people with less than 2 years of industry experience. Don’t make the unwise choice to be satisfied with a low-price course.On the Internet, you will get only what you pay for. And this is your future career in the subject area of your dreams. Surely a little initial investment will go a long way in the long run.
Additionally, it will help to gain the employers’ perspective as well. You can refer to this Hiring Guide by TopTal for further reading.
Always keep learning. ML and AI are fields that move forward at an incredible pace. Subscribing to RSS feeds and online websites that keep you updated with the latest developments in the field is something that you absolutely have to do. Nothing shows your commitment to excellence a much as keeping up with the latest state-of-the-art research. And you can do it quite easily by using Reader applications like Feedly and Inoreader. Learning might be something you do in college. But mastery is something you aim towards for your entire lifetime. Never give up. All the best for your job search, which will definitely be successful if you can follow the instructions mentioned here on this blog post. Finally, pay special attention to your portfolio of data science projects on GitHub to make sure you stand out from the competition.
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It was a very good experience learning Data Science with Dimensionless. The classes were very interactive and every... query/doubts of students were taken care of. Course structure had been framed in a very structured manner. Both the trainers possess in-depth knowledge of data science dimain with excellent teaching skills. The case studies given are from different domains so that we get all round exposure to use analytics in various fields. One of the best thing was other support(HR) staff available 24/7 to listen and help.I recommend data Science course from Dimensionless.read more
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It was a great experience leaning data Science with Dimensionless .Online and interactive classes makes it easy to... learn inspite of busy schedule. Faculty were truly remarkable and support services to adhere queries and concerns were also very quick. Himanshu and Kush have tremendous knowledge of data science and have excellent teaching skills and are problem solving..Help in interviews preparations and Resume building...Overall a great learning platform. HR is excellent and very interactive. Everytime available over phone call, whatsapp, mails... Shares lots of job opportunities on the daily bases... guidance on resume building, interviews, jobs, companies!!!! They are just excellent!!!!! I would recommend everyone to learn Data science from Dimensionless only 😊read more
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