Congratulations on choosing data science as your future career! It’s a great decision.
Data science is a thriving field with a remarkable number of job openings around the globe. The demand is outstripping the supply! That means there are more vacancies than qualified data science professionals.
So this journey you have taken to become a hands-on data science professional? You can already visualize why it’s the path to future success. There are a variety of problems you can solve, a whole host of tools you can master, and a broad range of techniques you can learn and then play around with.
Although being from an IT background, we would highly suggest you take data engineering into consideration for your career transition as it matches almost all your strong points (plus it is a potential future job role!). According to the report by datanami, the demand for data engineers is up by 50% in 2020 and there is a massive shortage of skilled data engineers right now!
The canvas is in front of you – now it’s your turn to pick up the data science brush and start painting your way to a successful data science transition.
Why Data Engineering is the natural fit for you?
Before a model is built, before the data is cleaned and made ready for exploration, even before the role of a data scientist begins – this is where data engineers come into the picture. Every data-driven business needs to have a framework in place for the data science pipeline, otherwise, it’s a setup for failure.
Most people enter the data science world with the aim of becoming a data scientist, without ever realizing what a data engineer is, or what that role entails. These data engineers are vital parts of any data science project and their demand in the industry is growing exponentially in the current data-rich environment.
A data engineer is responsible for building and maintaining the data architecture of a data science project. These engineers have to ensure that there is an uninterrupted flow of data between servers and applications. Some of the responsibilities of a data engineer include improving data foundational procedures, integrating new data management technologies and software into the existing system, building data collection pipelines, among various other things.
Given your background in the IT field, data engineering will be a natural fit for you and complement the skills you already have. Data engineers and data scientists work in sync with each other for data science projects. Once you have the grip over data engineering subjects, you can then delve into data science subjects.
What can you expect in a data engineering manager role?
A data engineer is someone who develops and maintains architectures for smooth data flows within large-scale data processing systems. He deals in raw, unstructured, and dirty data which is often inconsistent and invalidated.
It is the job of a data engineer to create architectures and systems to churn out data that is efficient, reliable, and of high quality. They work in sync with the team of data scientists as well as the stakeholders.
Given your engineering background, you will find it much easier to understand the following concepts –
- Software engineering,
- Database systems
- Cloud technology
- Efficient programming skill
This will definitely provide an edge over other candidates!
The role of a data scientist is really crucial to the whole organization and the economy as a whole. But the problem is – there is a shortage of “Skilled” data scientists globally. The AI and ML Blackbelt plus program aims to make you an industry-ready certified data science professional with 14+ courses, 39+ real-life projects, and 1:1 mentorship sessions so that you are never off-track.
What are the key skills required to excel in a data engineering manager role?
Data engineering is a multi-faceted role. There is no one-size-fits-all approach to learning the subject. Having said that, there are a few core skills you will need to pick up to make a successful career transition to data science.
Here are the key skills you would need:
- Programming Languages
- Database Systems
- Data Engineering
- Software Engineering
- Building data pipelines
- Data scalability
- Cloud computing
- Moving applications to production
Apart from these core skills, there are other skills you should be aware of, such as:
- Structured Thinking
- AI/ML ecosystem and applications
Data engineers need to have a good command of programming languages like Java, Python, Scala, and SQL in order to be successful as a data engineer. An advantage for you is that you must have covered most of these skills in the past as part of your IT experience.
Some of the commonly used tools in big data are Apache Spark, Hadoop, AWS, and on the database side, both SQL and NoSQL databases have equal importance. MySQL, CassandraDB, MongoDB are a few common ones.
How can you excel in each of these required skills?
Ah, the key question! Now that you know what you need to learn, the attention turns to how you can learn those skills. Let’s look at a few options and suggestions on how to pick up and hone the key skills we mentioned above.
Programming provides us a way to communicate with machines. Do you need to become the best in programming? Not at all. But you will definitely need to be comfortable with it. You will be required to code the ETL process and build data pipelines.
The following programming languages are the most popular among data engineers:
Python: It is one of the easiest to learn a programming language and has the richest library. I have found Python to be a lot easier to perform machine learning tasks, web scraping, pre-process big data using spark, and also is the default language of airflow.
If you want to learn Python, here is a great free course you can refer to:
Scala: When it comes to data engineering, the spark is one of the most widely used tools and it is written as Scala. Scala is an extension of the Java language. If you are working on a Spark project and want to get the maximum out of the spark framework, Scala is the language you should learn. Some of the spark APIs like GraphX is only available in the Scala language.
Here are some of the recommended resources to get started with:
SQL Databases- SQL databases are relational databases that store data in multiple related tables SQL is a must-have skill for every data professional. Whether you are a data engineer, a Business Intelligence Professional, or a data scientist – you will need Structured Query Language (SQL) in your day to day work.
NoSQL Databases – To handle huge amounts of data, we need a more advanced database system that can run multiple nodes and can store as well as query a huge amount of data. Now, there are multiple types of NoSQL databases, some of them are highly available and some of them are highly consistent. Some are column-based, some are document-based and some are graph-based.
Apache Airflow – Automation of work plays a key role in any industry and it is one of the quickest ways to reach functional efficiency. Apache Airflow is a must-have tool to automate some tasks so that we do not end in the loop of manually doing the same things again and again.
Apache Spark – It is the most effective data processing framework in enterprises today. It’s true that the cost of Spark is high as it requires a lot of RAM for in-memory computation but is still a hot favorite among Data Scientists and Big Data Engineers.
ELK Stack – It is an amazing collection of three open-source products — Elasticsearch, Logstash, and Kibana. More than 3000 companies are using ELK stack in their tech stack, including Slack, Udemy, Medium, and Stackoverflow.
Hadoop Ecosystem – To handle big data, we need a much more complex framework consisting of not just one, but multiple components handling different operations. Hadoop is a complete eco-system of open source projects that provide us the framework to deal with such type of data.
Apache Kafka – Tracking, analyzing, and processing real-time data has become a necessity for many businesses these days. Needless to say, handling streaming data sets is becoming one of the most crucial and sought skills for Data Engineers and Scientists. Kafka is a much-needed skill in the industry and will help you land your next data engineer role if you can master it.
Amazon Redshift – AWS is Amazon’s cloud computing platform. It has the largest market share of any cloud platform. Redshift is a data warehouse system, a relational database designed for query and analysis. You can query petabytes of structured and semi-structured data easily with Redshift.
To write a high and good quality code that won’t cause havoc during the production stage, it is necessary to know the basics of some of the software engineering subjects like – basic lifecycle of software development projects, data types, compilers, time-space complexity, etc.
Writing efficient and clean code will help you in the long run and help you collaborate with your team members. Again, you don’t need to be a software engineer but being clear with the basics will help you.
- Basic Concepts of Object-Oriented Programming in Python
- Inheritance in Object Oriented Programming for Python – An In-Depth Guide for Everyone
- Methods in Python – A Key Concept of Object Oriented Programming
Structure Thinking and Communication
Structured thinking is a process of putting a framework to an unstructured problem. Having a structure not only helps an analyst understand the problem at a macro level, but it also helps by identifying areas that require deeper understanding.
Without structure, an analyst is like a tourist without a map. He might understand where he wants to go (or what he wants to solve), but he doesn’t know how to get there. He would not be able to judge which tools and vehicles he would need to reach the desired place.
How many times have you come across a situation when the entire work had to be re-done because a particular segment was not excluded from data? Or a segment was not included? Or just when you were about to finish the analysis, you come across a factor you did not think of before? All these are results of poorly structured thinking.
Understanding of AI/ML Ecosystem and application
After gaining your core data engineering, it is time to get to understand the Artificial Intelligence ecosystem and its applications.
The AI and ML ecosystem comprise of diverse tools and techniques for various applications. For example, self-driven cars are powered by computer vision and deep learning. Search engines and social networks employ applications of NLP very heavily.
To get accustomed to AI and ML, you must be familiar with –
- Types of ML
- Building blocks of AI and ML
- Common Terminologies
- Common Data Capturing Types and Tools
- Skills Required in Data Science
The AI and ML Blackbelt Plus program not only covers all the hard skills like Python, machine learning, statistics but also other essential soft skills like structured thinking and storytelling skills. Not just that you also get a resume and interview assistance!
Apply newly learned skill to your current job role
Whatever we have covered so far has a lot to do with understanding different data engineering concepts. We’ve covered both the technical side (programming, data engineering, etc.) and the soft skills aspect (structured thinking).
So, what’s the next step for you in your transition journey?
It’s time to apply your knowledge in a practical scenario! Yes, you need to marry your theoretical knowledge with hands-on practical experience to truly stand out as a data science transitioner. Given your background, there are broadly three ways you can do this
Look for DE projects within your organization: The first and most accessible way to build upon your knowledge is by getting a hand on a data engineering project within your own organization. You’ll get an idea about the tools and techniques employed there. It’ll be a thrilling experience to help the data science team get better data and putting their models into production.
Open Projects: One key thing that has helped transitioners immensely is picking an open-source data science project and running with it. This not only helps you understand the key areas you need to improve on but also shows you the way forward. And these projects aren’t your run-of-the-mill data science projects. You can collaborate with other data scientists and help them put their machine learning models into production. So, pick a project that intrigues you and start working on it today!
Look for Freelance work: This is one of the most popular paths to breaking into the data science industry. Even for experienced people – freelance work is a great opportunity to break into data science. We have now seen so many successful transitions enabled by freelancing. Not only do you gain hands-on experience in data engineering projects, but you also get to learn how the industry works and how a typical data science project functions. It’s an invaluable experience!
The AI and ML Blackbelt plus program not only covers all the hard skills like Python, machine learning, statistics but also other essential soft skills like structured thinking and storytelling skills. Not just that you also get a resume and interview assistance!
Stay up to date with current developments in the domain
This is another essential aspect of working in data science. We’ve seen the majority of transitioners skip this step and focus exclusively on picking up machine learning concepts – don’t do that!
Data science is still a very nascent field. We see major breakthroughs happening on a regular basis (sometimes a weekly basis!) and it can become difficult to keep up with all that’s happening. But if you can find time to catch up on the latest developments, you’ll already have an edge on your competition.
Let us give you an example. The Natural Language Processing (NLP) field has come a long way in the last 3 years (since 2017). We see a new language model seemingly every week that builds on the last major breakthrough. If you can keep up with this pace, if you can spend a bit of time understanding what’s going on, you’ll gain invaluable knowledge that your peers won’t have.
So what are the different ways in which you can stay up to date in the vast space of data science? Here are three suggestions based on our experience:
- Follow Newsletters and blogs: This is the easiest way to stay abreast of developments. There are plenty of good newsletters out there (just do a quick Google search) that will send you weekly updates. You can also subscribe to blogs like Analytics Vidhya to check out the latest tools and techniques in data science.
- Follow People: Another no-brainer! The data science community is a great place to connect with fellow transitioners, experts, and industry veterans. You’ll be surprised how approachable these experts are and they’re always willing to share their knowledge and advice. Find these people on platforms like LinkedIn and keep following them regularly.
- Attend MeetUps: This one requires a bit of effort but the eventual payout can be HUGE. Meetups offer you an unparalleled opportunity to meet your fellow transitioners and connect with them, learn from them, and build a rapport that might benefit both parties. Over time, once you are comfortable with core machine learning concepts, you can even try and speak at these meetups to build your profile
The big salary question – what can you expect from this transition?
Making a career switch to data science for getting a salary bump is entirely justified. However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post-transition.
Taking figures from the popular and relatively accurate website called Glassdoor, this is what the salary situation looks like for a data scientist:
As you can see, the average salary is approximately INR 10,00,000 per year in India whereas the figure is approximately $113,000 in America.
Similarly, the average salary for data engineers in 2020 is approximately INR 7,00,000 per year in India wheras America has around $103,000.
If you bring a bit more experience to the table and you have relevant domain experience, you might look at a more senior role (though this is a bit rare if you have no prior data science experience):
Similarly, the average salary of a senior data engineer is around the lines of INR 18,00,000-19,00,000 in India and in the USA, you can expect this figure to be around $136,000.
As we said, it comes down to how relevant your previous experience is. More often than not, as a person transitioning from an IT role to a data engineering manager role, you’ll be looking at the second graph.
What are the challenges to get the “Sexiest Job of the 21st Century”?
There has never been a better time to become a data scientist. Data Science is a booming industry but it also comes with its own set of challenges. Keeping in mind that you come from an IT background, it should help you overcome the majority of challenges, however, we’ll list a few that need your special attention. If you have reached here, we know you can work out through obstacles. Let’s take them up one by one –
- Hard to find the job – At the mid-management level, it becomes crucial not just to find the company offering great compensation but which has a supportive environment where data science is embraced and data-based decisions are at the core. To get all the requirements in one organization, you will need to work hard in job-hunting.
- Finding projects within your organization – As discussed above, a supportive organization will help you grow and magnify your perspective along with providing a new experience. Finding a data science project within your own organization will help you gain much-needed learning without job-hunting.
- Knowledge of Data Engineering projects is also important – Data science is relatively a new and emerging field but data engineering is still in its nascent stage and organizations are in constant effort in formalizing this job role. Both are equally important. Therefore you might find it a little hard to find the right resources to study data engineering. However, you can get past this by studying data engineering from analyticsvidhya.com/blog.
- Communication Skills – Getting into a data science mid-management role from a hands-on IT role is a big transformation. One of the key job requirements will be to manage the stakeholders. This especially requires a great command over your communication skills. It is a challenge that we believe you can overcome in a short duration.
- Structured Thinking – Ah, the most crucial skill yet the most overlooked one. Structured Thinking as discussed above is the art of breaking down the large unstructured problems into smaller and manageable problems. A data science project is valid as long as the problem statement is correct, otherwise, the whole project goes down the drain. Being a data science professional, you must ensure that you are working on the right problem statement.
Afraid of all the challenges that are supposed to come in your way? Well not anymore, how about an expert mentor that will provide you with a personalized learning path that is in sync with your goals and keeps track of your progress? It is possible with the AI and ML Blackbelt Plus program which comes along with 75+ mentorship sessions.
Now that you are aware of the various components you’ll need to put together to make this career transition, are you prepared to buckle up and take this thrilling journey? The payoff is immense but as you might have gathered, you’ll face plenty of obstacles along the way. Your eventual success will come down to how well you can get past these hurdles.