How To Start A Career in Data Science in 2024 [Updated]

Download ebook

Starting a Career in Data Science is a Nightmare: Is it?

Getting started with data science is something that every professional in the IT space aspires to do. Data science as a field have gained popularity very recently though the data mining algorithms date back to the 60s. Data Science is the most in demand career in today’s highly connected, highly digitized world.

In fact, the charm that data science has also influenced academia and every year more and more students opt data science as their career option. But there is a catch, a lot of confusions all around, a lot questions that are left unanswered and a lack of proper guidance and mentoring. Many of the young graduates fresh out of college with degrees in their hands and a lot of energy and passion often ask me questions such as,

Where should I start my career in Date Science? Is it okay to start with anything that is offered to me and later when I have some years of industry experience I would then switch to a data science role because that is the ultimate next in the digital space?

There are openings for data engineers, model engineers, data analysts, machine learning engineers, etc. How do I know which one is the perfect suit for me?

I also often have questions from experienced professionals who want to switch to and start afresh a career in data science such as,

I am from a software engineering background having X numbers in my profile, can I get into data science? If yes, where do I start if I want a Career in Data Science?

I recently completed a course on end to end Data Science project management from my organization’s internal Learning and Development portal and I want to switch to a Career in Data Science. But looking at the requirements, I do not think I got shot. Do you think I should quit and start all over again with a lower package in some other organization?

All of these questions when laid on a piece of paper, often looks scary because they are real questions not limited to a handful of professionals who want a Career in Data Science. It won’t be wrong to admit that it is because of these questions that pop in one’s mind and a lack of proper guidance in answering them where a lot of professionals never change their careers to Data Science even after having a very strong knowledge and understanding of the subject.

If all of these gives you nightmares and you felt you are not the right person for a career in data science, this article gives you more clarity on what it takes to start a career in data science. I will also give you some tips and a strategical roadmap which might help you out land that dream job of yours in data science. I am sure that by the end of the article, you would be confident enough to start working on your existing skillsets and build your way up towards a career in data science.

This article is for professionals in general, young graduates who want to start a Career in Data Science and for students who are looking for sources to answer their questions before they appear in front of the interview panel.

But before going into the details and answering these questions, lets first understand what is Data Science in a nutshell?

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.

Data Science in a Nutshell

As per the definition of Data Science in Wikipedia,

“Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data”

Data Science is a multidisciplinary field which encompasses expertise from Project Management, Statistics, Mathematics, Computer Engineering, Algorithm design, cloud storage and Data Handling. It is thus for sure, irrespective of the number of courses you have completed online or offline and the number of Proof of Concepts (POCs) you have designed to prove your knowledge or the degree that you hold, starting a career in data science with an end to end responsibility has a very low probability.

It is because of a very simple reason that when we refer data in the corporate world, it is the live data from different sources which is very delicate, private and is often governed by stern laws to protect customers from getting exposed to unwanted agents in the digital space.

A data scientist thus has to gain a handsome understanding on the different phases involved in a data science project, also should be able to work out processes that would optimize resources and time and must deliver fruitful results in a live environment where billions of transactions take place every day between the customers and the organization. The typical steps involved in data science projects are as below,

• Data capturing: Data capturing can be considered as places which capture data in a business process. For example, when we sign up for a newsletter on a website, the form that we fill in can be considered as data capturing a point or when we swipe our cards while checking out, the Point of Sales (PoS) devices can be considered as data capturing a point. In this digital world, any activity on the internet can be considered as a data capturing activity because all of our activities get stored in databases and can be accessed by the organizations whenever required.

• Data Storage and maintenance: Once the data is captured in data capturing points in an organization, the data is securely routed to different storages such as relational databases or NoSQL databases where they are stored for further utilization. It is also a common practice to maintain the data in the same place where they are stored by creating indexes, data warehouses, caching the data, etc. for faster access and many times for getting insights into user activities on the websites.

• Data Visualization and processing: The stored data becomes useless if there is no information that can be extracted from the data. This is where data visualization and processing comes into use where the data is taken and visualized to see trends in the data or getting patterns so that organizations can optimize their resources for better services. It is also a common practice to get some general idea of the data in the form of statistical terms such as mean, median, mode, variance and standard deviations.

• Data Analytics: Data Analysis is a more, in-depth analysis of data which focuses on getting in-depth information from the data gathered and stored in the databases so that those results can help organizations optimize their resources and time.

• Result Communication: Once the entire data science process is complete, the last steps in communicating the findings to the stakeholders and the key decision-makers of the organization. This is usually done with the help of graphs and charts.

Now that we are familiar to what a data scientist does and what are the various steps involved in a data science project, let us now turn to understand how one should go about in order to start a lucrative career in data science.

Answering the big question: Where do I start?

This has been the biggest question in the minds of many people as to where one can start in this vast space of data science with so many things to learn, practice and gain experience. Often, it is not the theory or the codes or even the languages that are daunting but the list of skill sets, responsibilities and expertise that the organizations want from the candidates is what keeps them in the back seat.

Below is a strategical roadmap of how can one go ahead and start with a career in data science. Please keep in mind though that the below is just a strategy and as all other strategy it depends on a number of other factors for its success.

A plethora of online resources are available on the internet and offline to help you enhance your skillset. For instance, if you are into machine learning, you have resources like coursera, udacity, udemy, cognitiveclass.ai, etc. which offer MOOC programs where you are taken step wise from one concept to the other. Make sure you enroll into one of the MOOCs and complete it. The main advantage of taking a MOOC is you not only get access to video lectures but also get access to online discussion forums, live interactive classes and peer reviews of your work which adds value to what you are trying to gain.

The good news is all of these limitations of a tool can easily be ignored if one is comfortable working with programming languages. By going through the documentation of various functions in a package one can easily understand how graphs are plotted or how models are built and what can be tuned to further improve the performance of those models. This comes handy especially when there is a requirement from the business executives on a different more intuitive plots or also improve the efficiency of the model whereas achieving all these requirements with the help of tools might not be feasible due to their innate limitations in design.

Once building the PoC is complete, getting it peer reviewed is a plus. There are thousands of communities on social media which you can join. Get your dummy model uploaded in a shared repository such as Git and share the link across in the community to get the model reviewed by a variety of people. This helps in two ways,

• The performance of your model gets better with time since the model would then be reviewed by a number of professionals both experts and amateurs. That way you get a feedback on your model which can be worked upon to make your model better.
  • You get the highlight in the community. The more models you create and share across the more visible you become in the community as a result your chances of getting hired by one of the experts in the community gets exponentially high.

Now that we have a strategy which we can employ while getting a career in data science, it’s now time to have a look at some of the roles that is available in the data science field and the key roles and responsibilities they must fulfill in an organization.

Roles in Data Science

• Data Scientist: The role of a data scientist is perhaps the most lucrative and sought after career in data science. It is challenging and is equally interesting to work as a data scientist in any organization. Data Scientists are often termed as data unicorns because of their obsession with data. They work both with structured and unstructured data to extract information from them and communicate them to the clients or executives and their peers. Data Scientists often are wonderful communicators and they communicate their findings with utmost precision and tact. A data scientist has knowledge of the overall data science process and is not confined to any particular step in the data science processor technology.

• Data Analyst: As the name suggests, data analysts are the real professionals who deal in raw data to figure out insights. They are involved in data gathering, data cleansing, getting the basic data analysis done and also communicate the patterns that can be seen in the data. Data analysts build models to get key insights from the data in terms of various business Key Performance Indicators. Data Analysts must be great team players and communicators in order to deliver the best in their roles by working in tandem with a variety of teams and professionals in the organization.

• BI analyst: The BI analyst deals in the performance of the business as a whole by looking into historical data and trends. They work alongside the executives to formulate policies based on the interpretation they have from the data provided to them. BI analysts usually are an expert in Tableau, a tool for plotting interactive graphs of the data provided as inputs and usually align their findings in the best interest of the business performance. BI analysts also analyze trends in the market to figure out trends of high and lows and help business gain a competitive advantage over their peers in the market.

• Data Engineer: Data Engineers are the ones who are known for their wisdom in data. Data Engineers are specifically trained professionals who focus their attention towards data cleansing, outlier detection and data preprocessing before letting the data into the model. Data Engineers are often data experts specializing in big data and their focus area lies in analyzing, visualizing, collecting and processing big volumes of data. Data Engineers are also seen as experts in ETL which is the abbreviation of Extraction, Transformation and Loading in the field of data science. Data Engineers work closely with BI analysts, data analysts and data scientists and support them during model building and generate visual plots for business communication.

• Machine Learning Engineer: As the name suggests a machine learning engineer is an expert in machine learning algorithms which involves supervised, unsupervised and deep learning. Many experts consider machine learning engineers as data engineers with an aptitude in machine learning. Machine Learning engineers are expected to have sound technical knowledge on the niche skills required for machine learning and deep learning and they are also expected to have a good hold on mathematics so that they can work on the existing algorithms and propose improvements on the same.

• Data Architects: Data Architects are the creators of the data which digitizes the business world. They are specialized experts in data storage, data collection and data flow management in the organization. They are concerned with the improvement of the data flow and data storage within and outside the organization. They also plan strategies that would help an organization optimize the data management process and also work closely with other data experts while they access, maintain and work with sensitive data for modelling.

This concludes the list of roles available in a data science project. Please note that the list above is only an indicative list and not an exhaustive list. Depending on the requirements, business structure and organization hierarchy the roles are subject to change and there might be additional roles introduced to support these roles in a data science project.

Starting a Career in Data Science is a Nightmare: No it is not?

By this time, I am pretty confident that what once looked like a nightmare to many young graduates and experienced professionals is no more a nightmare now. If you have gone through this article, I am confident that you have got a strategy on how does one land a job in pursuit of a career in Data Science. Below is the brief summary of what it takes to start with a Career in Data Science,

• Find out where do you fit
• Enhance your skill set to match the industry
• Learn a Language and then a tool
• Build PoCs and get it peer reviewed
• Keep yourself updated
• Practice Communication Skills

We also looked qualitatively on what roles does a project in data science usually offers. They can be summarized as below,

• Data Scientist
• Data Analyst
• BI analyst
• Data Engineer
• Machine Learning Engineer
• Data Architects

However, I should also point out the this is a qualitative list and not an exhaustive list. Depending on the organization structure, hierarchy and the demand of the data science project, the roles and responsibilities changes; there are even instances where roles are added to support the other roles involved in the project.

Recommended Reads:

• Top 15 Best Data Science Course in Mumbai
• Top 10 Data Science Course in Pune
• Top 10 Data Science Course in Bangalore
• Top 10 Data Science Courses in Nagpur
• Top 20 Data science course in Delhi NCR
• Top 10 Data Science Course In India
• Data Profiling, Process and its Tools

Also, Check this Video

Download ebook