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HomeBlogData ScienceIs Data Science For All?
Data Science seems to be the buzzword for the decade. The internet is talking about it, companies want it, and your friends and family are in the loop about how data science changes our world. For example, notice how Google’s news feed algorithm is updated based on the conversations around you and how YouTube videos are so apt based on recommendations.
Let us now come to budding data science professionals and how complete data science Bootcamp can be an answer to this question. Irrespective of the position, industry, and level of technical knowledge, you would like to understand where you fit into all of this? Is it possible to leverage this “new oil” everyone talks about? The title does say, “Is Data Science for All?” but I would like to focus on “Is Data Science for you?.” Let us get to it.
Let me give you some context. Suppose your grandparents learn and upskill themselves on how to use the internet. Then, they will enjoy a whole set of new privileges in life-like video calls with family, content online, group conversations, digital photos etc.
On the other hand, if they do not know how to use the internet, they are digitally disabled, missing a big part of the new world.
Similarly,
If you do not know how to leverage Data in this Digital Age, you become digitally disabled
Whether you are a student, professional or entrepreneur, let me explain to you a situation that you might have faced in your day to day. If I were to give you a dataset or an excel file with one million rows (ten lakh rows), you can open up excel and perform some sort of analysis.
The situation changes if I were to give you ten million rows – you would not be able to open it in Microsoft Excel. Now, suddenly, you cannot do much because Excel has a limit of one million rows. Learning how to work with data, perform analysis and even visualize data can help you make better decisions. People who are done with their MBA (Master Of Business Administration) are returning to Data Science because it can help them make better decisions.
The most unique thing about Data Science is whether you are a fresher or an experienced professional in any field, you can become a Data Scientist.
There are two main reasons that anyone can enter Data Science with a little bit of effort are:
Whatever your nature or experience is, there is a Data Science course in India available for your enrollment. How can I be so sure? Let me show you precisely what I am discussing using the roles under Data Science.
I could go about and just jot down the roles and tell you what we do in them. But instead, let us try to understand with a solid example of how Data Science works in the real world. Follow along closely. We will also be talking briefly about the tools and technologies needed!
Let us say we are a retail giant, Knowledgehut Retail. We are looking to optimise our products in our retail stores across India. So the business stakeholders will need to understand our best selling and worst selling products. The steps that we can take to understand what is Data Science all about are as follows:
1. Heuristic model
A heuristic model is a fancy way of saying that business rules are used to decide whether the products are performing well or poorly. For instance, we can say that if a particular oil brand is selling over 1,700 packets in a month, it is a good product. For example, writing code for these business rules as if-else statements would entail a heuristic model.
2. Statistical model
Let us say we are selling umbrellas. The umbrellas sale is low throughout the year, except during the monsoon season. Does this mean that umbrellas are poor performing products? No. If we were to look at the distribution over five years, we would see that it peaks during the monsoon season. So the seasonal sales can be inferred by looking at their statistical distribution.
3. AI/Machine Learning model
Think of a Machine Learning model as a black-box model. The input is the same as we have seen above, an extensive master dataset. The output is the good and bad performing products. However, instead of a manual, heuristic approach or just statistics, we let the model look at historical data. It learns what is happening and tells us what the good and bad commodities are.
Great job, you understand how the end-to-end process of how a Data Science process flow works. That was all about Data Science. Let us look at what you have understood in the form of an image and briefly discuss the prominent roles that exist in the industry.
1. Data Engineer
Like how water flows from one end of a pipeline, data also flows in from multiple sources to the master dataset. Data Engineers help make the data pipelines to get data from many sources and bring it to a common location. While getting the data, the data needs to be cleaned, standardised, and bought to a standard format.
Platforms to be familiar with (either one) - Google Cloud Platform, Microsoft Azure, Amazon Web Services
Languages & skillsets: SQL, understanding of databases and python (fundamentals)
2. Machine Learning Engineer
As discussed, there are three primary kinds of models that one can use: the heuristic model, statistical and machine learning model. A machine learning engineer needs to work with the business team to understand the requirements and the data engineering team to get the relevant data. A knack for practical problem solving can aid an individual in fitting into this role.
Languages: Python, SQL, MLOps fundamentals
Other skillsets: Machine Learning, Big Data, Business & domain understanding
3. Business Analyst
Once the model is made, and we have the output, we display it to the business so they can view the information visually and come to a business decision that benefits the organisation. This can be done with the help of a dashboard and presentations to quantify business impact.
Visualization & Dashboarding tools (either one or two): Tableau, Power BI, Qlik Sense
Tools & skillsets: Business Understanding, stakeholder management, Microsoft excel, project management, strong communication skills, presentation skills
4. Data Scientist
The most sought-after role in the market is that of a full-stack Data Scientist. To be one, the candidate must understand everything mentioned above end-to-end. In addition, a Data Scientist needs to have the skills of a Data Engineer, Machine Learning Engineer and Business Analyst combined.
Skills: All the above (fundamental understanding) + Team management
Now that you have gained a fair knowledge of the roles under Data Science let us explore the golden question – “What role should you pick?”
This is a subjective question. Based on my substantial experience in the industry, I would chalk out this decision to the knowledge of the person you are currently and the kind of person you would like to be. We would also want to consider future growth over the next few years. One of the best pieces of advice I can give you is:
Talk to people who are where you want to be.
I have done the research for you, and here is my opinion that I will divide based on the roles in Data Science that we discussed above.
If you are the kind of person that is an introvert and is comfortable with extremely process-oriented work, this role is for you. You will learn and eventually master a particular cloud platform and implement it for a few projects in terms of growth path.
As you grow up the ladder, the roles are Senior Data Engineer and Technical Architect. You will need to manage data pipelines for more projects, help team members/teams, and eventually design the end to end pipelines for various groups.
Having a knack for experimentation with data, along with being good with mathematics, numbers, and statistics, will put you in an excellent position to be a machine learning engineer. This is one of the roles in Data Science that has the word “engineer” in it, which indicates that there is some amount of Software engineering present in it.
An extraordinarily respected and future-facing role in the market, the growth in this role can be in terms of business or technology, depending on the individual’s inclination.
With a heavy emphasis on efficient communication, presentation and visualisation, this role is suitable for extroverts with a presence of mind. Furthermore, in a position that has a low amount of coding, business analysts are also highly desired in the market.
Growth in this role is along the lines of Senior Business Analyst, Business Managers, VP, director, etc. Strong domain knowledge can help business analysts be influential decision-makers.
Given the current pace of technology, being a Data Scientist would entail a lifelong role of learning. Having the patience and know-how to manage technology, clients, and teams. A strong growth mindset coupled with strong technical experience can help individuals make a big difference in the organisations that leverage Data Scientist folks!
Growth can be along with technology or management. Data Science can help you be wherever you want to be!
Disclaimer: The Data Science market is in its nascent stages of growth. Although the terminology and work mentioned in the above sections would be ideal, different companies have different terminology, roles, and responsibilities for the same positions. The same has not yet been standardised across the industry. Before applying for any job(s), speaking to someone already doing the role you are looking for is strongly recommended.
Now that you have understood the “what,” you might be interested in knowing more about the timeline for you to become one!
Focusing not just on how you can crack the interview but also on giving importance to other factors can make you stand out in a crowd.
The more work you put into it, the more you get out of it. I have practised all I preach; please feel free to Google my name. You can get into Data Science as fast as you would like to.
When I started out as a data scientist, my major fear of code. The one thing I have learned is - The most effective way to get good at something is to keep doing/practising it daily. Secondly, rather than just learning the theoretical way, put the knowledge into practice, which can supremely enhance one’s skillset. Here, I would like to break the mandatory skills into different domains of skills, i.e., interpersonal, and technical.
To master all these tools, let us discuss some of the more popular Data Science resources available.
Multiple resources can be used for Data Science.
Congratulations! You are the top 5% that have managed to come to the end of the article. This shows that you are here to understand and know how Data Science works in the real world. Whatever you decide regarding your journey in Data Science may be, remember that data is the new future. Everyone is afraid of code initially, but eventually, we must move in the direction that the world is moving. The faster we adapt, the more we will benefit from the boom of Data Science.
Thank you for taking the time to read my article. I am a Lead Data Scientist with the most prominent Data Science firm in the country – you can connect with me on LinkedIn. If you have any doubts, please feel free to reach out to the team or Google me and shoot me an email. I am always happy to help!
Yes, everyone can become a data scientist, irrespective of their experience, domain, technical and coding knowledge, and background. However, the focus should be on how you can add a “data edge” to your current experience and area of interest.
For instance, some of my mentees are interested in Formula1 racing. When they start in Data Science, they already have an intuition about the data. So. I encourage them to connect with Data Scientists who work in Formula1 and make projects related to their area of interest based on what they learn.
Lifelong learning is at the heart of Data Science. Whether you are a student, developer, marketing/sales professional, HR professional, teacher, entrepreneur, businessman or a housewife, everyone can study data science!
Even if you have your own venture, learning data science can help you improve whatever you do in life. Data is all around us, and leveraging data to make decisions is extremely powerful!
Anything that is being developed will grow. If you look at small/big companies, much of the work no longer revolves around sales/marketing/development. Even software engineers, developers and testers are focusing on maintaining systems.
The one area that is being built on is data! Naturally, therefore, the name Data Science might evolve to different terminology. Still, the fundamentals of Data Science will stand firm. Companies have matured in all other core areas; that is why there is and will be a future in Data Science.
In the current state of the market, Data Science has a higher demand, at least for the next decade or so. Data Science would comprise almost 90-95% of what is currently being leveraged in the market. AI is a niche skill that a selected set of companies are leveraging. If research and futuristic endeavours are what you are interested in, then AI might be a better choice.
In terms of selling yourself in the market, there is a higher demand for Data Science. However, having said that, once you learn Data Science, learning AI is incremental learning and not complicated!
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