Data Science is one of the most sought-after fields in the modern technology-driven world. It is rapidly being employed in every single field; from Medicine to Space to Sports to Entertainment, Data Science and Data Scientists have become a core, important part of every department. Now, while the career path to Data Science has been thoroughly laid out (link to the article ‘How to build a career in Data Science’), there are several essential skills required to be a successful Data Scientist.
A Data Scientist is someone who studies complex sets of data, researches it to extract valuable information, and then transforms it into insightful models. A Data Scientist has to be aware of a lot of factors and skilled enough to undergo this entire procedure and finally present a solution to a problem.
What are the Skills Required to be a Data Scientist?
Data Scientists require a wide array of skills to be adept at their job. Their skillset can generally be broken down into ‘Technical Skills’ and ‘Non-Technical Skills’. Data Scientists are generally required to work when a business-related problem arises. Strategy-building goes a long way in Data Science to solve those problems, be it in small businesses or large ones.
The data interpreted are from a variety of fields such as Mathematics, Statistics, Business, Computer Science, etc. Data Scientists utilize their skills in programming, statistics, and industry domains to arrive at a solution. Let’s have a detailed look at their skill set.
Technical Skills Required for Data Scientists
More than anything, Data Scientists possess a variety of technical knowledge and are skilled at operating them. It is a testament to their passion for solving almost any problem; the more skills that a Data Scientist acquires, the wider range of problems they will be adept at solving. Here is a list of technical skills Data Scientists possess:
1. Machine Learning
This is an essential thing that Data Scientists need to understand and correctly predict the solution model. This skill is about understanding machine algorithms and evaluating them.
2. Programming
Programming languages like Python, R, and SQL, should be a necessary skill set in a Data Scientist.
3. Advanced Mathematics
Having a firm grip on advanced mathematics like Calculus and Algebra always comes in handy for a Data Scientist.
4. Data Visualisation
As part of their regular job, Data Scientists have to create tables, charts, and graphs with tools like Tableau, PowerBI, Matplotib, and Seaborn among others.
5. Statistical Analysis
Data Scientists should know how to do Statistical Analysis using measures like Median, Mode, Standard Deviation, and Correlation.
6. Computing
Parallel processing, Computer Architecture, and Optimization Techniques, are some of the skills Data Scientists need to have.
7. Deep Learning
This skill involves learning about artificial neural networks inspired by the human brain’s structure and function.
8. Natural Language Processing (NLP)
An increasingly important tool that Data Scientists need to be proficient at.
9. Big Data & Cloud Computing
These are just the other technical skills that Data Scientists should be privy to.
Non-Technical Skills Required for Data Scientists
Besides being adept at technical skills, Data Scientists also require a host of non-technical skills to be proficient at their job. Here are some of the non-technical skills listed below:
1. Critical Thinking
Since the process of studying large data sets and finding a solution is a long and multi-layered one, Data Scientists are required to be on their toes and keep their minds sharp at all points of time. Critical Thinking is very essential.
2. Effective Communication
Data Scientists often work in teams to brainstorm the solution-finding approach before creating the final model. For that, there needs to be effective communication between every team member.
3. Being Proactive
It’s not just the problems that arise out of business requirements that need to be solved by Data Scientists, but they also need to create opportunities by being proactive – finding problems and then solving them.
4. Intellectual Curiosity
Data Scientists cannot just rely every time on the same process to find solutions to every problem. A lot of times they will get stuck at some critical problem when need to dive deep and find answers outside of the normal pattern. Therefore, intellectual curiosity is very important.
5. Business Sense
The need to solve a problem, or a problem itself, arises from business needs. Therefore, Data Scientists need to be aware of business needs and have a very sharp business acumen to be good at their job.
How to Develop Your Data Scientist Skills?
To remain relevant as a Data Scientist in this ever-changing world, one must constantly upgrade their skills. Due to the increasing demand for Data Scientists, they are also expected to solve a wide range of problems that arise due to business conflicts or otherwise. They also create opportunities using their analytical and resolution skills. With that, here are some ways a Data Scientist can develop their skills:
1. Online Courses
There are online courses for every facet of Data Science. From Statistics to Programming to Machine Learning to Data Visualization, there are detailed online courses for each of these courses. Data Scientists can enroll in them and learn to improve their skills.
2. Gaining Practical Experience on Real-World Data Sets
Before graduating as a Data Scientist and working for some business firm, they can practice on real-world data sets online. They reflect the same challenges and opportunities of data analysis and modeling. Several online data sets like Kaggle, UCI Machine Learning Repository, or Google Dataset Search, are available online.
3. Joining a Data Science Community
Data Science communities are a great way to learn from one another. They also get acquainted with the brainstorming process, which will be required in their professional journey. There are Data Science Communities on Reddit, Stack Overflow, and even Data Science Central.
4. Books and Blogs
Books and Blogs are important sources of knowledge base to improve one’s Data Science skills. Books such as Data Science for Dummies, The Elements of Statistical Learning, and Data Science from Scratch, are invaluable books on Data Science.
5. Experimentation
Since Data Science is a dynamic and ever-evolving field, it is important to keep up with the trends and experiment at every stage. This helps Data Scientists learn quicker, getting to know the ropes of their job. Google Colab, Jupyter Notebook, and RStudio are some of the tools that will help Data Scientists experiment.
How to Become a Data Scientist?
The path to becoming a Data Scientist starts with a Bachelor’s Degree in a related field. Post that, an MBA in Data Science & Artificial Intelligence further bolsters the chances of someone becoming a Data Scientist and working with a business firm. An MBA bridges the gap between non-specialists and experts in this dynamic industry. The course is designed to enable professionals with the necessary skills to become leaders in the fields of Data Science and Artificial Intelligence.
MBA ESG is one of the top colleges for an MBA in Artificial Intelligence & Data Science in India. The program is designed by dedicated industry experts keeping in mind current industry needs and future forecasts. The trifecta of scientific learning, tech knowledge, and managerial projects offered by the program makes it a specialized course required in the digital era.
As a post-graduate in this field, you’ll be equipped with the skills to collect, process, and analyze large datasets, develop AI-driven models, and apply these insights to optimize operations and create innovative solutions.
An MBA in Data Science equips you with the skills and knowledge to interpret data and make corporate decisions. Artificial Intelligence and Data Science may seem like technical profiles, but in our dynamic world today, their application in everyday business strategy decisions has become rather imperative and need of the hour. The benefits of studying MBA in Data Science & Artificial Intelligence Management are numerous.
From Earning Potential to Career Opportunities to Cutting Edge Technology to High Demand of Professionals to Dynamic Landscape, an MBA in Data Science and AI has tremendous potential.
Frequently Asked Questions
1. Does Data Science Require Coding?
Not necessarily. It does not matter how much of coding you know but it is important that one should understand the basics of programing and should be comfortable. This helps them to manipulate data, create algorithms, and deploy automated processes.
2. Can I Become a Data Scientist Without Any Experience?
You can certainly become a data scientist after pursuing relevant degree in Data Science and AI. Having an experience in domain increases your chances of employability and higher salary.
3. Is an MBA in Data Science & AI Worth doing?
Yes. An MBA in Data Science & AI is a must-do if one wants to build a prolific career in the field. An MBA separates the non-specialists from the experts in this field. It bolsters their chances of getting hired by the best firms.
4. Is it hard to become a data scientist?
No. It is not hard to become a Data Scientist per se. It just requires learning a range of topics and acquiring those skills over time. Alongside, one must also pursue a Bachelor’s Degree and MBA from the same field.