Building a Strong Foundation in Data Science: Essential Skills and Concepts

Did you know that as per Forbes, the global big data market is expected to grow from $138.9 billion in 2020 to $229.4 billion by 2025? Not only this, the demand for data scientists will increase by 38% by 2025!

Over the past decade, the availability of data and demand for data science skills and data-driven decision-making have skyrocketed. Pushed further into the limelight by the changing dynamics of business applications and consumer behavior because of the COVID-19 pandemic, analytics, and data science are now emerging as crucial navigational tools across industrial verticals. ​For tech giants such as Asana, Amazon Web Services—and even Google search ­–data analysis leads sales force development teams globally, helping reduce customer churn rate over time while improving product performance.

Hence pursuing a career in data science is not only lucrative but also rewarding. However,  the road to starting a career in data science or analytics is not always linear. In this guide, you’ll gain an in-depth knowledge of analytics and data science career pathways and requisite skills that are in demand right now. Besides this, you’ll also gain insights into which data science career is right for you.

Data scientists aren’t just engineers anymore…they’re programmers of next-generation technology. 

However, learning data science and programming can appear intimidating at first. Especially so, when you are just beginning your journey. Questioning appropriate learning tools, developing techniques, and analyzing statistics alongside coding and programming can cloud your mind. However, these are some essential questions that need to be answered before you start your journey. 

Navigating through the data science career can become a daunting struggle for beginners due to the abundance of resources which often leaves one in utter confusion. Regardless of whether you are a data scientist or just a beginner in the field of Analytics or Data Science, this article will guide you to make informed decisions in your career pathway. This simple guide would set a framework that can help you learn data science and inform you which specialization should you opt for.

Bachelor in Data Science at SRM University, Delhi-NCR is a thoughtfully curated interdisciplinary program designed for students wanting to learn data science, machine learning, and deep learning in their quest to become an AI professional. The B.Tech CSE (Data Science) course in collaboration with IBM and Xebia makes enrollment even more lucrative for aspiring students. You’ll get access to several courses, growth-driven internship projects, and the best part dedicated mentorship sessions with experts!

  1. Select The Right Role

The data science industry provides a platter of specialization and a variety of opportunities. You can opt for multiple professional roles such as data visualization expert, machine learning professional, data scientist, data engineer, etc. These are some of the many roles that learners can definitely pursue. While there are multiple specializations available for students to pursue, many business giants now look for professionals who dabble in multiple fields and can present uniform and diverse ideas. Hence it is the impetus for learners to have a clear idea about their future plans and pursue interdisciplinary curriculums that meet current industry demands and help students to foster holistic skills. 

For example, if you only delve into software development, it would be impossible for you to know even the basics of data engineering and thus restricting you from widening your horizons or even making career changes in the future! So, it is important for students to analyze and evaluate the courses or academic fields they will pursue and the requisite skill development opportunities offered by their desired institution.

Some of the best ways to gain clarity on this is through the following-

  • Build a network in the industry and engage in conversation with experts and mentors. Do thorough research about the programs, courses, and job opportunities available in the respective fields.
  • Figure out your personal strengths and weaknesses and then choose the role that suits your field of study.
  1. Learn practical applications and not just theory

The dynamic growth of the tech and corporate industry demands you to stay updated with recent skills requirements in the global market. Hence it is incredibly important for students to develop practical knowledge and real-life applications of how things work. Pursuing group work, innovative projects, and engaging in insightful internships will help you understand the subject matter in deeper layers and offer you insights into how it would be in applied reality. 

Following are a few tips to enhance your practical skills:

  • Complete exercises and assignments to understand their applications.
  • Apply your theoretical knowledge by working on a few open data sets. Even though the math behind the techniques might initially appear incomprehensible, through practice and perseverance, you will gain a better understanding of the process. 
  • Engage in activities with experts who have worked in the field as they will mentor you and improve your learning graph. 
  1. Follow the right resources

Students should never stop learning, and to do so they must be encouraged to engulf each and every source of knowledge they can possibly find. Some of the most useful resources for learning are to immerse themselves in various online courses, indulge in internships and workshops, and read books and informative blogs on data analysis that are written by the most influential Data Scientists and are easily available online. 

Learners must read vigorously about data science everyday day and develop a habit to be updated with recent discoveries and marketing strategies. 

  1. Basic Database knowledge and SQL is a must

It is important for a learner to understand that data doesn’t magically appear in the form of tables. Usually, beginners should start with gaining knowledge about machine learning by using data in the form of CSV, however, this is just the tip of the iceberg. In order to build comprehensive data analytics, students must learn SQL as it is the most basic skill for a data science professional. 

Gaining knowledge of data storage techniques along with the fundamentals of big data will prove advantageous in a student’s career development primarily because this is the most coveted skill in today’s industry-driven market. 

  1. Right guidance is essential

Last but not least, finding the right guidance is essential for pursuing a career in data science. Data Science and machine learning, data engineering, and relatively a very new field, and is constantly evolving with improved techniques, hacks, and strategies. There are only a handful of people who have decrypted the challenges of this field.

There are many ways to become a data scientist and emerge as an industry-ready professional, however, the process becomes easily navigable if you seek the guidance of experienced mentors and industrial leaders. Queries such as “What are the best ways to become a data scientist?”, “ Which courses should I pursue?”, “What are the industrial risks?”, often perturb students. Hence they should make wise decisions about career counseling services and the degree of experienced mentorship offered at their desired educational institution. 

However, the problem is that not everybody can get access to these expert mentors. That’s why the unique career counseling services offered at SRMUH provide a 1:1 mentorship program where the mentors get in touch with learners and build a customized learning path that suits their career needs! 

The demand for data science across industries is constantly growing as there are several employers who are investing significant time and money in data scientists. Hence, taking the right steps through informed decisions can lead to exponential growth.