Data Science and Analytics

Step-by-step Guide to a Career in Analytics and Data Science

At the Audit Diwas event, our honorable Prime Minister, Mr. Narendra Modi, said, “Data is information, and data will define the future.” This clearly highlights the potential of data science and analytics in the future. People who can use data insights to make well-informed decisions are needed in every business today. In most businesses, only a few people have the ability to grasp and evaluate data. Data literacy is in demand and highly regarded across all industries, making an analytics profession attractive to people from all walks of life.

Why Build A Career In Analytics And Data Science?

According to IBM forecasts, by 2020-21, there will be more than 2.7 million job openings for people with critical data capabilities, up from 2.35 million in 2015. Data is the future, and with so many job prospects, the sector is anticipated to continue to grow. One can build a career in analytics and earn a high-paying job in any multinational company. Following are the various reasons why you must build a career in data science or analytics:

  • Every company nowadays requires a data expert who can assist them in making well-informed decisions based on useful data insights. As businesses become more global and have multiple supply chains, data scientists become increasingly vital in ensuring that data is appropriately used and that the target audience is reached.
  • In 2021, data science and analytics were among the most in-demand professions. Data science jobs are expected to rise by 27.9% globally by 2026, according to the Bureau of Labour Statistics in the US.
  • When you’re working in a growing and high-demand industry like data science, with a scarcity of skilled individuals, you’ll want a salary that’s as competitive as possible. A data scientist’s average base pay is $143971 globally and 10 lakh in India, according to Glassdoor.

Types Of Careers In This Field

As data is crucial for every aspect of a company, there are numerous employment opportunities in this industry. Each career path and function has its own set of skill needs and niches to fill. The core skills that every data professional must learn are Microsoft Excel, strategic data analysis, and data visualization. Other abilities necessary vary depending on the professional route chosen; deciding which path to take is the first step on the roadmap to building a career in analytics or data science. Analytics-related and data science jobs are the two main career paths in data. Analytics-enabled jobs cater to a specific niche or domain in the organization. Following are the various careers in analytics:

  • Data Analyst
  • Business Intelligence Analyst
  • Quantitative Analyst
  • Marketing Analyst
  • Sales Analyst
  • Operations Analysts

Jobs in data science use a comprehensive approach and consider the entire organization. The following are the various data science job opportunities:

  • Data Scientist
  • Data System Developer
  • Data Engineer
  • Database Administrator
  • Data Architect

Data Science vs. Data Analytics: Roles And Skills

Both data analytics and data science place distinct emphasis on data. Statistics, machine learning, mathematics, computer science, and artificial intelligence are all incorporated into data science. Contrary to popular belief, data analytics is primarily concerned with mathematics, statistical analysis, tools, and statistics.

Data analytics is focused on a certain business domain, such as marketing and sales data, whereas data science is concerned with the data of the entire enterprise. Apart from the essential data skills that are common to both, the skill sets for both are completely distinct.

  • Maths, python, statistics, predictive modeling, SQL, and machine learning are all required skills in data science.
  • Data modeling, data warehousing, data analysis, statistical analysis, and data visualization are all skills needed in data science.

Roadmap To Build A Career In Analytics Or Data Science

Due to the availability of materials, getting started in data science and navigating through it can be a difficult task for newcomers. So, here’s a simple step-by-step guide to starting a career in analytics or data science, which will come in handy if you’ve decided to enter this ever-growing field:

1. Choose the right career role

Selecting the right role is the first and the foremost step of the “roadmap to build a career in analytics or data science” guide. This step is critical because each job role has various tasks, working domains, and skill needs. You must select the appropriate role based on your expertise, preferences, knowledge, experience, and work background. For example, a software engineer may find it challenging to take on a data engineering role due to a lack of required skills.

2. Professional knowledge

The only way to break into the data field is to upskill and learn new abilities. If you want to build a career in analytics or data science, make sure you have the necessary professional skills from a data analytics degree or online data courses. These will assist you in laying a foundation and understanding fundamental concepts, which will enable you to begin working for any organization since it is a minimum eligibility criterion.

3. Networking

Networking is vital for everybody, but it is especially critical for newcomers since networking can aid. You make contacts with experts in the sector and land your first data contract or project. It might assist you in learning practical skills from people with data knowledge that you can apply to your next initiatives. To network with the right people, join peer groups, or attend data analytics conferences.

4. Learn job-related skills

As already mentioned, ensure that you have properly learned both theoretical and practical concepts of the job role you have decided. For example, data scientists need expertise in SQL, Python, predictive statistics, database mining, and more.

5. Updated resume

The ever-changing nature of data science is part of what makes it such an interesting field to work in. As the sector evolves, you’ll learn various new skills and concepts, so stay current on the latest data market trends and tools. This is also the final step of the “roadmap to build a career in analytics or data science” guide. You must keep on updating your resume with new skills and trends you learn so that the employer can differentiate you from other applicants.

Conclusion

Data science is an area that will undoubtedly develop in terms of career prospects and growth opportunities as businesses move online. So, if you’re interested in a career in analytics or data science, start by reading our “roadmap to build a career in analytics or data science” guide.

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