According to a recent report by LinkedIn, here’re some of the fastest-growing in-demand jobs of the past year and the next few years to come. Hiring for the roles of Data Scientist, Data Science Specialist, Data Management Analyst, Statistical Modeling has gone up by 46% since 2019. While there has been a surge in job openings, there are also some common myths co-existing with them. Contrary to popular belief, you don’t need a programming background or advanced math skills to learn
Data Science and Business Analytics skills.
This is so because most of the tools and techniques are easy to use and find ubiquitous application in all domains and professionals from vastly different industries like BFSI, Marketing, Agriculture, Healthcare, Genomics, etc. Good knowledge of statistics will need to be developed though. Also, Data Science and Business Analytics is based on the use of common human intelligence that can be applied to solve any and all industry problems. Hence, you don’t need a Fourier series or advanced mathematical algorithms to build analytical models. Math learned till 10+2 level is good enough and can serve as a starting base for professionals in all domains.
Here’re a few of the best high-paying jobs worth pursuing in this field:
1. Data Scientist
Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through “unstructured/disorganized” data to offer actionable insights. They can also do this by identifying trends and patterns that can help the companies in making better decisions.
2. Data Architect
A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also ensure that the data engineers have the best tools and systems to work with. A career in data architecture requires expertise in data warehousing, data modelling, extraction transformation and load (ETL), etc. You also must be well versed in Hive, Pig, and Spark, etc.
3. Data Analyst
A data analyst interprets data to analyse results to a specific business problem or bottleneck that needs to be solved. It is different from the role of a data scientist, as they are involved in identifying and solving critical business problems that might add immense value if solved. They interpret data and analyse it using statistical techniques, improve statistical efficiency and quality along with implementing databases, data collection tools, and data analytics strategies. They help with data acquisition and database management, recognize patterns in complex data sets, filter Data and clean by reviewing regularly and perform analytics reporting.
4. Data Engineer
Today’s companies make considerable investments in data, and the data engineer is the person who builds, upgrades, maintains and tests the infrastructure to ensure it can handle algorithms thought up by data scientists. They Develop and maintain architectures, align them with business requirements, identify ways to ensure data efficiency and reliability, perform predictive and prescriptive modelling, engage with stakeholders to update and explain regarding analytics initiatives. The good news is that the need for data engineers spans many different types of industries. As much as 46% of all data analytics and data engineering jobs originate from the banking and financial sector, but business analyst jobs can be found in e-commerce, media, retail, and entertainment industries as well.
5. Database Administrator
The database administrator oversees the use and proper functioning of enterprise databases. They also manage the backup and recovery of business-critical information. Learning about data backup and recovery, as well as security and disaster management, are crucial to moving up in this field. You’ll also want to have a proficient understanding of business analyst courses like data modelling and design. They build high-quality database systems, enable data distribution to the right users, provide quick responses to queries and minimise database downtime, document and enforce database policies, ensure data security, privacy, and integrity, among other responsibilities.
6. Analytics Manager
An analytics manager oversees all the aforementioned operations and assigns duties to the respective team leaders based on needs and qualifications. Analytics managers are typically well-versed in technologies like SAS, R, and SQL. They must understand business requirements, goals, objectives, source, configure, and implement analytics solutions, lead a team of data analysts, build systems for data analysis to draw actionable business insights and keep track of industry news and trends. Depending on your years of experience, the average Data Science and Business Analyst salary may range between 3,50,000-5,00,000. The lower end is the salary at an entry-level with less than one year of work experience, and the higher end is the salary for those having 1-4 years of work experience.
As your experience increases over time, the salary you earn increases as well. A Business Analyst with 5-9 years of industry experience can earn up to Rs. 8,30,975. Whereas a Senior Business Analyst with up to 15-years’ experience earns close to Rs. 12,09,787. The location you are situated in plays a significant role when it comes to compensation. A Business Analyst in Bangalore or Pune would earn around 12.9% and 17.7% more than the national average. Hyderabad (4.2% less), Noida (8.2% less), Chennai (5.2% less).
For those interested in upskilling,
Great Learning has emerged as one of India’s leading professional learning services with a footprint in 140 countries. Delivered 55 million+ learning hours across the world. Top faculty and a curriculum formulated by industry experts have helped learners successfully transition to new domains and grow in their fields. Offers courses in one of the most trending topics of today –Data Science and Business Analytics, Artificial Intelligence, etc.
PG program in Data Science and Business Analytics is offered in collaboration with The University of Texas at Austin and Great Lakes Executive Learning. It is becoming a sought-after course among working professionals across industries.
Here’re a few highlights:
1. 11-month program: With a choice of online and classroom learning experience. The classroom sessions strictly follow all COVID safety measures.
2. World #4 Rank in Business Analytics: Analytics Ranking (2020) for Texas University
3. Hours of learning: 210+ hours of classroom learning content, 225+ hours of online learning content
4. Projects: 17 real-world projects guided by industry experts and one capstone project towards the end of the course
- 1:1 mentorship from experts: professionals will be guided by mentors through their learning journey
- Course completion certificate: PG Certificate from UT Austin and Great Lakes
- Faculty and mentors: The faculty are alumni of reputed universities like Stanford, ISI (Indian Statistical Institute, FMS Delhi etc.)
5. Access to GL Confluence – Industry and peer networking events
At the end of this program, you will be on your way to becoming a well-rounded Data Science and Business Analytics expert. Join a successful alumnus of 25,000+ professionals, who have upskilled with Great Learning and powered ahead in their careers over the years. They have become a dependable brand for career success, today. So, enrol for the course today and build a rewarding career. Click
here to know more about the program.
Disclaimer: This article has been produced on behalf of Great Learning by Times Internet’s Spotlight team.