Today, have gained the status of being all-pervasive across functions and domains. The mammoth wings of data and analytics are determining how we buy our toothpaste, how we choose dating partners, and how we lead our lives.
Nearly 90% of all small, mid-size, and large organizations have adopted analytical capabilities over the last 5 years to stay relevant in a market where large volumes of data are recorded every day. They use it to formulate solutions to build analysis models, simulate scenarios, understand realities and predict future states.
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 Modelling 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 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. A 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 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, today:
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 an “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 interpretation and analysis 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. 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, and objectives, Source, configure, and implement analytics solutions, lead a team of data analysts, build systems for data analysis to draw actionable business insights, 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 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).
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This content was originally published here.