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Thinking about a career in Data Analytics

Date Added: 27/09/2021

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Data analytics is a hot career and one of the most in-demand jobs in recent years. Not only data analysts are in high demand but also in short supply. As a result, the eligible candidates await huge salaries and amazing perks right from the entry-level.

According to HR surveys, heavy hiring of data analysts are on in technology companies, insurance companies, financial firms, cloud data companies, financial credit bureaus, software agencies, and consumer goods companies. 

Function-wise, data analysts gather information to help companies customize products, marketing strategies, improve production processes to optimize market share and customer satisfaction. Data analysts are essential for any company dealing with data. 

Data specialists analyze data and derive insights to help company management to make investment decisions, better targeting of customers, risk mitigation, and operational capital planning.


Good salary and perks

Data analysts draw lucrative salaries. Even in junior positions $70,000 plus per annum is common while senior positions exceeding the $100,000 benchmark.

Since data analysts also work in teams and contribute to the decision-making process, they ascend managerial positions fast.  

Data analysts scour through the large pool of data to trace trends, excavate information to make informed business decisions. These analysts are the backbone of the financial sector mainly in stock markets, hedge funds, private equity firms, and large investment banks.

A qualified data analyst will get job offers in B2B and B2C industries including finance, commerce, manufacturing, healthcare, and marketing. 

The massive data explosion is driving the demand for data analysts. Rough estimates suggest that 2.5 quintillion bytes of data are created daily.  


Data analytics functional areas 

Data analytics cover fur areas—diagnostic analytics, prescriptive analytics, descriptive analytics, and predictive analytics. The stream uses both qualitative and quantitative approaches to arrive at insights from big data tracking data points, data connections, patterns, and relationships. 

For companies, data analytics is crucial to assess their current position and to know the trajectory ahead based on customer base, competitors, business processes, and market trends.


Many jobs in data analytics

Although specialised degree courses are a main route to the job, another “passport” to enter the data analyst job market is a bachelor’s degree in Maths or Computer science. The job has a high emphasis on statistical and analytical skills. 

A certification from a reputed brand like Google or IBM can give a good start. After the entry-level data analyst job, acquiring a master’s degree in data analytics will help in career mobility. Some of the starters, mid-level, and senior profiles in data analytics are the following.

  • Data analyst 
  • Quantitative analyst 
  • Data engineer 
  • Project manager 
  • IT systems analyst 


Entry-level Data analyst jobs and skills

A graduate in a data analysis program with a good GPA (grade point average) can gain entry into data analyst jobs. 

But a degree in Mathematics, Statistics, or Economics will be a good background. For people with technical backgrounds, the following entry-level positions will be good. 

  • Statistical assistant 
  • Business support analyst 
  • Operations analyst 

Proficiency in Microsoft Excel is essential as this is important because data and number crunching are major tasks.

A strong base in statistics and distributions, binomials and tests, inferential and descriptive statistics, and statistical design help in entry-level data analyst jobs. 

A data analyst has to express in numbers and translate word-based data into mathematical expressions. For this, strong algebraic expressions and multivariable calculus are required. 

Machine Learning: In machine learning, linear algebra and multivariable calculus are core foundations for accurate predictions. Knowledge of all the three machine learning aspects such as supervised, unsupervised, and reinforcement learning are also very important. 

Knowledge of programming languages: In data analytics, programming ability is crucial to interpret data. For entry-level analyst jobs, proficiency in one programming language is a must. In data analysis, some of the programming languages will include SQL, Python, R, Java, PHP, and MATLAB.

Some of the basic roles in organizations that offer great experience and confidence will include the following roles. 

Operations Analyst: Large companies hire operations analysts and involve them in data planning for domestic reporting systems, manufacturing, distribution, and reengineering of operations. Operations analysts are hired by industries and the pay is humungous. 

Quantitative Analyst: The incumbent will study market statistics to develop analytical software and collaborate with Mathematical professionals. It will help in improving business profit. 

IT Systems Analyst: IT systems analyst designs systems to address challenges in the IT space. They also use third-party tools to test software while the use of new tools based on data analytics is not uncommon. 

Data Analytics Consultant: A data analytics consultant will yield insights from data to help growth. Consultants work for more than one client and also work remotely. 


Where to learn data analytics

There are online and offline courses in data analytics. Some of them are noted below.  

  • Business Analytics at University of Pennsylvania
  • Excel Skills for Data Analytics at Macquarie University
  • Data Analysis and Presentation by PwC 
  • Google Data Analytics by Google
  • IBM Data Analyst by IBM
  • IBM Data Analytics with Excel and R by IBM
  • Introduction to Data Analytics by IBM
  • Data Analysis and Visualization Foundations by IBM
  • IBM Data Science by IBM
  • Data Science at Johns Hopkins University


Promising outlook

Tech major IBM’s survey has predicted that the demand for data professionals in the U.S. market alone would surge by a quarter million in 2020 and beyond. That suggests the use of data as a growth engine will continue to grow. 

Companies are making big investments in data processing as noted by the study of Dresner Advisory Services. The big rate of data adoption in enterprise businesses jumped from 17 percent in 2015 to 60 percent and above in the last few years. 

The Dresner study also notes that big-data analytics adoption has been the maximum in Telecommunications followed by insurance, advertising, financial services, healthcare, and technology sectors.

Big hiring trends in data analytics are also corroborated by the McKinsey study that shows how the U.S. healthcare industry is leveraging big data to improve efficiency and service quality. It also suggests large retailers using big data will have the advantage to expand operating margin by more than 60 percent. 

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