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A Typical Career Path For Any Data Analyst

In recent years, there is a lot of hype about a career in data analysis. As this is one of the most highly paying fields, freshers are keen on pursuing a career. However, not many people are aware of a data analyst’s work, roles, and responsibilities.

With people from backgrounds making careers in data analysis, it is understandable for you to be a little confused about this profession’s prerequisites and the career path to expect. In this article, we throw light on all that you need to know about a data analyst’s career path to help out of this dilemma.

Becoming a Data Analyst

You need to have a background in mathematics, economics, statistics, or core data science to begin your data analysis career. If you are making a switch from some other profession, then professional certifications will form the bridge for you. This also holds for people from non-technical backgrounds who are keen to make a career as a data analyst.

Taking up internships and personal projects help you get an edge over the competition. While skills are given a higher weightage over grades, having a good academic performance is an added advantage while applying for entry-level data analyst positions.

Once you work in such a role, you will have a better understanding of what appeals to you, enabling you to chalk out your career path.

The typical career path involves one starting a career in an entry-level position and then choosing the industry they want to associate themselves with. Every industry has some specific data analyst roles, and people usually choose the role after deciding on the vertical they want to work in.

Data Analyst Career Trajectories

While a look at any junior data analyst job description may seem like the industry expects the same skillset from every candidate, the fact is that the roads diverge as you move higher in the corporate ladder. Let us look at the various roles you can consider as you continue your career journey in the path of data analysis.

Business Analyst

Every industry has come industry-relevant data that is useful for businesses operating in the vertical. A business analyst is a data analyst who works exclusively on such business-specific data.

Insurance Underwriting Analyst

As an insurance undertaking analyst, you will be expected to analyze the data of your client and guide them on insurance plans. The client in question may be an individual, company, or group of companies.

Compensation and Benefits Analyst

The human resource team of any larger organization has some people in the compensation and benefits analysis role. Such data analysts investigate employee compensation data and come up with ways to optimize company costs and employee retention.

Fraud Analyst

With increased data manipulation, financial organizations need to be on their toes and monitor organizational data for fraud. A fraud analyst works on such data to minimize the risk of cheating and fraud. In the event of fraud, the analyst works on the data to identify the root cause and come up with a solution.

Actuary Analyst

As an actuary analyst, you will work on data revolving around disability, sickness, accident, and retirements. You need to come up with probability tables, liability planning, and risk forecasting. Insurance companies hire data analysts in this role.

Corporate Strategy Analysts

Senior data scientists who analyze company data and use their deduction to steer strategic growth planning are known as corporate strategy analysts. Such people play an instrumental role in the mergers and acquisitions of a firm.

Web Analyst

In a digital era, there is a rising demand for a web data analyst. One works on a dashboard of facts and data around a website, topic, or trend in this role.

Budget Analyst

No business can function without a well-planned budget, which makes the role of budget analysts indispensable to any organization’s functioning. In this role, you will be expected to focus on analyzing a budget and helping curtail unnecessary expenditure of the firm you are working for.

Management Reporting Data Analyst

Management reporting is a mid-level position wherein one is expected to report data analytics to higher management. In this role, one is expected to advise the business management on everyday functioning based on the data’s deduction.

Credit Analyst

From credit monitoring to reporting, there is a host of datasets in the credit market. A credit analyst works on computing the lending risk, approvals and establishes a detailed lending analysis. All financial bodies have multiple people working in this role.

Sales Analyst

With technology having a massive impact on all our lives, companies are looking to shorten their sales cycles and making it more effective. In such a situation, sales analysts focus on understanding sales data and supporting and improving the sales process.

Machine Learning Analyst

Machine learning is an emerging field of data science, and several experienced data analysts are now considering making a move to this field.

A senior data analyst job description states that one must have a fair amount of data feeds, numerical analysis, and programming systems. It is the same skillset that is needed to be a machine learning analyst.

Business Product Analyst

A business product analyst works on data surrounding the physical attributes and characteristics of a product. Such a person guides a firm’s management on the optimum pricing of its products and their packaging.

Social Media Data Analyst

Most tech companies depend on data to monitor and grow their business. As a social media data analyst, you will be analyzing the data from the different social media sources and giving your inputs on driving your brand’s success story. In most organizations, the marketing team works with such analysts to facilitate digital marketing initiatives’ success.


It is predicted that in the coming years, data will be the most valuable human asset. While the definition of the ‘’typical’’ data analyst career path may change in the coming years, their importance and career scope will continue to improve.

In each of the trajectories discussed above, there is ample scope for growth. As you use your knowledge in data sciences to chalk out your professional success story, here’s wishing you the very best for your journey ahead.