How to Start Your Career in Data Analytics

January 17, 2022

Earning Potential: Money isn’t everything, but it’s a nice perk when considering a career, and in the data analytics field, you can expect to earn a considerable salary. The U.S. Bureau of Labor Statistics doesn’t provide income statistics for “Data Analysts”, but it does offer income data for several roles in this field, including computer and information research scientists (average salary: $126,830), database administrators (average salary: $98,860 per year), and mathematicians and statisticians (average salary: $93,290 per year). If you’re looking for a career with good earning potential, data analytics is an excellent choice.

Demand for Related Roles is Projected to Continue Rising: Another great thing about data analytics is that the BLS projects employment will continue to rise sharply for related professions, with employment rates rising considerably over the next decade. The BLS estimates that the role of computer and information research scientists will see a 22% rise in employment between 2020 and 2030, while employment for database administrators and architects will rise 8%, and employment of mathematicians and statisticians will skyrocket by 33% over the same time period. That’s a huge increase in demand and a signal that this industry is growing rapidly.

Highly-Valued Employees: In a world where data is becoming increasingly important to everyday business operations, companies are eager to find employees who know how to use data analytics tools to help them make better decisions. As a result, people with experience in data analytics are some of the most highly-valued employees in any organization. If you’re looking for a career where you can make a real difference in organizational success, then data analytics may be the perfect profession.

Learning processing languages: This is essential to be able to clean and evaluate data sets. The most common languages for data analytics professionals are SLQ and Python, but there are many others, including R, MATLAB, and SAS.

Understanding data modeling: Data analysts need a strong understanding of how data models work so that they can properly query and analyze data sets.

Designing databases: A data analyst’s job is to make sense of the large amounts of information that companies and their clients generate, which requires an understanding of how to properly organize and structure a database.

Collecting and storing data: Data analytics professionals are in charge of gathering the data that their organization requires, either internally (via reports generated by company software) or externally (through manual research). After the data has been collected, the data analyst then must decide how to store it, and this is where things can get extremely technical. Data analytics professionals must be able to store data in a way that allows it to be easily accessed and analyzed. They must also ensure that the data is secure so that unauthorized individuals cannot gain access to it.

Analyzing data: This is where the most important part of data analytics work is performed. Data analysts employ a variety of tools and techniques to analyze raw data so that they can identify patterns, trends, or anomalies.

Visualizing the data: One of the most important aspects of data analytics work is the ability to visualize data in a way that makes it understandable to others, and any patterns, trends, or insights found within the data. To become a truly effective data analyst, you’ll need to develop advanced presentation skills so you can employ various methods and tools (such as charts or graphs) to communicate what you’ve discovered to others.

Presenting findings: After discovering something interesting or useful data, analytics professionals must interpret the data, put together the insights they’ve gleaned from the trends, patterns, etc., then present their findings to key stakeholders. These presentations may also include suggestions on how the organization should respond to the insights found by the data analyst, but in other cases, all the data analyst is responsible for presenting is insights, trends, or patterns, without strategic recommendations.

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