How to Break into Analytics

Data Analytics is a field that is in high demand, and for good reason. According to the U.S. Bureau of Labor Statistics (BLS), employment of statisticians and operations research analysts, including data analysts, is projected to grow as much as 31% from 2021 to 2031, much faster than the average for all occupations. Furthermore, the BLS projects that employment of Data Scientists, a related field, is projected to grow 36% from 2021 to 2031, again much faster than the average for all occupations. With the ability to extract insights from data, data analysts play a crucial role in helping organizations make better decisions. If you’re interested in breaking into analytics, here are some tips to help you get started.

I: Developing a Strong Foundation

Developing a strong foundation in math and statistics is crucial for success in not only the field of data analytics but in many areas of life. Having a solid understanding of math and statistics allows individuals to make informed decisions, understand complex information, and solve problems efficiently. In data analytics, math and statistics provide the necessary tools for understanding, manipulating, and interpreting data. With a strong foundation in math and statistics, data analysts are able to apply statistical methods, perform data analysis, and make data-driven decisions.

A solid understanding of probability and statistics is essential for data analysis, as these concepts form the basis for many of the techniques used. Understanding probability distributions, hypothesis testing, and statistical inference will give the ability to draw valid conclusions from data. Furthermore, understanding linear algebra, optimization, and multivariate calculus will help in understanding the underlying mathematical concepts used in machine learning and deep learning, which are becoming increasingly important in data analysis.

Brush up on your algebra, calculus, and statistics skills and consider taking a course or two to deepen your knowledge. Practice with real-world data sets and work on projects you’re interested in to apply your skills. Consider seeking out resources such as online tutorials and practice problems to help you build your math skills.

Additionally, being able to communicate complex data-driven insights to non-technical stakeholders can be challenging. It’s important to be able to explain technical concepts in a clear and concise manner. This will help you to be able to tell a story with data and make sure that your insights are being understood and acted upon.

It’s also important to consider what specific area of data analysis you’re interested in and specialize in, otherwise known as your domain. Your domain can be comprised of both your industry as well as your business function. It’s alright to choose broadly when you’re beginning your journey but you should start to think about what types of problems you’re interested in solving. Specializing in a particular domain will give you a deeper understanding of the industry, its trends and the specific tools and techniques used in that field.

II: Gaining Technical Skills

In data analysis, it is essential to have a solid understanding of basic data analysis tools such as Excel and SQL. Excel is commonly used for ad-hoc data manipulation and cleaning, while SQL is used for data retrieval and management. These tools are widely used across most industries and are essential for data analysts. To develop these skills, consider creating projects that you are personally interested in or are related to your industry.

Programming languages such as Python and R are also widely used in data analysis. These tools are commonly used for data manipulation, data cleaning, data visualization, and statistical analysis. Some industries, such as finance and pharmaceuticals, may also use tools such as SAS for data analysis. It is important to note that the choice of programming language may vary depending on the industry or the specific project. There are many free resources devoted to helping people learn these skills. For example, here is a free book I wrote helping people learn R for data analysis: https://trevorfrench.github.io/R-for-Data-Analysis/

Data visualization is an important aspect of data analysis and allows analysts to present their findings in a clear and easy-to-understand manner. Visualization platforms such as Tableau, PowerBI, and Looker are commonly used by data analysts to create interactive and engaging visualizations. These platforms provide drag-and-drop interfaces, pre-built templates, and a wide range of visualization options that make it easy to create professional-looking visualizations without the need for coding. PowerBI allows users to create visualizations for free on their personal devices while Tableau allows user to create visualizations and share them for free via their “Tableau Public” offering. Both of these options are great for learners who want to familiarize themselves with the tools. Additionally, there are open-source options such as Shiny Apps, Dash, ggplot, and Plotly for users who don’t mind coding a bit more.

With the increasing amount of data being generated, data analysts need to be able to work with large data sets. Cloud infrastructure providers such as AWS, GCP and Azure provide services such as data storage, data processing, and data management. These services allow data analysts to work with large data sets without the need for expensive on-premise infrastructure. These services also provide scalability, flexibility, and cost-efficiency, making it easier for data analysts to work with large data sets and to collaborate with others. Knowing the ins and outs of cloud computing likely won’t be necessary to secure your first role in analytics but it would definitely set you apart from other candidates. To develop these skills, consider finding tutorials or official training programs to get you started.

III: Finding a Role

As you gain experience and learn new skills, start building a portfolio of your work. This will include projects you’ve completed, data sets you’ve worked with, and any analyses or visualizations you’ve created. Having a portfolio will be useful when you start applying for data analyst jobs and can help you stand out from other candidates.

The data analysis field is a constantly evolving one, and staying connected with other analysts can help you stay up-to-date on the latest tools, techniques, and best practices. Attend meetups, join online communities, and seek out mentors in the field to expand your network and learn from others.

When searching for your first role, try reaching out to hiring managers directly and sharing projects which are relevant to what they do.

Breaking into the field of data analytics can be challenging, but with a strong foundation in math and statistics, programming skills, experience with data analysis tools, a portfolio of your work, and a network of industry contacts, you’ll be well on your way to becoming a successful data analyst.

References

Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Operations Research Analysts, at https://www.bls.gov/ooh/math/operations-research-analysts.htm (visited January 06, 2023).

Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Data Scientists, at https://www.bls.gov/ooh/math/data-scientists.htm (visited January 06, 2023).

Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Mathematicians and Statisticians, at https://www.bls.gov/ooh/math/mathematicians-and-statisticians.htm (visited January 06, 2023).