How to Become a Data Analyst with No Degree


How to Become a Data Analyst with No Degree

A data analyst is a bit of a ubiquitous job title—while the root responsibilities are the same, the specific day-to-day tasks vary widely. You’ll find data analysts in every industry, and it’s a job that isn’t going away anytime soon. In fact, it’s only getting more popular. The Bureau of Labor Statistics estimates that the demand for data analysts will grow by an astounding 20 percent in the next decade. The skill sets you’ll use are widely transferrable, so if you're considering a career change and long-term economic stability is important to you, data analysis deserves a look.

What is a Data Analyst?

A data analyst gathers information and then organizes and evaluates it to collect insights. The information can be quantitative or qualitative, and an analyst’s job can vary broadly on the ratio of human interaction and sequestered data interpretation. On the more customer-facing side, data analysts are involved with designing surveys and other measurement tools that are specifically fashioned to solicit the kind of data they need to collect. At the other end of the spectrum, your job might involve poring over spreadsheets daily, examining tables of numbers for hints that will guide your organization’s future.

It’s equally important to realize what a data analyst is not, and this can most clearly be illustrated by comparing the role to that of a data scientist. While analysts are expected to have moderate math, statistics, and coding skills, scientists should display mastery in these arenas. Analysts are often more concerned with understanding what the data indicates about what has happened or what is currently the case; scientists make a career out of taking past and present information and converting it into predictions about future trends.

Data Analyst Salary: How Much Do They Make?

A data analyst’s salary can vary widely based on several factors, such as the cost of living in the business area, seniority, supervision, and the amount of coding and in-depth analysis someone is expected to do. According to Glassdoor, the national average base pay is $62,500 per year, with the low end averaging $43,000 and some of the most senior-level analysts pulling in $95,000. To give a point of comparison, data scientists pull in around $113,000 per year on average and peak at approximately $154,000.

Tangible skills can make a substantial difference in pushing an analyst’s paycheck toward the right side of that scale. On the more common end, mastering Microsoft Excel and having solid presentation skills are foundational, while knowing how to code in SQL, R, or Python can provide a noticeable boost on the higher end.

Data Analyst Training: What Skills Do You Need?

As we mentioned before, data analyst roles vary greatly. Some data analysts primarily work with customers. Other data analysts are indistinguishable from developers. However, most data analysts involve several universal skill sets:

  • Proficiency with one or more programming language
  • Data visualization and report generation

Companies rely on analysts to look at mountains of data and interpret that into tangible recommendations the business can act on. This also involves understanding what kind of information you’ll need to consider and starts with learning at least one programming language (and probably more).

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Learn a Programming Language

Data analysts should learn how to retrieve and manipulate data, which means either learning a programming language or being proficient with a data tool like Tableau or PowerBI.

The most commonly used coding languages in data analysis are SQL and Python. The latter is incredibly flexible and is used in all areas of data analysis, particularly when designing models to analyze, interpret, and illustrate trends in specific scientific disciplines. It’s a high-level, general-purpose, interpreted language, which makes it easy to learn. On the other hand, SQL is a domain-specific language with a single purpose: querying databases.

To oversimplify the differences, Python is used to create, structure, and populate databases, while SQL is employed to access, read, and analyze those databases. Both languages will come in handy as a data analyst.

Data Analysts Must Visualize Data

Producing reports is another crucial skillset. Being able to glean information from data is one thing, but converting that into an easily digestible format for non-data analysts in the C-Suite to understand and implement is another. Reports can be pulled from complicated databases or may need to be artistically rendered in a presentation; whatever this looks like in your case, understanding how people think and how to tell a story with your data is vital.

Finally, data analysis is never a solo job. You’ll collaborate with others at every step of the way, from receiving your assignments on what data to analyze and what you’re trying to understand to brainstorming conclusions with coworkers and presenting reports up the organizational hierarchy. Companies know that data analysis is a core hub in a complex machine, and collaborating and communicating well with others is a non-negotiable requirement.

How to Learn Data Analysis

Data analysis is a mixture of soft and technical skill sets. The technical skills are easier to quantify, so let’s start there. Finding online or in-person courses to master Microsoft Excel and Google Sheets is easy and straightforward. If you’re not already performing at an expert level in spreadsheets, prioritize this first. Focus on the more advanced data manipulation tools already inherent in these software systems—you’ll use them daily.

You can take a similar approach to learn Python or SQL. Begin with an online course or a boot camp and join a development community like GitHub or DevNet. Create projects on your own and begin coding based on your interests; you’ll move much more quickly and gain a broader range of skills by letting your curiosity drive you.

On the soft skills side, having a strong basis in formal logic can only help. You can engage in self-study by reading books on the topic, to begin with, but it’s always preferable to study under someone with a formal education in this area. Online courses abound, as do offerings at local universities and community colleges. Other soft skills like teamwork, communication, and presentation abilities can be developed anywhere. Adopt the mindset of "blooming where you're planted" and look for opportunities in your current job.

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Portfolio: How to Build Your Code Portfolio

Data analysts without a degree have to rely heavily on showing they can do the work. For a technical profession like data analysis that means building a code pile — or a code portfolio.

One of the most impressive things you can show a hiring manager during an interview is that you're already doing the job for which you're applying, and the easiest way to do that is with a code portfolio. Use the universal nature of data analysis to your advantage and begin looking for data to analyze in your current life. If you're a help desk technician, dive into your ticket data and slice and dice until you find which days a particular load is highest, what that tells you about scheduling decisions, and which tech resolves certain types of problems most quickly. If you work at a grocery store, ask the produce manager if you can analyze ordering and pricing trends to streamline and enhance profitability.

Any area in life can be made better with effective data analysis, which means you can use virtually any opportunity to build up your code portfolio.

How to Get an Entry-Level Data Analyst Job

The standard answer you’ll receive to this question typically begins with: “You need a four-year degree in math or computer science.” That is, by and large, an outdated requirement that hasn’t been completely purged from HR job descriptions (or mindsets). The substantial growth that this field has seen makes it physically impossible to meet the demand with the supply of college graduates in these and related fields. Although a bachelor's degree in these areas can certainly help, it isn't required.

In the absence of a degree, proving skills become much more important. Showing that you can program in Python or SQL demonstrates you have the technical acumen to succeed. Pursuing projects on your own and delivering tangible results validates your creative and analytical thinking skills. Having a results-oriented presentation conveying what you learned from your projects establishes that you have the communication abilities necessary to succeed as a data analyst.

Certifications can also be valuable, from Microsoft Certified Azure Data Scientist Associate to broader validations like the Associate Certified Analytics Professional. These not only indicate that you can do the job itself, but earning them also clearly conveys how serious you are about making this a career.

The future is bright for data analysts, and earning your spot among them isn’t difficult if you’re determined. Prioritize learning the skills you’ll exercise daily and use every chance you currently have to embrace those learning opportunities.

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