Data analysts are in high demand right now, with all sorts of businesses looking to hire them and many universities offering degrees on data science or data analytics. But not everyone has the resource or time to attend school and get a degree, which is why we will be looking at the steps to becoming a data analyst without a degree.
To become a data analyst without a degree, you need to first understand the job of a data analyst. Next, you need to develop a strong foundation of theoretical knowledge, followed by some technical knowledge in the domain. Then, you need to get some domain expertise and build a portfolio. Finally, get an entry-level job.
In this article, we will be looking at each of these steps in great detail. We will also be sharing some links to resources you can learn from in your journey to becoming a data analyst without a degree. Let’s get started!
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
Understand the Job of a Data Analyst
The first and perhaps the most crucial step to becoming a data analyst without a degree requires you to understand a data analyst’s job.
A data analyst uses some combination of mathematical and statistical knowledge, programming, machine learning, and domain expertise to derive useful information and insights from raw data. They are one of the most sought after professionals in the modern job market.
The useful information that data analysts derive from raw data can help businesses and organizations optimize their services and increase their profit.
We live in an era dominated by data. So it is no surprise that data analysts are high in demand in the current market. This is also one of the highest-paid jobs in the market right now.
One domain where data analytics is crucial that is very relevant to the current global scenario is studying and predicting the spread of a pandemic. There are currently thousands of data analysts crunching numbers trying to help governments and organizations around the world understand how the COVID-19 pandemic is spreading.
This is, of course, just one of the many demands of this career path. While data analysts are currently high in demand in pretty much every sector of society that deals with data, there are three main domains where they have the highest demands:
Finance
The financial sector has, for long, relied on data analytics to predict the rise and fall of stock prices in the stock market. Data analysts are more in demand today than they’ve ever been. Pretty much any firm dealing with finance or investment requires plenty of beginner and professional data analysts to be part of their team.
As a data analyst in the financial sector, a successful career trajectory will involve transitioning into a managerial role, where you will be managing the work of a team of data analysts.
Marketing
Marketing is another huge area where data analysts are high in demand. Marketing groups use data analysts to study the success or failure of marketing campaigns. This can help them optimize future campaigns accordingly.
Furthermore, marketing groups also employ data analysts for market research before launching a new product into the market. Data analysts can help them predict anything from whether or not a particular product will sell, or when or where a product has to be launched to ensure maximum sales and profit.
Sales
Sales is another domain where data analysts play a huge role. Big companies and supermarkets keep track of all the data from their product sales. Data analysts then study these data, looking for important patterns in them. The company can then use the insights from these patterns to improve their overall sales in the future.
Get the Theoretical Knowledge Needed to Be a Data Analyst
In order to become a data analyst without a degree, you will first need to get some basic theoretical knowledge of mathematics and statistics.
Mathematics
Good foundational knowledge of mathematics is an important necessity for becoming a data analyst. Just how deep your knowledge and understanding of mathematics needs to be is a widely debated topic.
Some people believe that a deep and intuitive understanding of mathematics is absolutely crucial for a data analyst to be able to develop and use models. But other experts believe you don’t need to be as worried about your mathematical knowledge.
They argue that most statistical and mathematical analysis can be done using programming libraries, so you don’t need to know as much math as you’re led to believe.
Either case, it won’t hurt to have in-depth mathematical knowledge. Besides, even if you never enjoyed mathematics in school, you may enjoy it while studying it for data analytics. This is because most of the mathematics involved in data analytics deals with real-life problems and statistics, unlike school, where you might have been forced to learn more abstract ideas.
There are few domains within mathematics that you need to place special emphasis on. Linear algebra is perhaps the most important domain. You also need to learn some probability. Regression and numerical analysis are two other important topics. You will find plenty of free courses on these subjects on Coursera, edX, Khan Academy, and YouTube. Happy learning!
Statistical Analysis
This is perhaps one of the most crucial skillsets for a data analyst. Statistical analysis involves the use of models and representations to help analyze and derive useful information from a data set.
That means it is at the center of the data analytics domain. All the mathematical, programming, or business knowledge won’t mean anything as long as you can’t actually study and comment on statistical findings derived from the data.
So we’re not exaggerating when we claim that a good foundational knowledge of statistics is absolutely crucial. You won’t have to completely master all of the other domains to become a good data analyst. But one thing you will absolutely have to master is statistics.
While there are programming libraries that can help you with statistical analysis, it is still important that you take and complete an online course first. Libraries can hide away a lot of the internal details of statistical analysis. This strategy may work in some cases, but it could completely fail in others.
For example, unless you have a deep understanding of statistics, you won’t understand concepts like confidence intervals or the importance of the right sample sizes, which will explain outliers in the statistics. Without a good knowledge of these, statistics could look as though they mean something completely different from what they truly are. And you could end up deriving wrong information from them.
Khan Academy is a great place to start learning statistical analysis. There are also plenty of courses you could take on Coursera.
Learn the Technical Skills Needed to Be a Data Analyst
Once you’ve mastered the theoretical part, the next crucial step for becoming a data analyst is the learning of some technical skills. We’ve divided this section into four parts:
Programming
While data analysts aren’t required to develop complex software like software engineers are, they will nonetheless be presented with several scenarios where writing scripts can greatly simplify a task. For this purpose, good programming knowledge is crucial to becoming a good data analyst.
Python is the language of choice for most data analysts. This is because the language is easy to learn, and then once you’ve mastered it, it is incredibly powerful as well. In addition, Python has a lot of libraries for tasks like data analysis and visualization that are a crucial part of data science. This is simply because Python was adopted early on by people in the data science domain to serve as the de facto language of choice.
Another incredibly useful language for data analysts is R. R is a language created specifically for data analysis. Python, on the other hand, is more of a general-purpose language. The learning curve for R is compatible steeper.
Learning both R and Python should be a priority for you as both of these languages have their specific use cases. Python is generally preferable for data manipulation and writing scripts to handle repetitive tasks. On the other hand, R is more suited for data set exploration, with an emphasis on ad hoc analysis.
But if you can only manage to learn one of the two languages, you should pick Python simply because of its ease to learn and a wider range of applications. The most popular data science related libraries in Python include Pandas, NumPy, Matplotlib, SciKit-Learning, etc.
Given its popularity, you will find plenty of courses online that can teach you to program in Python. There are even courses like “Python for Data Science” on edX that teach you how to use Python as a data analyst.
SQL
It is no brainer that, as a data analyst, you will be working with a lot of databases. Most of the data in the world are stored in relational databases and you will need to learn SQL (Structured Query Language) to access and manipulate those data.
A typical task as a data analyst will include extracting data from a database and then processing it to derive important insights from it. Oftentimes when faced with repetitive tasks, you might use your programming skills to write a script that automates this task.
You will find plenty of resources online to learn SQL. We recommend this “SQL for Data Science” course on Coursera.
Machine Learning
Compared to the other technical skills on this list, machine learning is a relatively new domain. In essence, it involves the use of techniques that train computers to learn and improve over time. This is different from the traditional model of programming, where every new change had to be explicitly programmed and accounted for.
Machine learning, however, helps build intelligent machines that almost learn like humans do, adapting to the changes over time. It is no surprise that this domain finds increasing use within data analytics.
One use case of machine learning in data analytics could be in the creation of a model that studies and predicts the spread of an epidemic (like the ongoing COVID-19 pandemic). The data for such an unpredictable scenario will always be skewed over time, so any hardcoded model could fail. Simply put, there are way too many variables involved, some of which we don’t even know yet.
Machine learning could be employed to build a model that can adapt and account for any change in information over time. In an intense scenario such as this, this could mean lives could actually be saved thanks to a self-learning model’s smart decisions.
Another slightly less intense scenario where machine learning could be employed is in the identification of fraudulent transactions. In the traditional model, fraudulent transactions are hard to detect, and when they are detected, it is often too late to do anything about it. Using machine learning, you could train a model to identify patterns that separate fraudulent transactions from non-fraudulent ones.
Once trained, the model will continue to learn from new data sets, over time, learning to identify fraudulent transactions instantaneously, so that they can be stopped.
Machines have been faster than humans for a long time now. The only edge humans had over machines was their ability to identify changing patterns over time. With the advent of machine learning, data scientists can now train machines to cover both these responsibilities.
Andrew Ng’s Machine Learning Course on Coursera is one of the most popular and comprehensive beginner courses on machine learning.
Business/Analytics Software
The fourth and final element of the technical skills required to be a data analyst involves learning some technical software. These include the likes of Microsoft Excel, RapidMiner, or Tableau. Of these, the most crucial one is perhaps Excel.
Oftentimes as a data analyst, the data sets you will be working with will be too large to be analyzed using Excel alone. This is where the other tools come in. But for the most part, Excel is a great tool to share your results. It is also a tool a lot of businesses already use, so communicating with Excel could be easier.
Get Some Domain Expertise and Explore Some Real-World Cases
Domain expertise is often overlooked by a lot of beginners in the field of data analytics. But it, in fact, is just as important as all of the skills we discussed above. Simply put, domain expertise refers to the intuitive knowledge of the real-world workings and intricacies of the business or enterprise one is working for.
For instance, for a data analyst looking to work for a financial institute, it makes sense to learn a bit about finances, investment, and the economy. Your number crunching and data analysis skills will only mean anything when you can actually see the patterns that an investor or financier would see and appreciate.
Or, say you’re working for a weather station, tasked with studying the weather patterns and predicting future forecasts. In this scenario, you will be tasked with collecting raw data and finding patterns in them to help predict the future weather. But unless you have a good working knowledge of meteorology, you won’t be able to see things that a seasoned meteorologist would see.
As yet another example, consider you are planning to work as a data analyst in the retail industry. You could be tasked with analyzing the data collected by a retailer from its customers. In this case, it helps to know about the retail business and also about whatever it is that the retailer is selling. If you’re working for a retailer selling clothes, for instance, a good working knowledge of fabrics and fashion could go a long way.
These are just a couple of examples, but the implication extends beyond them to every other field that requires data analysts. If you want to become a professional data analyst, you will also need to identify the industry or industries you want to work for primarily.
Once you’ve identified them, you need to learn about them alongside all the technical and theoretical data analytics skills we discussed in the previous sections. This could give you an edge in the job market.
Build Your Portfolio
With or without a degree, you will have to start your career as a data analyst with some entry-level job—most entry-level jobs quiz candidates on theoretical knowledge of data analytics. But as a self-trained data analyst, you will need an edge over someone who also has a degree to show.
This is where building a portfolio is crucial. Your data analytics portfolio should comprise some real-world data analytics problems and projects that you’ve undertaken. These could include projects from hackathons or even an internship you’ve undertaken. Either way, you will need to have a handful of real-world experience with you.
Alternatively, you could undertake your own projects. Look for challenges online. Or even better, look for problems that data scientists actually work with. Having such projects in your portfolio can really boost your chances of getting hired.
You need to know that one full-sized project will look a lot more impressive than several tiny projects. So when building your portfolio, try to have at least one or a couple of full-fledged projects in your name.
When you’re building a portfolio, make sure it looks as professional as possible. Just having a bunch of python scripts won’t be enough to impress your prospective hirers. Make sure you also include detailed reports with relevant visualizations, such as what you will be practicing as a professional. This will surely make you an impressive candidate.
Get an Entry-Level Job as a Data Analyst
This is the fifth and final step to becoming a data analyst without a degree. Whether you went to school for a degree or if you are self-trained, you will only be considered a data analyst when you start working as one. Fortunately, data analytics is a job that is currently high in demand. So if you’ve diligently considered all of the steps in this guide, you shouldn’t have a lot of trouble getting a job.
Confidence is key. All the knowledge and training in the world will not matter if you’re still insecure about not having a degree in your name. You should know that to job recruiters, a degree is nothing more than a familiar pattern that tells them that you have received data analytics training. What they’re really interested in figuring out is how valuable you’ll be to the company.
This is the main thing you need to have in mind when applying to a job or to go to an interview. Regardless of the traditional recruitment routes, if you can demonstrate competence and confidence, recruiters will see your value.
When applying without a degree, you have to really work on demonstrating your competence. Make sure to include the portfolio you built in the previous section, along with links and brief descriptions for each. When taking online courses on Coursera, try to purchase the certifications instead of just auditing the courses. If you can’t afford the fee, you can apply to their financial aid program.
Having a certification for all the relevant Coursera courses you’ve taken can go a long way for you if you’re applying to a job without a degree.
These tips apply to most companies looking to hire data analysts. But you could have trouble getting recruited by a big company like Google or Facebook without a degree since their recruitment is extremely competitive. If you want to get into these companies without a degree, your best bet is to get into a smaller company in an entry-level job, do your best to shine there, and then apply with the experience.
The Interview Process
The interview process is often the determining factor in most recruitments. This is where prospective recruiters actually meet and question you to see if you’re really as impressive as you’ve presented yourself on your CV.
Knowing all the data analytics theories and techniques won’t be enough. You will need to know when it’s right to use a particular technique and when it’s not. This is what recruiters will be looking to check more than anything.
Also, make use of everything at your disposal. If there’s a whiteboard, use it to demonstrate your thinking to the recruiters. Let them know how your mind tackles data analytics problems because the solutions you have in your mind may not be as easily expressed in words.
If the interviewers don’t have a whiteboard in the room, don’t hesitate to ask for a piece of paper. If you do eventually manage to communicate what you have in your mind, they will be impressed.
And like we’ve already mentioned several times before: Confidence is key!
Do You Need a Degree to Be a Data Analyst?
The answer is a resounding no.
Like with any other areas within the tech domain, real practical skills are much more appreciated than a college degree. Of course, in places like Facebook or Google, where the recruitment is extremely competitive, a college degree is often sought for most entry-level jobs. But with the right steps, you can find a way around that as well.
Businesses and organizations looking to hire data analysts aren’t doing it as part of a University curriculum. They are real-world entities either looking to provide a service, increase their profits, or oftentimes both. So when they hire entry-level data analysts, what they’re looking for isn’t a college degree but rather a long term investment.
Simply put, their objective is to see if, in the long run, they can make more money using you than the amount they’ll have to pay you during that time. Your job as a candidate is to present yourself and your portfolio in a manner that tells them you are a valuable investment.
Once you’ve entered an organization (any organization) as an entry-level data analyst, you will gain a lot of experience very fast. This is because you will be working on actual real-world projects with a lot at stake. You will have plenty of opportunities to shine while working on real-world projects.
Once you’ve gained some experience working for a business, you can think about applying for your dream job wherever it is you want to work at.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Contrary to popular belief, you can become a data analyst without a degree. In this article, we outlined six distinct steps for this. The first is learning what data analysts are required to do, with case studies from multiple sectors like finance and sales.
Second, you need to get some theoretical knowledge of mathematics and statistics. Third, you need to learn some programming, SQL, machine learning, and software skills.
The fourth step is to get the domain expertise of the field you want to get into real-world examples. The fifth step is to build a portfolio of your own data analytics projects and accomplishments. And the sixth and final step is to apply for an entry-level job.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
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