How to Become a Data Analyst is one of the recurring question I get on Quora. This is not surprising of course, as it is a very rewarding career. Since writing the same answers over and over is quite redundant I have decided to group the information into one post.
As a Senior Data Analyst (and soon to be manager) I get to interview many data analyst candidates. I’m not saying this to brag, but because it has given me a very good sense of what skills are necessary to score a Data Analyst job.
In this article, I will share with you details on the education, technical skills, and soft skills that employers are looking for to fill a Data Analyst position.
Data Analyst Education
A bachelor degree is expected from data analyst candidates. The degree should preferably be in Mathematics, Statistics, or Computer Science.
If you have a degree in another field you can still become a data analyst. Keep in mind that the less quantitative your degree is, the more you will need to prove your analytical skills. For example, If you have a bachelor in History hopefully you also learned why the mean and the median of a normal distribution are always equal in your spare time. If you find yourself in this situation you can:
- Learn the required technical skills (more on that later) by reading books, watching videos, enrolling in online courses, or going back to school.
- Concentrate on learning skills in a practical sense. What I mean is that we don’t care if you know the precise definition or a normal distribution, we want to know if you can calculate a 95% interval around the mean.
If you don’t have a degree, then the truth is: You won’t be invited to many interviews. Even if you make it to the interview it will be very hard for you to convince people to hire you over someone with a math degree. If you are in that situation you can:
- Go back to school (Highly recommended!)
- Apply to small businesses and startups who might consider gambling and hiring you.
- Build a portfolio of projects and network online.
Data Analyst Technical Skills
As a Data Analyst, your role is to dissect data and understand its structure. To do so you need, by order of importance, the following 3 skills: Math/Stats, Programming, and Data Engineering.
Math & Statistics
The application of Mathematical & Statistical concepts to data is the core skill of a good data analyst. The rest are tools to apply these math/stats concepts. Here’s what you need to master as a data analyst:
- Descriptive Statistics: Size of sample, max, min, mean, median, mode, standard deviation, variance, quintile, confidence interval, hypothesis testing, probability functions, etc.
- Business & Marketing Measures: YTD, YoY, MoM, rolling averages, CPA, CAC, CPC, CPI, AOV, usage, etc.
- Time Series: Decompose trend and seasonality, calculate daily distributions, forecast, etc.
You need to have a general mathematical and logical reasoning to solve your employer’s problems.
Imagine a data analyst copying data from a SQL table to a spreadsheet manually. Now imagine him decomposing a time series by hand. It wouldn’t make sense, right?
Technology is here to help us process all that data, and that’s why we write programs and scripts. Here are the main languages you need to know:
- SQL: This is by far the most important programming language you need to learn. SQL let’s you extract and manipulate data from relational databases. Most companies use SQL for everything from testing to production. If you did master SQL yet, I encourage you to enroll in my Job-Ready SQL Server online course.
- Excel & VBA: I know, these tools sound like a prehistoric hand axe. What you need to realize though is that most companies built their whole reporting infrastructure on Excel & VBA. Also, Excel is still a good tool to do some quick and dirty Exploratory Data Analysis.
- Python and/or R: Python and R are used more and more in businesses today, because they are free, computationally more efficient than Excel & VBA, make it easy to share code with colleagues, and have hundreds of statistical libraries.
Logical thinking is a must since all programming languages share the same main concepts.
As a data analyst you will be dealing with data all the time. You won’t need to take care of the whole process of data engineering, but you will need to be good at certain aspects of it.
The reason is that most companies don’t have Data Engineers, and so data analysts have to take on that work.
Here are the area of Data Engineering that you need to master:
- Data Cleaning: Before analyzing your data, and most importantly drawing conclusions, you need to be 100% sure that you can rely on it. To do so, you need to clean your data by making sure it’s accurate and well formatted.
- Scheduling: You will sometimes have scripts that you will want to run every morning. You need to know how to do your own ETLs because most often than not BI will be too busy to do it for you.
- Automating tasks: Remember that SQL query that you rewrote 10 times because you never saved it? You should either save it, or even better functionalize it using Python or R. You can also automate plenty of calculations often done in Excel.
- APIs/Scraping: Sometimes the data you need is external, so you need to use APIs/Scraping to get it. This is particularly useful when the need to merge internal data with external data arises.
If you want to be independent and take initiatives you will absolutely need Data Engineering.
Data Analyst Soft Skills
Would your believe me if I told you that most candidates are rejected not because of their technical skills, but because of their lack of soft skills? Sadly this is often the case.
Here are the soft skills that you need as a data analyst:
- Ability to take initiatives: This is similar to curiosity, but on steroids. A good data analyst will have the drive to dissect the data in every angle to generate insights. Then, these insights can be brought to the attention of colleagues and management to improve things. This is the “always pushing forward” mentality.
- Ability to present data: You’re convinced we could try raising prices in Japan because of your new, fancy, and complex sensitivity analysis? How do you present to management what you see in the data? How do you get your message across without losing your audience and keeping them interested? This is both art and science. You will need to do this by creating presentations, charts, dashboards, and vulgarizing the concepts.
- Organization: As a data analyst you will have a ton of projects to work on, and often at the same time, and due yesterday. You need to be highly organized, and I recommend using an agile project management tool to be the most efficient as possible.
- Ability to learn: With new technologies you will continuously need to be learning. Are you coachable? Do you have the necessary humility to recognize you’re not always the best? Can you take criticism?
- Team player: You can’t do everything. If someone comes to you with a problem that you can’t or don’t have time to solve, can you direct them to the right person? Will you be able to be there for the team and help them when needed? Can you share your knowledge and help the team become more efficient?
This is not the complete list, but definitely the most important soft skills I look for in a candidate.
Becoming a data analyst is not easy. It requires you to have education, to understand math/stats concepts, and to master various technical and soft skills.
Even though most data analysts have a bachelor degree in math, stats, or computer science, it is still possible to make it into the field with a non-quantitative degree. It is a lot harder if you don’t have any degree, but like anything, not impossible.
The maths/stats concepts that you need to master as a data analyst revolve around descriptive/business statistics and trend analysis.
When you think of technical skills, think about all the tools that can help you extract data, manipulate data, or apply statistical/mathematical/computational operations to your data. SQL, Excel, VBA, Python/R, are a must!
Soft skills on the contrary are oriented towards you and how you can evolve in a work environment. Taking initiatives, as well as presentation and organizational skills are absolutely necessary.
Do you have what it takes to be a data analyst?