Seven years ago, I was a financial analyst in Ecuador and today I lead the strategy & analytics team of a German tech unicorn.
How did I do it?
There have been many factors, but the following created the foundation for this incredible adventure: During one year, I consumed all the free (or low-cost) educational content I could find on the internet related to data analytics and applied it in different ways to put together a portfolio that showed the skills I had acquired.
The most challenging part was doing it without a guide since there wasn’t one that met the following criteria:
- Designed for people with absolutely no prior knowledge of programming and analytics that want to make the following transition…
- From a finance or business role to a data analytics role
- From the consumer staples sector to the technology sector
- From a blue chip company to a high-growth startup
- Free or very low cost (less than $50 / month)
- Covering both hard and soft skills, as both are important for this transition
- 100% online content
This article is my 1.0 version of the guide I wish I had during my year of research, as it would have helped me better prioritize what content to ingest from the endless array of Google results (there are currently 548 million results for “Analytics Courses”). Knowing what to prioritize from the start would have saved me a lot of time and effort spent in trial and error.
Before diving into the fun stuff, it’s important to mention that the target audience for this article are folks with at least a couple of years of previous work experience in finance or business.
Below, you can find my 15 tips for aspiring data analysts:
- Have very good handling of spreadsheets and modeling. It is not necessary to use advanced BI tools (eg Tableau, PowerBI, etc.) or programming for every analysis or report, spreadsheets are often good enough.
- When are spreadsheets good enough?
- To easily test the first version of a dashboard or report that would require a higher effort to build in a more advanced BI platform
- When the datasets are of a manageable size for your spreadsheet tool and do not affect performance
- Preferably use Google Sheets
- Collaboration is key in startups and doing it in Google Sheets is extremely easy and intuitive
- It has formulas and ‘features’ very similar to Excel
- Easily integrates with other Google and 3rd party tools
- You should have excellent handling of pivot tables and formulas
- You should be very comfortable doing ‘data cleaning’ with formulas
- When are spreadsheets good enough?
- Know how to write SQL queries. In a startup, there are few things more important than the speed of execution. As an analyst, having the ability to query a database directly, without relying on someone else, will save you time and allow you to deliver results faster.
- Recommended course (free): https://mode.com/sql-tutorial/
- Learn how databases are structured. I can’t stress enough how much time you will save and errors you will avoid by getting familiar with your company’s database and tables from the start. What tables are there? How do they relate to each other? What fields do they have? What is the logic with which these fields are populated?
- Recommended content (free): Database Tutorial for Beginners
- Feel comfortable doing Exploratory Data Analysis (“EDA”). When getting started with a new dataset, it’s important to first understand its structure. What information does each row represent? What columns does it have? What type of data is shown in those columns? How is the data in each column distributed? Are there any cells that are empty, duplicated, or that have errors? Are there any outliers in the data? These are just some of the standard questions you’ll have to ask at first, but afterwards you’ll most like find your curiosity kicking in and driving you to explore the dataset in many different ways. Personally, I found the most fun way to learn EDA to be by inspecting other people’s work on Kaggle and then doing it myself using interesting datasets they have about companies like Netflix, Spotify, and YouTube, among others.
- Recommended content (free):
- https://www.kaggle.com/code/ash316/eda-to-prediction-dietanic (Read “Part1: Exploratory Data Analysis“)
- Recommended content (free):
- Leverage the different types of data visualization. A chart can say more than a thousand words. When working with large amounts of data, charts become necessary tools to intuitively show trends, distribution, composition, or relationships in the data. There are several types of charts and each one can have many variations, so it is important to know the ideal type for what you want to display. Eventually, you’ll find that the right chart simply feels ‘right’ in that it can be surprisingly intuitive.
- Recommended content:
- Learn to tell stories with data. After exploring your new dataset, finding something interesting that you want to highlight, and determining the chart that best shows it, it’s important to put all the pieces together to tell a story in a format commonly referred to as data storytelling. The University of Palermo describes storytelling as “an engaging narrative of events, with a final message that leaves a learning or concept” and I think it is spot on, especially the part about it needing to be engaging. Your writing should guide the reader towards your main message, with captivating visualizations offering bursts of excitement along the way to keep them engaged.
- Recommended content (free):
- Get familiar with these additional technical skills
- Learn the basics of:
- Python
- Statistics and probabilities
- Conditional logic
- Machine learning
- Recommended course ($45/month):
- https://app.dataquest.io/learning-path
- Choose the learning path called “Data Analyst in Python”
- See parts 1-6
- Then, change the learning path to “Data Scientist in Python”
- See part 7 (“Machine learning fundamentals”)
- Learn the basics of:
- Learn about the main growth metrics. Regardless of the area in which you want to work in a startup, it is important to know what the main metrics are that are measured in this type of company because (1) surely one or more of your KPIs will be related to them, (2) you’ll hear these names everywhere and (3) this way you’ll know the positive impact that your work has not only on your KPIs but on the company itself.
- Recommended content (free):
- Know what a funnel is. Frequently we hear about sales or marketing funnels, but there are many other areas that manage funnels in their day-to-day. For example, someone in talent acquisition manages a funnel of candidates to hire, someone in real estate acquisition manages a funnel of properties to buy or rent, etc. Knowing how other teams run and optimize their funnels can give you ideas on how to improve yours.
- Recommended content (free):
- Have a savings cushion. One of the best pieces of advice I have heard is that “to go to a startup you must first have a savings cushion because you never know what can happen”. Before accepting that first offer (which will surely come!) ask yourself: “In the event that the startup disappears after a month or two, will I have enough savings to support myself until I find a new role?”.
- Build a portfolio. As you acquire new skills, find ways to apply them to create content that you can turn into a portfolio with tangible proof of what you can do. For example, I mainly focused on writing data analysis articles, but there are also other types of content such as podcasts, videos, online competitions (ex: Kaggle.com), among others. I would suggest posting this content online and sending it to publications or people for feedback. Feedback is critical in order to check if you’re on the right track. Tech recruiters and managers will surely appreciate seeing these examples on your resume that show your skills and passion.
- Participate in local and online groups. Find groups or meetups of people in your city who share your interest in learning about tech and startups. In case you can’t find it locally, you can surely find it on the internet by participating in virtual forums.
- Get used to using stackoverflow.com. As you learn new tech skills (especially SQL and Python) you will have a lot of questions and you will often find bugs in your code. The odds are very high that someone else has previously had the same question, posted it on Stackoverflow.com, and received an answer. This will save you a lot of time trying to solve a bug on your own… and I do mean a lot of time.
- Offer your help to early-stage startups. Once you have acquired new skills and have put together an initial portfolio with content, share it with founders of early startups (ideally local ones) to ask for their feedback and if it is good, offer to help them (even if it is free) in some task or project where you can apply these skills. This will help you improve your network, apply your new skills in a work environment, and put it on your resume to give your application even more credibility.
- Use interviews as training. Interview processes can help you get an insider’s look at what companies and their employees are like, beyond what you can read on their website. Often, in these processes you will be given exams and case studies that will put you in the shoes of people in those roles, helping you to assess which of your new skills you have learned well and which you still need to reinforce. Every interview, whether it ends in an offer or not, prepares you in some way for the next one.
Questions or comments? You can write them in the comments section below or contact me directly here and I’ll get back to you shortly.
