The 2023 Data Analyst Starter Pack

There’s never been a better time to be a Data Analyst. In the current era of explosive data availability and every company realizing the value of becoming “data-driven,” it’s no surprise that there’s been a surge in demand for the Data Analyst role. This role turns data into business value and actionable insights, paying a critical part in steering the company in the right direction. Now that’s impact!

And with demand, comes supply - an increasing number of people have shown curiosity and interest in transitioning into this sought-after role, which is why we’re put together this Starter Pack with everything you need to know to get started. We’ve noticed that there are still more Data Analyst job openings than candidates due to a skill gap that seems daunting to overcome.

These days, every company needs analysts - not just the bigger enterprises, banks, or consultancies. There’s an infinite range of industries, small startups and SMBs that are in need of your skills. Whether you’re a recent university graduate or 10 years into an unrelated career path, we believe that there are just a few core skills and qualities that you should master to transition into your dream Data Analyst role.

While tons of resources exist to guide you on how to succeed, many of them will encourage you to learn 3+ different programming languages and complete a Master’s in Machine Learning - we’re here to argue that you don’t need all that! While a background in Math or Statistics and knowing programming languages like Python and R are a plus, we’re here to boil it down to a few of the most important ones for landing that first Data Analyst job. And no, they don’t require $50k in training courses and paid fancy certificates 🤗.

1. Learn SQL

SQL is the #1 skill that underscores everything you’ll do as a Data Analyst, and it’s here to stay.  Learn basic SQL as your primary programming language, and don’t worry so much about Python or others.

For context, SQL, short for “Structured Query Language,” is a programming language that’s used to manage and manipulate data stored in relational databases. It’s a powerful language for organizing, storing, and retrieving data - enabling data teams to access and work with large amounts of data quickly and effectively. It introduced the concept of accessing many records with a single command, so you can see how useful it is if you’re dealing with data sets of any size.

With SQL, users can create tables and databases to store data, as well as to query, update, and delete data from these tables and databases. You can also filter, sort, and perform calculations to generate insights and reports.

Needless to say, SQL is the baseline, and you should really start your transition by learning basic SQL. Not only is SQL widely used in the industry, there are many tools in the Modern Data Stack that are entirely SQL-based - such as Whaly, Hex, and dbt. These tools don’t require a proprietary language or Python or other coding requirements.

Resources:
SQL for Beginners by Kevin Stratvert
Learn SQL on Codeacademy
The Structured Query Language on Coursera
The Complete SQL Masterclass by Udemy (paid)
SQL for Data Analysis by Udacity

2. Become an Excel whiz

It’s important to be proficient at Microsoft Excel as a Data Analyst. Ideally, take some time to geek out on Excel and become a power user.

While modern, robust BI platforms have made it so that using Excel for analytics and reporting is (rightfully) seen as “overly basic” in the perspective of data professionals, it is powerful and encompasses a lot of functionalities that take you through the analysis process.

As you’re probably familiar, Excel spreadsheets allow users to structure data, apply formulas and make calculations, perform data aggregation, represent data visually and graphically, calculate margins, and more.

Similarly to SQL, Excel is a minimum necessity and expectation, as the root of how analytics as we know it started. Excel has long been the preferred tool for business professionals for analyzing and displaying data. More often than not, Excel spreadsheets will be the most common format in which your end business teams are accustomed to consuming data. If you want your users to understand and benefit from your analyses, you’ll be wise to know how to be a whiz on Excel - even if you do have a robust BI platform. There’s no doubt that it continues to be the most popular data analytics tool, widely used across different functions, from Marketing to HR to Finance. Plus, if you do end up joining a company that uses Microsoft Power BI as their platform, knowing Excel will be very helpful since it leverages the core structure of Excel in its design.

Last but not least, becoming proficient in Excel means you can perform analyses and get started on your own analysis projects to build up your portfolio as you transition to your new role, even if you don’t have a BI platform.

Resources:
Excel Data Analytics Full Course by Simplilearn
Mastering Data Analysis in Excel (paid Udemy course)
Full Project in Excel by Alex the Analyst

3. Start Answering Questions & Practice Data Storytelling

Start working on answering questions that require querying certain data set(s), and telling a crisp story around your answer. Data visualization refers to how you use graphs and charts to show data outcomes and tell a compelling story with the data that leads to insightful clarity and next actions. In data visualization, you are demystifying and removing complexity from your analysis to convey a message to a certain audience. Non-technical (business) audiences should be able to gain a clear understanding of your concepts that are backed by data - even if they don’t have any analytical background. Usually, this requires taking things a step further than visualization - it involves crafting a narrative in a way that’s most impactful and easy to grasp for your audience.

Here’s our advice on how to approach this:

• Start with the “why” and the “who” by defining who your audience is, and the purpose of the analysis. What would your audience like to know?
• Determine suitable data sources and sets - what are the data sources that you need to answer this question? Collect this data.
• What are the main concepts or takeaways that you’d like to showcase based on the data? What should your audience implement or do after reviewing your data visualization? Ensure that the data is clean and accurate according to what you want to present - Google Spreadsheets and Excel have tools for cleaning.
• Select / produce relevant visualizations accordingly, that tell your story
• Is there any complementary data that’s required to get your point across? If so, add them to your story
• Craft an overall narrative in a way that’s clearest and most striking to your audience. To achieve this, feel free to complement with other visuals or text write-ups. The end goal is always tied to your audience and the purpose of your analysis.

Doing this efficiently and consistently takes practice. To achieve this and enhance your abilities, a proficiency with visualization and BI tools would be extremely helpful - particularly if you need to model data together from different sources.

Many companies have a BI tool in place, some of the biggest ones being Microsoft’s Power BI or Tableau. Having a basic understanding of these tools is helpful. Be in the know about other up and coming BI tools, like Whaly, particularly if you’re looking to join a smaller company. BI tools like Whaly include several parts of the data stack in one, so you can streamline the process for getting insights.

Resources:
Top 7 BI tools by Whaly

4. Start Your First Project for Your Portfolio

This is the exciting part - when you start applying your learnings, and having tangible projects to show for it! When you start applying for jobs, hiring managers and your future boss will want to see how you approached and completed an actual analysis project - and what you learned from it. You’ll need to start building a portfolio 1) to have something to show to stand out, and 2) to get that much-needed practice so that your interviews aren’t so daunting, 2) to show your ability to reduce complexity, simplify messaging, tell a story with takeaways

We recommend doing some guided practice projects with  Alex the Analyst on his free YouTube channel.

When starting your own first project, think about whether you have access to a data set around a topic that you’re passionate about. Perhaps fitness/running data if that’s your thing - if that’s the case, do you have data from your FitBit or Apple Watch that you could analyze? If you’re into movies, is there a data set on movie trends? Starting your first project around your passion points means that you’ll already have an understanding of the subject matter, and an elevated interest in the project itself. Hiring managers can tell when you’re presenting your project with passion - and if you fill your portfolio with passion-fueled data projects, you’ll come off strong!

While Alex the Analyst and other courses do guide you through a specific project, these generic ones that are pre-defined by the instructor are not going to be effective in helping you stand out.

Resources for finding data sets:
A roundup of data set sources by LearnSQL.com
Google trends
Data.gov
Kaggle.com
Data.world
9 Best Analytics Portfolios
How to Tell a Compelling Story with Data

Other than these skills and steps, here are the key traits to embody that will set you up for success. Do these come naturally to you? If not, don't worry. They may still develop, and there are always ways to improve as you go along!

Critical Thinking & Big Picture Understanding

Understanding your audience and the overall purpose of your analyses is crucial. Your ability to think “big picture” and always tie it back to “who” and “why” will allow you to tell your story in a clear way that maximizes clarity and understanding. No matter what step you’re at in your analytics project, you should also be able to identify areas that look off or unclear, using critical thinking skills to sharpen the focus.

Communication

As a Data Analyst, you’ll be working cross-functionally, since you’re often the one bridging the gap between the technical data team and the business teams. You’ll be dealing with plenty of personas even within business teams - from Sales reps, VPs of Growth, to Marketing leads and CSMs.

Depending on the size of the company you join and what their data team set-up is like, you may find yourself working closely with the Data Engineering team, who are responsible for ETL / data integration, warehousing, transformation, etc. It will benefit you to develop trust and strong relationships cross-functionally across both technical and business sides.

Data analysts also often have to be reactive, being asked to deliver data for an urgent purpose. The ability to communicate your timings and priorities will be important in managing your workload.

Curiosity

Curiosity is an important quality for data analysts because it allows them to explore data sets, ask questions, and uncover insights that may not be immediately apparent. After all, you’re spending most of your time answering questions through complex queries that require a methodological process. A curious mind will make this entire process more natural and appealing to you.

Data Analysts who are curious are more likely to look beyond the surface level and dig deeper to find answers to complex questions. This can lead to more in-depth analysis, and can help you identify trends and patterns that might otherwise go unnoticed. Additionally, curiosity can help you think creatively and come up with new solutions to problems.

Resilience & Persistence

Data analysis can be a complex and challenging field. You may encounter obstacles or setbacks in you work, such as data that is difficult to clean or interpret, or problems with your tools or methodology. In these situations, resilience and persistence can help you overcome these challenges and continue making progress in your projects.

Bouncing back from failure or or challenges, and the ability to continue working towards a goal will help you stay focused and push forward.

An Interest in Business / A Business-Focused Mindset

As a Data Analyst, your role is closer to the business side than any other data role (e.g. Data Scientists or Data Engineers). Your role involves develop a strong understanding of business goals and the business context behind the numbers and data. Your company’s revenue objectives and leveraging data and dashboards to steer your company’s teams towards a “north star” should be in your best interests. This is how you make a real impact at your company and instantly become the hero - helping everyone back their key decisions with data.

When you land that interview, here are some resources to help you prep and shine:

• HackerRank (SQL focused technical interviews)
• DataLemur (SQL focused technical interviews)
• Data Analyst Interview Questions by Alex the Analyst
• Top 5 Data Analyst Interview Mistakes by Alex the Analyst
• SQLBolt (to refresh on SQL)

Books to consider:

• The Big Book of Dashboards by Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave
• Storytelling with Data by Cole Nussbaumer Knaflic

We hope you found this resource valuable, and that you'll keep this in your back pocket as you transition into your Data Analyst role! Remember, it's never too late to switch into Data Analytics. There's no time like the present, especially as demand is predicted to continue to rise into 2023 and beyond. Good luck!

We’re also excited to see how this role might evolve over time with new tools that present new opportunities for different organizational structures of data teams.

We'd love to hear your thoughts, feedback, and journey! Reach out to anna@whaly.io with any comments.