Roadmap to Becoming a Data Analyst

What is a data analyst? What are their daily duties, and what skills do they need? In this article, I discuss the role of data analysts and share a step-by-step guide on how to become one.

As organizations start to realize the value of their data on their customers, target audience, competitors, suppliers, and other market players, the role of data analysts becomes more important.

To become a successful data analyst who can bring real value to the organization, you need to possess a specific set of skills. This includes the ability to extract and analyze data from relational databases. And as SQL is a standard programming language for communicating with databases, SQL should be definitely on the top of your list of skills to acquire. You can start learning SQL right away with our interactive SQL Basics online course.

To help you understand why SQL and other skills discussed below are essential for the role of a data analyst, let’s start by exploring who data analysts are and what kind of tasks they do.

Who Is a Data Analyst?

Data analysts collect, analyze, and report valuable insights found in data. The daily duties of data analysts include:

• Collecting data from a variety of sources.
• Preparing the data for analysis (i.e. cleaning the data).
• Performing an exploratory data survey.
• Modeling and analyzing data.
• Creating data visualizations and reports.

There is a lot of hype around this profession, but it’s hard to overestimate the role of data analysts in today’s data-driven organizations. With proper data analysis in place, a company can understand its customers better, improve the targeting of marketing campaigns, organize better logistics, improve HR management, prevent fraud, and much more.

For an even better understanding of a data analyst’s role, let’s list some activities that data analysts are likely to be tasked with when working on, say, a big project aimed at reducing customer churn. The data analyst might:

• Use different data sources to collect information on customer dynamics over the last few years.
• Clean the data, which among many other things includes fixing missing data, applying consistent formatting, and ensuring the compatibility of data collected from different sources (e.g. all measurement data is in the same units).
• Detect anomalies in the data and explore possible explanations. For example, let’s imagine there was a huge customer outflow in March-April 2020. Was it due to COVID-19? Are there other possible explanations?
• Analyze the data from different perspectives and with different tools. For example, the analyst may see if it makes sense to group churned customers in several groups to better understand why they left. Or they may look for factors that are leading indicators for customer churn. They may also use some simple machine learning models to assist with data analysis.
• Visualize the data, or use it to create charts, plots, etc. This helps them see patterns that are not easily detectable with other analytical tools and check out data distribution, correlation, and linear/non-linear relationships between different variables.
• Report findings to management after discovering interesting patterns and valuable insights that indicate specific action items likely to reduce customer turnover. Reports shouldn’t be too technical and should never go beyond the data; a good analyst always allows for a multitude of possible interpretations.

As you see, data analysts can impact all strategic decisions a company makes; no wonder this career pays off. Indeed reports that the average salary for a data analyst in the United States is $70,218 per year. If you have less than one year of experience, you are likely to earn around $66,552 per year, while a data analyst with 10+ years of experience usually gets $83,000 and up.

Are you already excited about becoming a data analyst? Let’s see what kind of skills you would need to be effective in this role.

7 Steps to Becoming a Data Analyst

To succeed in this career path, we’ve mentioned that you’ll need a specific set of skills. Here are seven steps that will help you acquire them.

1. Learn Statistics

To spot real trends, patterns, and causal relationships, you need to be familiar with basic statistical concepts such as significance, predictors, response variables, leading indicators, lagging indicators, hypothesis testing, etc. Just building a correlation graph is not enough to understand the true relationships between variables and all the underlying processes and interactions. Learning statistics is one of the key first steps to becoming a data analyst.

2. Get Really Good at Excel

Even though Excel might not be the most effective tool for data analysis – especially when working with large amounts of data – you are still likely to find lots of company data stored in Excel spreadsheets.

HR managers might use Excel to track information on employee training. Supply chain managers may track their supplier contacts in Excel; sales teams might use Excel to calculate their monthly bonuses. While large companies will usually have database management systems for all these use cases, smaller companies still rely on Excel.

So, as a data analyst, you should be very familiar with Excel. You should be able to collect data from spreadsheets and know when it can be more effective to do data analysis in Excel. Excel is a very powerful analytical tool, but as you’ll learn with experience, SQL often works better for data analysis.

3. Learn SQL

Companies of all sizes usually store most of their data in relational databases, and SQL is a language used to interact with relational databases. So, if you want to be able to extract data from these databases and then work with it, you should learn SQL.

In big organizations, you can probably rely on database administrators and other “IT guys” to extract data from relational databases. However, knowing SQL lets you retrieve data on your own, giving you additional speed and independence. Data analysts with SQL knowledge can respond more quickly to requests, and thus add more value to their companies.

Furthermore, SQL doesn’t just extract data – it is also a very powerful analytical tool. You can use SQL queries for marketing analytics, creating SQL reports from Google Analytics, doing sales analytics, and even conducting complex financial analyses.

SQL is one of the most essential data analyst skills these days. Luckily, its syntax is quite easy to master, especially with high-quality learning resources. If you have little prior experience with SQL, I recommend starting with our SQL Basics course. Once you are ready to become a SQL master, consider completing our interactive SQL from A to Z and SQL Reporting tracks.

4. Dig Into Python

Python is another essential  tool for modern data analysts. Of course, data analysts don’t need the same programming skills as software engineers or developers. But they are expected to know how to clean data, explore and visualize it, and build simple machine learning models with Python. If you want to do data analysis, you should be familiar with Python’s popular data analysis and visualization packages.

If you haven’t mastered Python for data analysis yet, check out the interactive courses on our sister site, LearnPython.com. You may want to start with the Python for Data Science learning track; it’s designed specifically for aspiring data analysts and data scientists.

5. Discover Data Visualization

Good data visualizations often help us unearth hidden patterns and understand complex relationships between different factors. Creating meaningful, valuable, and visually appealing charts is one of the key skills that any data analyst should master.

You can use Excel, Python, R, Tableau, or any other tool to create your visualizations. But remember that these charts should be based on reliable information to be useful for decision-makers. They should also be accurate (not slanted to a particular viewpoint) and easy to follow.

6. Tackle a Few BI Tools

To become an effective business data analyst, you should also master Business Intelligence (BI) tools – software used to collect, organize, visualize and analyze data accumulated through business operations.

Some examples of popular BI tools include Tableau, Microsoft Power BI, Oracle Analytics Cloud, Qlik, and Domo. Different companies prefer different BI tools; if you know which organization you want to work for, just focus on the tools they use. If you don’t have this info yet, any of the tools on this list of top 10 tools for business analytics will be a good starting point.

7. Get Industry experience (optional)

Companies rarely require industry experience from data analysts, but it can definitely be a key advantage. If you have gained some experience in a specific industry, it might be easier for you to find a job in this domain – even if your previous role was not directly related to data analysis. Understanding a domain will help you differentiate between patterns that are really important for business and those that are irrelevant or insignificant.

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Want to Be a Data Analyst? Learn SQL!

High-quality data is crucial for effective data analysis, and the ability to extract data from relational databases with SQL is a must-have skill for data analysts. Fortunately, SQL has a very accessible syntax and is quite easy to learn. As I mentioned earlier, you can get started with our SQL Basics course. It covers the most important SQL topics, such as retrieving data from one or more tables, aggregating and grouping data, and combining query results from multiple tables.

If you want to go beyond the basics, check out our SQL from A to Z learning track. It has seven interactive courses that cover the breadth of the SQL programming language, including advanced features like window functions, common table expressions, and recursive queries.

Finally, if you want to learn how to create complex, multi-level reports in SQL, consider taking the SQL Reporting learning track. In three interactive courses, the track covers basic SQL reports and also more advanced business-focused reporting, such as revenue trend analysis and customer behavior analysis. The courses include real-world examples that can be applied to different domains and industries.

Bonus. Read these five data analysis books to jump-start your career.

Thanks for reading, and happy learning!