We'll tell you how to become a data analyst and what you need to do to hone your natural abilities into analytical talents that employers are looking for. We'll also give you an idea of where your data analyst skills could take you in your career, and how you can tailor your skill set to appeal to employers in the industry you choose.
Figure out where you want to go
Before we start mapping out your career path, it's important to get a clear idea of where you're headed and what you can expect to find when you get there.
Who is a Data Analyst (data analyst)?
These data-savvy professionals use a wide range of methods in their work. These include, but are not limited to, programming, machine learning, mathematics, statistics, and data collection to produce data-driven insights.
Although the niche responsibilities data analysts take on can vary by industry, company, and job, most professionals spend their workdays combing through huge databases for trends, patterns, or other observations. These observations allow them to form informed opinions about recent and long-term trends, make predictions, and share their thoughts with company stakeholders.
Data analysts are especially valuable to businesses because they can identify trends and create predictive models before problems or costly strategic mistakes occur. Data analysts can also help companies assess the likelihood that products and services will succeed by evaluating the performance of ads, platforms, and business website pages among target audiences.
Data analysts are often associated with the technology sector; however, they're by no means limited to it. In fact, data analysts can work at major investment banks, private equity firms, medical technology companies, and even public-serving nonprofit organizations.
Today, practically every modern business has access to a huge amount of data—so much that these collected treasures can't be put to use without experienced professionals. But with data analysts who clearly understand how to deploy data analytics, the benefits of collecting these information treasures are hard to overstate.
In 2018, the business intelligence company Microstrategy surveyed companies across industries and around the world about their use of data. Its researchers reported that 60% used data analytics to improve process and cost efficiency, 57% applied it to manage strategy and change, and 52% relied on data analysts to monitor and improve financial performance.
Given these benefits and our growing reliance on both data and the information it offers, it's no surprise that data analysts are in high demand. According to an 2019 Indeed report, the number of data specialist job openings grew by a staggering 31 percent year over year.
“Data work and analytics used to be largely limited to the technology and finance sectors,” LinkedIn Learning analyst Paul Patron wrote about the need for talent. “Now nearly every industry, from retail to manufacturing, collects data on its customers. This is fueling a surge in demand for data specialists who can best interpret all this data. And yet, because it's a relatively new job, few professionals have this skill set. As a result, there are few data specialists in most American cities—not just in financially and technologically oriented ones like San Francisco and New York.”
Which fields need data analysts?
Here are some of the most data-dependent industries:
Retail
Retail is a highly competitive industry, so companies that can't correctly identify emerging trends risk falling behind their competitors and losing favor with consumers. With data analytics properly deployed, retailers can anticipate what their customers want and then deliver it.
Finance
The banking industry uses predictive analytics to improve customer service, increase efficiency, and limit operational problems such as fraud.
Manufacturing
Data analysts working in manufacturing often use predictive analytics to assess the likelihood of system failure and schedule repairs before any equipment breaks down and affects production schedules.
Healthcare
With predictive analytics, healthcare organizations can get a comprehensive picture not only of how their facilities are being used, but also of how to optimize their existing resources. This capability is invaluable because it allows providers to optimize patient care and provide the best possible care to as many patients as possible.
Transportation
Organizations in this field can analyze collected data to examine how people might use transportation services and identify an increased risk of problems such as excessive traffic, collisions, or demand. These predictive insights allow transportation officials to adjust travel and service schedules and keep all modes of transport running smoothly.
How can I become a data analyst?
Get foundational training. If you ask anyone in the field for advice on getting into data analytics, their immediate answer is almost always the same: education and training.
Regardless of the industry they eventually work in, all data analysts need to develop a core set of skills. Let's look at some essential competencies you'll need to acquire before you ever join a hiring team.
Fundamental statistics
It's hard to get very far in data analytics without some familiarity with statistical thinking and theory, especially if you're going to specialize in machine learning. A basic understanding of statistical principles will improve your ability to classify information, create clear data visualizations, and use advanced data sampling techniques
Python
Python is one of the most popular programming languages in data analytics. Analysts use pre-installed modules and an extensive collection of libraries to extract, clean, and visualize data, as well as build analytical models.
Excel
You may know the basics of Excel, but most people don't fully realize just how advanced spreadsheet analysis can get. This essential office software is not only intuitive, flexible, and easy to collaborate with, but also incredibly useful.
Data analysts can import information from external databases, then use built-in Excel features such as pivot tables and VBA scripts to quickly analyze it and create easy-to-understand tables and reports.
SQL/NoSQL
Both SQL and NoSQL are languages used to communicate with databases. The difference between them lies in the types of databases each language can work with.
SQL, or Structured Query Language, is the standard language used to communicate with relational databases—that is, databases that store related data points according to their relationships. It can be used to update, retrieve, or delete information from these databases.
NoSQL, as its name suggests, is often used to query non-relational databases. Although data analysts should be familiar with both languages, they should prioritize learning NoSQL. Non-relational databases (and, accordingly, NoSQL) don't face the limitations imposed by a relational structure, making them well suited to processing large datasets. This capability makes them popular with many businesses that collect large volumes of information, and therefore increasingly relevant to aspiring data analysts.
Hadoop
Hadoop is popular open-source software that provides vast amounts of storage for any type of data. The platform can perform parallel tasks, making data analysis much less labor- and resource-intensive.
Tableau
Tableau is a useful tool that allows analysts to isolate data according to various factors; this lets analysts focus on specific regions, demographics, or outliers to better understand a dataset.
Frontend development skills
Most data analysts need to communicate their findings to clients, other departments, or company stakeholders. CSS and HTML can help analysts create attractive online platforms for displaying their data-rich visualizations.
HTML, or HyperText Markup Language, is used to describe the basic structure of a web page. Developers can use defining “tags” to place images, paragraphs, input forms, and other features on a site.
CSS (Cascading Style Sheets) describes how HTML elements should be styled, affecting font size, colors, spacing, and other aspects of design.
How to get these skills
It seems like a simple question: how do you become a data analyst?
But the answer is complicated. There's no “right” path in analytics. There are several ways to acquire the skills you need to grow professionally; you can invest in a four-year college education, enroll in a shorter boot camp program, study on your own, or combine all three. For employers, how you gain your skills matters less than the fact that you have them.
