Career Guide

How to Become a Data Analyst

If there’s one word that’s been especially popular in tech job postings, that would be “data”. But don’t just chalk this up to trendiness. The amount of useful data generated through users’ actions online is staggering and businesses want to capitalize on it.

The need for great data analysts is growing and is showing no sign of slowing down, meaning salaries are very competitive. It’s a new enough field that most people have no idea how to get started in it. Read on for a guide to what’s involved with the job, what sort of training you’d need to thrive in it, as well as what you can expect financially in a data analyst career.

What are the benefits of starting a career in data analytics? Check out these statistics:

According to the Bureau of Labor Statistics, data-related positions are expected to grow by 16% over the next decade. Specifically, data analytics jobs are expected to increase an impressive 23% in that timeframe.

Data analysts in the U.S. earn a median income of $61,473.

The demand for qualified data analysts will increase steadily in the future.

Experts predict that an astounding 40 zettabytes of data will exist by 2020.

$61,473

Median Data Analyst Salary

40,000

Open Job Listings

8,000

Hiring Companies

What Is Data Analytics?

Data analysis is the work of taking data and pulling useful information and predictions from it. This doesn’t just involve looking at rows of numbers. An analyst needs to examine the data for patterns of behavior and any possible trends that might determine future results. It also means cleaning and modeling the data to make it more useful.  

The end result of this work is better and more efficient decisions made by businesses or other large organizations. 

What Do Data Analysts Do?

The field of data analysis is open ended enough that each position is going to somewhat unique in its duties. There are some common traits with all analysis jobs. It would be worthwhile to first look at some data analysis basics.

Data analysis can be broken down into roughly four different areas of analytics: descriptive, diagnostic, predictive, and prescriptive.

Descriptive Analytics

Descriptive analytics covers what has happened. This is the fundamental job of analysis: looking at existing data and explaining what it can tell you about past behavior. This could be as simple as how many units of a product were sold last month, or as complicated as pulling in data from a variety of sources to examine overall costs. This provides companies with a lot of what but not much why.

Diagnostic Analytics

In diagnostic analytics, historical data is set against other data to try to understand (for example) why profits were down last month. This involves pulling any and all other data to search for correlations. Beyond number crunching, this involves some human analysis and speculative thinking.

Predictive Analytics

If descriptive and diagnostic analysis is about what happened, predictive analysis is about – you guessed it – what’s going to happen. This involves taking the data from the past and using it along with human reasoning to make accurate predictions of future behavior. This is where the real decision making can come into play for a business, as they can use those predictions to determine what products to market heavily, where to spend money, or any number of company actions.

Prescriptive Analytics

Finally, prescriptive analytics are to help decide what actions to take in the future to get ahead of potential problems or seize a latent opportunity. The work from the preceding three forms of analysis help shape this data so that an organization can make logical and fruitful decisions for their future.

Data Analyst Job Description

The responsibilities of a data analyst varying with employer and specific positions, but there are some general tasks that are common to any analyst position. Any data analyst professional will be collecting data, sift through that data looking for patterns or trends, and produce reports based on their findings. This process will inevitably involve collaborating with others to gather than information and present it in a useful manner.  In short, the data analyst job is designed to help companies make informed business decisions.

More specifically, a data analyst will spend a lot of time making data queries on databases (usually with SQL. More on that below), perhaps using self-coded programs to extract that data more efficiently. They will analyze it, possibly using mathematical tools and technical skills to present their findings in reports for a variety of audiences to understand.  Statistical analysis is also an important part of the role, similar to the responsibilities of a data scientist.

What Are the Required Skills for a Career as a Data Analyst?

There are a variety of so-called “hard” and “soft” skills required for a career in data analysis. While you shouldn’t write yourself off if you’re currently lacking some of these skills (particularly the math and programming related ones), this should give you a good guide for what you’ll want to address in your current skill set in order to pursue a thriving data analysis career.  

Data Analyst Hard Skills

Mathematics

There’s no question that mathematics plays a crucial role in data analysis. You should acquire a good basis of basic statistics, like that you’d have from a one year course. As you advance in the role, other mathematics could be involved in your job, like multivariate A/B testing, predictive modeling, and cluster analysis. But for starting out in the field, statistics should serve you well.

Data Management

As you can imagine, most of a data analyst’s role involves working with data. This involves collecting data and organizing and manipulating it using data technologies. The fundamental language of data work is SQL: Structured Query Language. This is the language you would use for any data acquisition and examination. There are other query languages you might use (such as HiveQL), but SQL will underlie any data queries you’d do.

Programming

There is a strong coding component to data analysis work. The most popular languages used are R and Python. R is specifically designed for statistical computing and working with large data sets. Python is a scripting language that’s often used for automating processes and for creating complex data visualizations. These two languages that have a lot of learning resources (including a number of bootcamp courses) available for them.

Data Analyst Soft Skills

Collaboration

Because the data an analyst works with comes from across every department of a company, a good data analyst needs to be able to work with people in a variety of areas. Although the programming aspect of their work may suggest they remain a subset of an IT department, the analyst will need to be able to work successfully with any area of a company.

Business Skills

For valuable information to get to the right decision makers in a company, an analyst also needs to understand what makes a good decision for their organization. This requires them to understand their industry well, as well as their competitors and audience. A strong business acumen comes in very handy in both analysis work and communication with others in your company. This includes communication skills and the ability to work with a team.

Data Visualization

It’s not enough to present spreadsheets to your decision makers. Sometimes data hides in a table and its full power isn’t understood until presented in an effective visualization. Data analysts need to tell a story with their data and since a picture says a thousand words, that data often needs to be presented visually to get its message across. Note that this doesn’t mean thoughtlessly including graphs for graph’s sake, but rather using design to get an intended point to its audience.

How Much Do Data Analysts Make?

According to Glassdoor, data analysts in the U.S. make an average base salary of $61,473 annually, but the range varies widely across the board and between organizations. The Robert Half Technology 2019 Salary Guide puts that range at $81,750 to $138,000. No matter what source you turn to, the outlook is good.  

Data analytics is a complex field, and workers are in demand–plus, the probability that demand and salaries will increase over time is good. Below, we compiled a table with the average data analyst salaries in 15 large U.S. metropolitan areas.  

Obviously, what sort of salary you can command depends on factors like education, geographical region, and years of experience. Nonetheless, the table can give you a good idea of what to expect in the field of data analytics.

CityAverage Salary
San Francisco, CA$81,837
Los Angeles, CA$64,919
Portland, OR$62,798
New York, NY$65,196
Philadelphia, PA$58,449
Seattle, WA$64,886
Minneapolis, MN$65,838
Atlanta, GA$59,546
Phoenix, AZ$57,881
Boston, MA$64,261
Miami, FL$57,090
Chicago, IL$59,604
Milwaukee, WI$59,342
Cleveland, OH$57,707
Dallas, TX$62,043

$61,437

Mean Annual Salary

23%

Projected Job Growth (2014-2024)

2,300,000

Number Employed

Data Analyst Salary by Years of Experience

  • Average Salary

Data Analyst Coding Languages

Coding is an important part of data jobs, and data analysts should be well-versed in a variety of languages. Each individual job will have different coding requirements, but data analysts are usually familiar with at least a few common languages. These languages vary in use, purpose, and format, but each is tremendously important for certain aspects of the job. Below, we’ve included 3 examples of common coding languages in the field of data analytics.

Python

Python is a well-known and extremely useful general purpose programming language. This language is a high-level option for a wide range of tasks--which partially explains its popularity. Python is used in many other fields, and programmers generally consider it a ‘user-friendly’ language. In data analytics, Python is a powerful tool. Artificial intelligence and machine learning systems often rely on Python as well.

R

R is a scientifically-designed programming language, made specifically for analyzing data. And while many programmers don’t consider it to be as user-friendly as Python, it’s very useful for data analytics. R makes it easy to analyze data and display the results in a way that’s easy to understand. Future analysts would benefit greatly from mastering the R programming language, especially for business analytics and data visualization is part of the job.

SQL

SQL is another popular language in the field of data. SQL is a querying language, used for managing databases. However, for very large data sets (known as ‘big data’) Another similar language called Hadoop is more useful. Nonetheless, data analysts are likely to encounter SQL on a frequent basis in many positions.

How to Become a Data Analyst

If you’re starting to look at undergraduate majors for data analysis, the prime areas are mathematics (especially statistics) and economics. Today there are even graduate level programs and statistics for data analysis specifically, something that wasn’t the case twenty years ago. 

If you’re coming to the field after college or after having worked in another career for a while, you’ll want to get yourself a solid statistics and programming background. A bootcamp program can get you started with courses in statistics for the math and Python or R for the programming.

A coding bootcamp in particular will last anywhere from three months to a year and give you the support you need to learn new job skills while not ignoring the experience you’ve had already in the workforce. Think of it as an accelerated college class that’s designed for an experienced adult. 

Note that if you’ve already learned another programming language (perhaps through a bootcamp!), this experience will be very helpful in learning R or Python. Programmers are accustomed to learning new languages and usually do so much faster with each new one they pick up.

Data Analytics Learning Paths

Data Analytics Bootcamps

Coding bootcamps are fast, short-term career education programs. They last an average of 6 months, and are available full-time, part-time, and online. Bootcamp students don’t need a college degree to enter into the workforce.

College/University

Data analysts often come from college programs, including math, statistics, and computer science. While universities offer degrees, they are often unaffordable. Additionally, a bachelor’s degree typically takes 4 to 6 years to complete.

Self-Study

Some people find an entry level data analyst position without any formal education. Highly motivated people can train themselves for a career using online resources and books, often for little to no cost. However, this approach requires time and an enormous amount of dedication.

What Companies Are Hiring Data Analysts?

Facebook

Cybersecurity Engineer Positions: 13

Average Salary: $108,000

Apple

Data Science Positions: 10

Average Salary: $95,000

Amazon

Data Science Positions: 27

Average Salary: $60,000

Google

Data Science Positions: 245

Average Salary: $103,000

2020 Best Data Analytics Bootcamps

FAQ

Will coding bootcamp help me get a job?

Absolutely! Coding bootcamp is a proven way to train for a job in tech. Many coding bootcamps offer job guarantees, and some refund tuition if graduates can’t find a job in the field they trained for. In fact, coding bootcamp teaches skills that many college computer science graduates lack.

How much do coding bootcamps cost?

Coding bootcamp tuition varies. Coding bootcamps in New York City cost around $10,000 to $20,000. However, many scholarships and tuition deferment programs are available, so what you see doesn’t have to be what you pay.

What are income share agreements?

Income share agreements, or ISAs, are a new way to pay for education. These programs defer tuition until after students graduate and find a job in the industry. Once students are employed and making above a certain income threshold (usually $40-60,000 per year) they begin paying a fixed percent of their income, often for around 2 years. If students can’t find a job, many bootcamps waive the cost of tuition.

Do I have to learn coding to work in the tech industry?

You don’t have to learn coding to work in the tech industry. In fact, there are several non-coding bootcamps in New York City. These programs train you for a position in tech sales, marketing, or product management–all of which are well-paid positions with plenty of advancement opportunity.

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