There are few jobs more futureproof than a data analyst. Once you become a data analyst, you will be in huge demand among companies from every industry. Many speculators expect that data analyst will be the most in-demand job over the coming years!
The LinkedIn Workforce Report states that the demand for data analysts has grown by 600% in the last five years.
So why all this fuss?
Simply: data analysts are crucial because data has become crucial. In the digital era, data is perhaps the most valuable commodity there is. Countless businesses rely on the collection and sale of that data to make money.
The demand for data analysts has grown by 600% in the last five years
Over the next several years, we can only expect this reliance on data to grow. Information technology is increasingly permeating every aspect of our lives, and the capacity to record, store, send and analyze data is accelerating. The Internet of Things remains poised to offer a wholly-connected future. Here, everything from our fridges to our plant pots will collect and transmit huge amounts of data about us. The 5G infrastructure that will support this future is very nearly in place.
All this has huge potential to improve our lives and could be extremely profitable for businesses.
If you want to be ready for that future, to be in high demand and enjoy your pick of jobs, why not become a data analyst?
Simply: data analysts are crucial because data has become crucial.
Here’s everything you need to know about how to become a data analyst, what that entails, and how to find work online.
What does a data analyst do?
The role of a data analyst is to look for useful insights in large data sets, as well as to help manage that data.
The role of a data analyst is to look for useful insights in large data sets
Companies collect massive amounts of information from a range of different sources. For example, a website will own data regarding how many people visit, the demographics of those visitors, the links they followed to get there, and much more. Data analysts use this information to demonstrate which promotional techniques are working best, and how to increase sales and profits.
Data sources and uses
Likewise, a company might be collecting huge amounts of user data from an app or piece of hardware. Google is the prime example, as it deals with gigantic amounts of information from its search queries, Chrome browser, and services. It then sells this data to advertisers. Data analysts help companies like Google to find valuable patterns in that data.
Data can similarly be collected from:
- Sales statistics
- Loyalty cards
- User accounts
- Website traffic analytics
- Market research
- Laboratory studies
- Hardware / IoT
- Health records
- Paper documents
A data analyst will also be tasked with data cleaning and maintenance. For example, if a company collects data from multiple sources, it will need to find a way to compare and combine that data in a single database. A data analyst may, therefore, be tasked with ensuring compatibility and creating algorithms that can work with all data sets at once.
In some cases, data sets may have been recorded manually in a paper format. Here, the analyst will need to update the records and digitize them.
Cleaning and massaging data
In other scenarios, data may be broken or “unclean.” A website might need to differentiate between “bots” (programs that scour the web) and real visitors. A company may be faced with forms that have been filed incorrectly.
In some rare cases, analysts may even be asked to “massage” data to achieve the desired message. For instance, if a company is up for sale, then choosing which data to report on can make it look more or less appealing to a buyer!
When you become a data analyst, you will often be given the exciting opportunity to recommend changes based on data insights. That said, you’ll also spend long hours rooting through data for errors, so keep that in mind!
Data analyst vs data scientist
Before we get onto how to become a data analyst, it’s first useful to address the difference between a data analyst and a data scientist. Is there a difference? Which is right for you?
Essentially, a data scientist does a little more with the data they work with. A data scientist will use data to make predictions, to support machine learning algorithms, etc. Thus, a data scientist needs a background in math, statistics, and programming; whereas the data analyst does not necessarily require any of those things.
As such, the data scientist will also typically earn a higher salary than the data analyst. Data analysts typically earn around $64,975 per year (according to Indeed.com), whereas the average data scientist earns $120,730.
How to become a data analyst or data scientist
No degree or qualifications are strictly required for a data analyst, as it is possible to get training “on the job.” However, they will greatly benefit from any of the following:
- Computer science
A degree in at least one of these subjects, or a data science degree, is highly recommended for anyone interested in becoming a data scientist.
Certifications and training
As well as general qualifications, prospective analysts also benefit from learning specific tools and skills. For example:
If you want to know how to become a data analyst, learn Excel! This may not be a glamorous or exciting tool, but it’s something a lot of companies still use regularly. This is a great entry point for those who want to get used to working with data.
Employers may like to see certification to demonstrate your familiarity with Excel. Fortunately, Microsoft offers an official option in its 77-727 Microsoft Certified Exam in Core Excel, which will also qualify you as a Microsoft Office Specialist. To prepare for the exam, you can also use the following course available from Udemy: 77-727 Microsoft Excel Certification – MOS Excel Core Exam.
Google Analytics is the tool that most companies, bloggers, and webmasters use to track website traffic. This tracks volume of traffic, demographics, traffic sources, bounce rates (average time spent on the site), which pages were viewed, and more.
Again, Google offers certification (The Google Analytics Certification Exam), and you can prepare for this using the Udemy course: Google Analytics Certification: Become Certified and Earn More.
Apache Hadoop / Spark
When you start getting into much larger data sets, this information may need to be stored across multiple devices. That’s where Hadoop comes in handy, as an open-source product that facilitates working with extremely large data sets across different servers.
Spark is a “cluster computing framework” that also works across multiple machines to compute big data for processing and analyzing.
Whereas smaller businesses may rely on Excel and Google Analytics, serious data analysis will require tools such as Hadoop and Spark. These are far harder skills to learn “on the job,” so familiarize yourself if you want to be a contender for the higher-paid roles.
Cloudera offers many training and certification programs that can bring you up to speed and communicate your proficiency to employers.
If you are interested in gaining skills as a programmer, and you want to know how to become a data analyst or scientist, Python is the programming language for you. Python is a highly popular object-oriented programming language that is particularly useful for web development, and that is commonly used in data analysis.
You should also learn the declarative language SQL. SQL (Structured Query Language) is a tool used for storing and retrieving data in large databases. It comes in a variety of flavors, and you’ll find that all of them can come in useful as a data analyst.
How to become a data analyst
Now you know the basics, how do you go about becoming a data analyst?
First: decide what training and certification you want to pursue (if any). If you are in the position to get a degree, this will give you a head-start against the competition and land the higher paid roles. Failing that, if you can set aside the time for some part-time learning, you may benefit from gaining certification in any/all of the above areas.
If you don’t have the time or resources to train however, you can alternatively apply to lower-paid roles, to gain more experience and expertise while you earn.
You can then begin looking through job listing sites such as Glassdoor for anything that stands out to you.
How to work online as a data analyst
If you want to know how to become a data analyst that works online, you first need to decide what kind of online work you wish to do. This will influence the type of training you complete.
You might find that a regular position within an organization allows you to work from home, depending on the nature of the data you are working with. In some cases, you’re required to be physically present though, so discuss this during your interview.
Another option is to become a freelance data analyst. Here, you will most likely sell your skills through freelancing websites such as UpWork and Freelancer. I wrote a whole guide to the top freelance websites for entrepreneurs and freelancers here, so check it out!
You can do a job search on one of these sites to see the kinds of things that will be expected of you. Usually, these listings will outline the specific types of software and data you’ll be working with. For example, you might see a post looking for an “expert in Google Sheets and Excel,” or perhaps “data analyst needed to build automated SQL server and dashboards.” Having a certification in these relevant fields can help you to stand out and land the jobs.
Keep in mind that although the role of data analyst is in high demand, the number of freelance gigs available through these sites is relatively low (owing to issues such as sensitivity of data and local network access requirements). One solution is to initiate contact with businesses with a pre-packaged service. Having a website can help with this, and will help your services appear in search.
There are other ways to make money online as a data analyst too. You can train others for example, or write technical books.
Finally, keep in mind that data analysis is a skill that is powerful as a supplement to other roles – especially in IT. Once you know how to become a data analyst, you’ll be ready for any future job role!