How to Become a Data Analyst in 2022

Data analytics jobs can be found in a variety of industries, and there is more than one way to get your first job in this in-demand field. Here are some steps to becoming a data analyst, whether you're just starting out in the professional world or changing careers.

Data analysts gather, clean, and study data to help guide business decisions. If you’re considering a career in this in-demand field, here's one path to getting started:

Get a foundational education.

If you're new to the world of data analysis, you should begin by learning the fundamentals of the subject. Getting a broad overview of data analytics can help you decide if this is the right career for you while also providing you with job-ready skills.

Most entry-level data analyst positions used to require a bachelor's degree. While many positions still require a degree, this is changing. While a degree in math, computer science, or another related field can help you develop foundational knowledge and boost your resume, you can also learn what you need through alternative programs such as professional certificate programs, boot camps, or self-study courses.

Build your technical skills.

Getting a job in data analysis typically requires having a set of specific technical skills. Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired.

• Statistics
• R or Python programming
• SQL (Structured Query Language)
• Data visualization
• Data cleaning and preparation

Take a look at some job listings for roles you’d like to apply for, and focus your learning on the specific programming languages or visualization tools listed as requirements.

In addition to these hard skills, hiring managers also look for workplace skills, like solid communication skills—you may be asked to present your findings to those without as much technical knowledge—problem-solving ability, and domain knowledge in the industry you’d like to work.

Work on projects with real data.

Working with data in real-world settings is the best way to learn how to find value in it. Seek out degree programs or courses that include hands-on projects with real-world data sets. There are also a number of free public data sets available that you can use to create your own projects.

Investigate climate data from the National Centers for Environmental Information, delve deeper into the news with data from BuzzFeed, or use NASA open data to devise solutions to looming challenges on Earth and beyond. These are just a few examples of data available. Choose a topic that interests you and look for data to practice with.

Get an entry-level data analyst job

After gaining some experience working with data and presenting your findings, it’s time to polish your resume and begin applying for entry-level data analyst jobs. Don’t be afraid to apply for positions you don’t feel 100-percent qualified for. Your skills, portfolio, and enthusiasm for a role can often matter more than if you check every bullet item in the qualifications list.

If you’re still in school, ask your university’s career services office about any internship opportunities. With an internship, you can start gaining real-world experience for your resume and apply what you’re learning on the job.

Consider certification or an advanced degree.

As you move through your career as a data analyst, consider how you’d like to advance and what other qualifications can help you get there.

If you’re considering advancing into a role as a data scientist, you may need to earn a master’s degree in data science or a related field. Advanced degrees are not always required, but having one can open up more opportunities.

If you are someone who is looking for a headstart in a career in Data Analytics or Business Intelligence; some relevant statistics might prove to be really encouraging.

 

1. It is estimated that by 2023, over 33% of Business Enterprises will resort to Decision Intelligence.

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2. Data Analytics streamlines and expedites the process of decision-making, making it 5x faster.

3. The Business Intelligence market on a global scale is expected to grow to $33.3 billion by 2025.

4. 7 out of 10 Business Enterprises rate the discovery of data as extremely important.

5. The Covid-19 pandemic propelled the adoption rate of Business Intelligence.

Data Analytics and Business Intelligence Course at Syntax Technologies

Syntax Technologies' Data Analytics and Business Intelligence course (DA/BI) is one of the best training programs on the market. The program is designed to train people with little to no programming experience to become data professionals who combine analytical and programming skills - using data manipulation, data visualization, data cleansing, and other techniques to make sense of real-world data sets and create data dashboards/visualizations to share your findings.

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What are the best types of data analytics?

Market and customer insights are critical for business success. However, obtaining those insights has always been difficult. In today's digital age, you require a data analytics solution that combines the best of analytics and data management capabilities to quickly and easily access and analyze the information you require—when and where you require it.

What are the best types of data analytics?

The best type of data analytics for a company depends on its stage of development. Most companies are likely already using some sort of analytics, but it typically only affords insights to make reactive, not proactive, business decisions.

More and more, businesses are adopting sophisticated data analytics solutions with machine learning capabilities to make better business decisions and help determine market trends and opportunities. Organizations that do not start to use data analytics with proactive, future-casting capabilities may find business performance lacking because they lack the ability to uncover hidden patterns and gain other insights.

Four main types of data analytics

Predictive data analytics

Predictive analytics may be the most commonly used category of data analytics. Businesses use predictive analytics to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling; however, it’s important to know that the two go hand in hand.

For example, an advertising campaign for t-shirts on Facebook could apply predictive analytics to determine how closely the conversion rate correlates with a target audience’s geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two (or more) target audiences and provide possible revenue values for each demographic.

Prescriptive data analytics

Prescriptive analytics is where AI and big data combine to help predict outcomes and identify what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in ML, prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome.

Descriptive data analytics

Descriptive analytics is the backbone of reporting—it’s impossible to have business intelligence (BI) tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.”

Once again, descriptive analytics can be further separated into two categories: ad hoc reporting and canned reports. A canned report is one that has been designed previously and contains information around a given subject. An example of this is a monthly report sent by your ad agency or ad team that details performance metrics on your latest ad efforts.

Ad hoc reports, on the other hand, are designed by you and usually aren’t scheduled. They are generated when there is a need to answer a specific business question. These reports are useful for obtaining more in-depth information about a specific query. An ad hoc report could focus on your corporate social media profile, examining the types of people who’ve liked your page and other industry pages, as well as other engagement and demographic information. Its hyper specificity helps give a more complete picture of your social media audience. Chances are you won’t need to view this type of report a second time (unless there’s a major change to your audience).

Diagnostic data analytics

While not as exciting as predicting the future, analyzing data from the past can serve an important purpose in guiding your business. Diagnostic data analytics is the process of examining data to understand the cause and event or why something happened. Techniques such as drill-down, data discovery, data mining, and correlations are often employed.

Diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two more specific categories: discover and alerts and query and drill-downs. Query and drill-downs are used to get more detail from a report. For example, a sales rep that closed significantly fewer deals one month. A drill-down could show fewer workdays, due to a two-week vacation.

Discover and alerts notify of a potential issue before it occurs, for example, an alert about a lower amount of staff hours, which could result in a decrease in closed deals. You could also use diagnostic data analytics to “discover” information such as the most qualified candidate for a new position at your company.

Data Analytics and Business Intelligence Course at Syntax Technologies

Syntax Technologies' Data Analytics and Business Intelligence course (DA/BI) is one of the best training programs on the market. The program is designed to train people with little to no programming experience to become data professionals who combine analytical and programming skills - using data manipulation, data visualization, data cleansing, and other techniques to make sense of real-world data sets and create data dashboards/visualizations to share your findings.

Read More