How to Prepare Yourself for a Career in Data Analytics

Businesses are dealing with more data than ever before. It is no surprise that the number of data and analytics jobs has exploded by 650% since 2012, according to LinkedIn. This demand opens up exciting opportunities for university graduates seeking to build data and analytics careers.
There are many ways to enter this field, even if you did not plan your academic career around becoming a data scientist or analyst. Let us look at six practical steps to get ready for a job in data analytics, regardless of your major.

1. Build your foundations with SQL

Those who want to get started in data analytics need to understand Structured Query Language (SQL). This is important because SQL allows you to query and manage data directly from relational databases — a crucial skill for data engineers who must work with raw information and turn it into usable information. Understanding how a relational database works conceptually is also required in any data and analytics job.

That said, SQL does have its limitations, which leads us to the next item on this list.

2. Master a programming language (or two)

There is no way to overstate the importance of computer programming abilities if you are interested in a data-heavy industry like data analytics. Learning Python, Java, and R goes a long way in this field. These languages offer vast libraries, enabling you to do more sophisticated levels of data manipulation. Learning these coding languages also allows you to clean, analyze, and visualize open data, a skill required in data and analytics jobs.

Many colleges and universities offer courses in this area. But if yours does not, hundreds of online resources are accessible at a fraction of the price — some completely free. The most crucial step is selecting a language and developing a mastery of it. Doing this takes time and a lot of perseverance, but once you have mastered the basic principles, more complex features become easier to grasp. This also applies to learning additional languages, as fundamental concepts and techniques apply to multiple coding languages.

3. Statistics is important for data science

You may think you are ready to begin working with machine learning algorithms after mentioning coding skills on your resume. But without a firm grasp of descriptive statistics and probability theory, you will not be able to extract the prescriptive and predictive insights offered by statistical modeling methods or artificial intelligence tools. This is why it is important to take course or module in statistics.

If your current course load leaves no time for immersive lessons, at least take an introductory statistics course. There are many online programs available and can help you better solidify that statistical foundation by supplementing your coursework.

4. Learn about business intelligence and data visualization platforms

Aside from being able to analyze and decipher data, you must communicate it in a way that different audiences and stakeholders can understand. Hence, it’s a good idea to learn how to use the likes of Tableau and Power BI. These are designed to put raw data into business contexts so that even users without an analytics background can use them to make essential decisions. These end-to-end data analytics solutions make it easier to prepare, analyze, share, and collaborate on big data insights.

5. Create a portfolio using real data.

Working with data in real-world situations is the best way to dip your toes into the data analytics industry and build a compelling portfolio, even if you do not have work experience.

Look for programs or courses with practical projects that use real-world data sets. You can also use freely available public data sets to design and complete your own projects. For example, you can delve deeper into climate data from government sources, use data from reputable news sites, or even design solutions using open data from Google dataset search.

Make a point of saving your best work for your portfolio as you explore data sets on the internet or complete hands-on projects at school. A good portfolio allows potential employers to gauge your abilities and will help you land the job.

Curate your portfolio to demonstrate your capacity to clean raw data, scrape data from various sources, present your findings using graphs, charts, maps, and other visualizations, and derive actionable insights from data. Consider including one of your previous group projects, if you did any. This demonstrates that you can collaborate as part of a team.

6. Improve your soft skills.

Earning a STEM degree (science, technology, engineering, or mathematics) is a solid first step toward starting a data and analytics career, but it is not enough. After all, a job in data analytics is not just about technical proficiency; it is also about communication and teamwork. Thus, in addition to technical abilities, hiring managers look for people with these soft skills.

As a data analyst, you will work with various users and global audiences. Therefore, it necessitates communication, problem-solving capabilities, confidence, and the ability to learn from others. Data analysis teams tend to be massive and collaborative, so you will encounter a wide range of professional expertise and personal viewpoints. When working in teams to solve a problem, you must know how to advocate for the benefits and advantages of your own ideas while also listening to and appreciating other suggestions.

Investing in your future career

Acquiring these new skill sets is not easy, but it is certainly possible with perseverance and proper time management. The good news is that most of them can be learned on your own time using online resources that are either free or less expensive compared to on-campus courses.

The learning journey should be fun and exciting if you enjoy working with numbers and solving puzzles. And if you find yourself losing motivation, remember there is a long list of data and analytics jobs locally and internationally. It is up to you to make sure that you are ready.

About the author:

Ambar is a Marketing Consultant at InfoCepts and provides business development, marketing, and sales enablement expertise to help promote business growth and improve brand awareness. He has worked on numerous go-to-market strategies, external campaigns, global events, and has helped create content that genuinely adds value. Working closely with clients, Ambar has helped build innovative solutions across technology practices like data & analytics, cloud, AI, robotics, hyper-automation, and application modernization. He has more than 10 years of experience, holds a master’s degree in business administration, and a bachelor’s degree in engineering.


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