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Before Big Data became a thing, small businesses worldwide were already using data to achieve business objectives. The local florist would use historical sales data to determine inventory levels and seasonal demand. Restaurants use data to determine popular items on the menu and rush periods to determine how much of a particular thing to have available and at what time of the day. This daily use of data allows businesses to be more efficient with their resources, minimize losses, and maximize sales. These days Data is big business. Companies like Meta - the owners of Facebook and Instagram- rely on data to drive consumer advertising, which helps them to generate billions of dollars in profits each year.

As the world advances towards a digital economy, businesses are using data to perform complex tasks and gain insights into business operations to achieve their business objectives. It is only enough to gather large volumes of data if you also have skilled personnel who can analyze and gain insights from that data. The demand for data professionals has steadily increased over the last few years. And it will keep rising as businesses build out online operations as the world moves to a digital economy.

There's no better time than right now to begin your journey to becoming a Data Analyst. But before we let you know how to become a Data Analyst, let's first examine who a Data Analyst is and what their job entails.

Who is a Data Analyst?

In simple terms, a Data Analyst is someone who, through self-learning or formal education, has acquired the skills and knowledge to collect, sort, analyze and interpret large datasets, often to uncover insights that organizations can use to drive their business objectives.

A Day In The Life Of A Data Analyst

We know that Data Analysts extract insights from large datasets used by management to achieve business objectives. But what does that look like daily?

  • Schedules and Tasks: To begin any project involves working with your supervisor and the broader team of analysts to set schedules and prioritize tasks. This could include identifying projects to work on, data sets of interest, potential models and processes, etc., and assigning these tasks to individuals and sub-teams.
  • Data Collection: To work with data, it must first be collected. One of the Data Analysts' tasks involves scraping or gathering data from primary and secondary sources. This data might have been collected internally in the organization or might have come from third-party data vendors.
  • Data cleaning: Data collected from primary and secondary sources usually have errors such as double entries, entries that fall outside the range of interest, incomplete information etc. The data analyst's job is to filter and clean up this data. This helps ensure that the following analysis is free from errors that might skew the investigation and impact the business decision based on that data.
  • Data Analysis and Interpretation: Once the data has been collected, sorted, filtered and cleaned, it is now the work of the data analyst to identify, study and translate trends from the data into a format that is understandable to perhaps less-savvy key decision makers.
  • Data Visualization and Presentation: Once the data analyst has come to verifiable conclusions about the patterns and trends in the data, it is part of their duty to present this information in a format and using language that is more accessible to stakeholders.
  • Report Production: Often, in addition to presenting data findings, the data analyst will also have to produce reports and brochures detailing the finer details and conclusions of their data investigations. These reports might be stand-alone or included in broader organizational releases and communications.
  • Build and Maintain Dashboards: A Data Analyst works alongside other analysts within the broad organizational environment. The data analyst can only attend to some data requests from the general staff. So they have to build and maintain dashboards within the organization that are easily understandable and accessible to other analysts as well as other members of the organization.
  • Build and Maintain Records: the data analyst must keep a record of all their data investigations which could involve documentation about the methods, models and visualizations that are easily accessible to all stakeholders even in their absence or departure from the organization.

Read our feature: Data Analyst career path.


Now that we've explored who a data analyst is and their job functions and responsibilities, let's look at how you can become a member of a class of in-demand tech professionals.

There are four main steps to becoming a Data Analyst.

  1. Get a Data Analytics Education
  2. Acquire core technical skills
  3. Build a portfolio of work
  4. Get an entry-level Data Analyst job

Get A Data Analytics Education

The journey to a new industry or field begins with acquiring the education and skills required for the role. As a data analyst, this will include mastering the skills of data acquisition, cleaning, interpretation and visualization. You will need data management skills and a mastery of programming languages such as SQL, Python, R, and Javascript to manipulate and interpret data. Soft skills will also greatly enhance your success as a data analyst, which we will cover in a separate section below.

There are three main paths to acquiring this education:

  • Self-Study: The days are behind us when looking for new information meant having to go to a library. The internet has changed how knowledge is acquired, as it has improved access to the many free resources and tools online. Suppose you have the time, discipline and commitment to follow strict self-learning schedules. In that case, you could easily find many online resources, including websites, videos and documents that would help you acquire the technical skills required to launch a career as a Data Analyst. The challenge with this approach will be the volume of resources available, which might feel a bit overwhelming. There will be many opinions and approaches to data analysis. It would be up to you to structure your curriculum and ensure you are acquiring up-to-date and industry-relevant knowledge and skills. Another downside would be that you will miss out on developing soft skills crucial to your success as a data analyst. Skills like collaboration, teamwork, brainstorming, and presentation, which are easy to acquire in a structured learning environment, will not be available in the self-study route. Also, unless you use this option to advance along a similar Data-centric career path, you may have to consider getting additional industry-relevant certification. While employers no longer have a strict college degree rule, you do have to show some formal accreditation. Pros: Cheapest option. Cons: It can be overwhelming. No accredited degree, diploma or certificate. Have to pass certifications on your own and no soft skills training either.

  • College Degree: With the increased demand for data to achieve business objectives, data analysis is becoming a popular field for those looking to get into the tech space. Universities and Colleges now have degrees structured specifically around Data Analysis/Science. However, you can specialize in something other than Data Analysis. Most employers will take a bachelor's degree in a related field, like statistics and computer science. Data manipulation is one of the core functions of a Data Analyst. A computer science degree that teaches you Python and Databases such as SQL and Advanced Excel will provide a solid foundation for launching a career as a Data Analyst. This route requires significant time and financial commitment. It is a good option for young people who plan on going to college anyway but less so for mid-career professionals looking to shift to Data Analysis. Pros: Broad curriculum will provide a strong foundation for any tech career. Cons: Expensive. Too time-consuming. Depending on your degree, you might need to acquire certifications or attend a short-duration course like a Bootcamp to get that specialization.

  • Bootcamp: Let's say you are a mid-career professional or have time and personal commitments that would get in the way of taking an extended program like computer science or a Data Analysis degree. In that case, attending a Bootcamp like the 8-week Data Analytics Bootcamp at Lighthouse Labs is the best and quickest option to jumpstart your career in the field. A Data Bootcamp at Lighthouse Labs will take you from novice to job and career ready in as little as 8 weeks. The comprehensive curriculum and quick pace of learning might be challenging for some. Still, this option is the most time-efficient, with a specialized curriculum and a diploma/certificate at the end of your study. Pros: Quickest option, specialized diploma, industry-aligned curriculum, network and community building, soft-skills enhancement. Cons: Intense pace, more expensive than self-study.

Acquire Core Technical Skills

Once you've acquired the foundational skills to begin your career, it is time to establish your credentials as a Data Analyst. This will require knowledge and mastery of industry-relevant tools and software. You will need to become familiar with programs like Microsoft Excel, Matlab, and IBM SPSS, used almost daily by data analysts to analyze and gain insights from large data sets.

You should also be competent in using data visualization tools such as Tableau, QlikView, and Power BI. This will help you translate those patterns and trends into user-friendly presentations.

You will also have to acquire the soft skills necessary for a data analyst to perform their duties. These include critical thinking, analytical abilities, collaboration, and presentation skills. These skills form part of your training in our Data Analysis programs.

Build A Portfolio Of Projects

Data Analysis is such a hands-on job that you have to be able to demonstrate your practical knowledge to employers. Having a theoretical understanding of data analysis is brilliant, but many employers also want to know that you have some practical experience. You don't need to have done any complex projects to build a portfolio of work; you can take on personal projects by scouting the internet and then documenting your solutions in a way that is easily presentable to potential employers. Demonstrating this hands-on ability will go a long way in impressing any interviewers and help you land your first job in the exciting data field.

Get An Entry-Level Data Analyst Job

So you’ve acquired a data analytics education, learned how to use industry-relevant software, improved your soft skills and built a portfolio of projects. The next step to begin your career is landing your first entry-level data analysis job. Getting a job can often be a relatively long and painstaking process. You’ll have to research the jobs and companies and create CVs and cover letters highlighting your skills, strengths and technical abilities. You’ll also have to hone your interviewing skills to maximize your chances of landing a role with each interview. While this might sound daunting, it can be an enjoyable process if you approach it with the mindset that each test and interview you get is a learning opportunity to improve yourself and put your best foot forward the next time.

With the Data Analysis Bootcamp at Lighthouse Labs, you automatically get access to Career Services. The Career Services team at Lighthouse Labs is a group of career experts dedicated to helping you land your first job post-Bootcamp. In addition, they will remain available to you throughout your career, providing any additional support you might require as you progress along your journey as a Data Analyst.

Read our feature: Data Analyst Interview: Top Questions and learn more about Career Services for Life here.

Frequently Asked Questions

  1. Is it hard to become a Data Analyst? Learning a new skill will always come with challenges, primarily due to unfamiliarity with the subject material. However, once you get started, those challenges will begin to seem trivial. As you progress along the learning journey, you’ll start to surprise yourself at all the fantastic things you can accomplish.
  2. What qualifications do I need to become a Data Analyst? As outlined above, you will need a degree or qualification to demonstrate your education in the core concepts of data analysis. This could be in the form of a college degree or a certificate like you’d get from the Lighthouse Labs Data Bootcamp.
  3. How do I start a career in Data Analysis? The first step to beginning a Data Analysis career is getting a relevant technical education. There are three main ways you can achieve this. Either through self-study, getting a college degree, or the quickest and the more specialized option would be a Data Analysis Bootcamp. Most employers require a relevant college degree or a Bootcamp Diploma demonstrating your understanding of foundational data analysis concepts.

Ready to jump in? Join the Data Analytics Program to launch your career into the stratosphere.