How to Become a Data Analyst: A Guide for Beginners

The advent of information technology and the internet has shed a new spotlight on data analysts. With the abundance of information and the need to collect and analyse it, data analysts’ roles in the workplace have evolved. To become a data analyst, you need to acquire a specific skillset including an educational background. Learn how to become a data analyst and start your career. 

Follow these steps if you want to become a data analyst: 

• Earn a Relevant Degree
• Acquire and Develop Technical Skills
• Practice Data Analyst Skills You Have Acquired
• Build Your Data Analyst Portfolio
• Apply for Entry-Level Jobs

What is a Data Analyst and What Do They Do?

A data analyst, in its simplest definition, is a person who collects, organises, processes, and analyses large-sized data sets. The ultimate goal is to discover relevant information that will help lead to a conclusion and that the decision-making process will be based on.  

A data analyst are important in the workplace because they work with data that will help make informed decisions. They single-handedly organise and make sense of these data. They then create a comprehensive report and present their findings in a way that’s easily understood. 

Here’s a comprehensive list of the roles of a data analyst in the workplace: 

• Data Collection - collects data and information from databases, surveys, and other sources; they then combine these data to create a more comprehensive dataset. 
• Data Cleaning and Preparation - determines errors, irregularities, and missing values in the dataset; they then correct these errors to provide a more accurate and reliable report
• Data Analysis - performs Exploratory Data Analysis (EDA) to pinpoint patterns, trends, or correlations within the dataset; they can also conduct statistical tests to validate their initial hypotheses. These data analyst skills are important if they want to flourish in this field. 
• Data Visualisation - creates charts, graphs, and other visual aids to present the findings; uses tools such as Power BI, Tableau, and Python libraries like Matplotlib and Seaborn. Transforming data into visual aids helps make it understandable. 
• Data Reporting - reports the compiled analyses for the company stakeholders; this is crucial as communicating the data and analyses can make or break how they are perceived by the people involved in making business decisions. 
• Data Lineage – tracks and visualizes the flow of data from its origin to its final destination. This helps data analysts understand how data moves through various systems and transformations, ensuring data accuracy and consistency throughout its lifecycle.)

In the next sections, we discuss thoroughly the specific steps on why and how to become a data analyst in 2024. 

Why Become a Data Analyst in 2024?

Data analysts have become increasingly in demand due to the growth of data across industries. Businesses nowadays generate huge amounts of data daily, which also increases the need for professionals who can collect, organise, and read this information to make important business decisions. 

The demand for data professionals is projected to grow by 28% by 2025, according to an IBM report. Big companies such as Amazon use data analysis to maximise their supply chain, predict market trends, and improve overall customer experience. This shows how in-demand data analysts have become today.

Data analysts help improve patient outcomes by examining treatment data and operational efficiency in healthcare. In finance, analysts help assess market risks to identify investment opportunities. Moreover, the skills acquired by data analysts are so versatile that they allow cross-industry employment, which means job security and competitive salaries are to be expected. 

According to the U.S. Bureau of Labor Statistics, the median annual wage for data analysts was $94,280 in May 2020. This trend will not likely change in the future as more organisations recognise the importance of data and how it can greatly contribute to their success. 

How to Become a Data Analyst: 5 Steps to Become a Data Analyst

Do you want to become a data analyst but do not have an idea how to become one without experience? It takes a couple of things mixed - education, skills development, experience, and continued drive for improvement - to name a few. That said, AdmissionSight - a leading college admissions counselling company with the highest rates of acceptance into the Ivy League in the industry - prepared 5 steps and tips on how to become a data analyst with no experience: 

Step 1: Earn a Relevant Degree

You will need a degree relevant to data analysis if you want to become one. This is the first step in how to be a data analyst. A bachelor in the fields like data science, statistics, computer science, mathematics, business administration, and economics is highly recommended. These programs will introduce you to the knowledge and tech skills you need for collecting and interpreting data. 

A degree isn’t a requirement to become a data analyst but it will surely help you be placed easily as you already have the educational background. If a degree isn’t feasible at the moment, you may want to consider online courses and boot camps. 

Tip: Data analysis is a big umbrella that covers many aspects. Begin with basic data analysis skills. You can start with the fundamentals of statistics. This will introduce you to topics such as probability distributions, measuring centre and spread, and hypothesis testing. Additionally, practising data analysis in spreadsheets will help you get a feel for collecting and wrangling data. You can also learn how exploratory data analysis works in tools like Power BI and others.

Step 2: Acquire and Develop Technical Skills

This part is the second step on how to become a data analyst. This is where you learn program languages commonly used in the field. This included Python, R, and SQL. You should also be well versed with data manipulation tools like Excel, the Dplyr package in R, and the Pandas library in Python. 

Most of these languages are taught or introduced in school if you go to a data analyst program, but this is where you have to master or be excellent at using such tools. It is equally important to be an expert in data visualisation tools such as Tableau, Power BI, and Python libraries like Matplotlib and Seaborn.

By this time, you should be able to import, clean, manipulate, and visualise data using one or two programming languages. 

Tip: Choose a programming language to master! There is no right or wrong decision here. A data analyst typically works with more than one or several programming languages in their lifetime. However, you need to master or be excellent at one language to establish expertise while not disregarding the other programming language. 

Step 3: Practice Data Analyst Skills You Have Acquired

Working with mock data analysis projects helps here, but working with real-world problems is the way to go. Even without professional experience, employers find applicants with experience solving real-world projects and problems more hirable. Stay engaged by accessing real, clean datasets with preselected data and that is how to become a data analyst. 

To prepare for real-world work experience, you will need to advance your studies and showcase your independent research and project management abilities to potential employers. This involves selecting topics, gathering data, designing project structures, making hypotheses, communicating findings, and planning future steps. Although individual projects are more time-consuming, they will surely enhance your employability. 

Tip: Use a free, clean data set for your chosen data analysis projects. Platforms such as DataLab, Kaggle, and UCI Machine Learning Repository are good data set resources. 

Step 4: Build Your Data Analyst Portfolio

By now, you're well on your way to becoming a data analyst. But you will need a compelling portfolio of your best work to attract employers. 

Start with guided capstone projects from online boot camps or university courses that showcase your mastery of different tools and techniques. Show both your technical and soft skills, and design your portfolio to appeal to your audience. Avoid common, cookie-cutter projects. Instead, focus on what truly interests you. As you work on developing your portfolio, replace broad-scope projects with more unique and personalised work.

Tips: Consider including the following in your portfolio: (1) data cleaning projects; (2) machine learning projects or statistical analysis; (3) Exploratory Data Analysis (EDA) projects; (4) business analysis projects; and (4) soft skills that include the reports or presentations you’ve made in the past.

Step 5: Apply for Entry-Level Jobs

You should be ready to apply for jobs now that your portfolio is all set. Since you are new to this industry, it is always best to start with an entry-level job post. 

Before your job hunt, review your data analyst skills and compare them with the current market requirements. Examine job descriptions for data analyst posts and identify the skills that are most in demand. This will help you identify any gaps in your knowledge and skills. Act on this discovery and work on these gaps. 

Moreover, create a striking resume. This is what you will need if you want to learn how to become a data analyst with no experience. It should be a one-page document that is customised to each job post you apply for. Review the job description, identify the skills they require and highlight those skills in your resume. 

Lastly, do not forget to establish your online profiles. The majority of employers now use online platforms to hire data analysts. Make sure you catch these job posts by building your online profiles on platforms like LinkedIn, GitHub, and Kaggle. 

Tip: Learn how to network; you’d need that if you want to learn how to become a data analyst. Since you are trying to penetrate a new work sphere, you need to grow your professional network. Creating a compelling online profile is tried and tested effectively. You may also want to join online data communities or groups or even join conferences and seminars. 

Data analytics is evolving and will continue to do so. New applications will be discovered, which will, in turn, expand job roles. To increase your chances of getting and staying employed, stay current with the latest tools and technologies. Strive to continuously develop your skills through practice projects and real-world experience. This will help you build a strong portfolio and establish yourself as a highly skilled and in-demand data professional.