The field of data analytics is poised for significant growth in 2024. Whether you’re new to the field or you’re more experienced, the industry is evolving and 2024 is full of potential for the field of data analytics.
So you’re wondering how to become a data scientist? Ironhack has designed this guide with the intention of breaking down a data analyst’s role, the skills needed to be successful in this industry, and the concrete steps you can take as we launch into 2024. Let’s get started.
What is a Data Analyst?
Data analysts utilize a variety of statistical and analytical techniques, as well as data visualization tools, in order to identify trends, patterns, and correlations in data. This information gives companies the competitive advantage in understanding everything from consumer behavior and market trends to business performance.
The role of data analysts is only becoming more crucial today across industries as businesses become more reliant upon data to improve their decision making processes. However, pursuing a role as a data scientist comes with its own set of pros and cons:
Pro–high demand: the field of data analytics is growing rapidly, meaning that data analysts are in high demand across a variety of industries. More and more businesses are looking to data in order to make informed decisions. This means that salaries are competitive, especially for data scientists with relevant experience and expertise in specialized areas.
Pro–diverse opportunities: given that businesses across industries are looking to utilize data in order to optimize their processes and improve their decision making strategies, there are data analyst jobs in a wide range of fields. From finance to healthcare, to marketing and technology, you’ll be sure to find a data scientist job in an area that interests you.
Pro–intellectually stimulating: data analytics jobs require statistical analysis, data modeling, and other more intellectually demanding tasks. That said, data analysts can be sure that they’ll be pushed intellectually and satisfied if work that requires creative problem solving and analysis is something they're seeking.
Con–technical skill requirements: data analysts must be proficient in various tools and programming languages, which can be challenging for individuals without a technical background.
Con–long hours: big data analysis projects can be time-consuming, requiring long hours to clean, process, and analyze large datasets, especially when working on tight deadlines. Some tasks like data cleaning and preparation can become repetitive, making this work potentially monotonous for some.
Con–significant pressure: the findings of data scientists directly relate to the strategic decisions that businesses then make, that said, the pressure to deliver accurate and reliable insights is high. Faulty analyses can have significant consequences for businesses or clients, making the work of data analysts potentially high-stress.
In all, tech professionals who enjoy working with data, who face problems head on, and who are analytically inclined may find this profession highly rewarding. Of course, like all professions, the work can become stressful, but with proper stress management, data analysts can flourish.
What are the responsibilities of data analysts?
Data analysts are pivotal in helping organizations make data-driven decisions. This is done through:
Data collection: the first step for data analysis is collecting data from various sources, including databases, spreadsheets, and surveys. They ensure that the data collected is relevant to the analysis being conducted.
Data cleaning and preprocessing: the following step is cleaning data, given that raw data all too often contains errors, missing values, or inconsistencies. Analysts clean and preprocess the data, which involves handling outliers, filling in missing data, and transforming data into a suitable format for analysis.
Data analysis: data scientists use statistical methods and data analysis tools to explore and analyze data sets. They identify patterns, trends, correlations, and anomalies within the data to extract meaningful insights.
Data modeling: analysts create mathematical models and algorithms to better understand complex data relationships. They may use a variety of techniques including regression analysis, clustering, or machine learning algorithms for predictive analysis.
Data visualization: analysts present their findings through visualizations like charts, graphs, and dashboards. Visualization helps convey complex findings in a clear and understandable manner such that stakeholders with non-technical knowledge can engage data and make informed decisions.
Ethical considerations: data scientists must handle data responsibly, ensuring compliance with data protection and regulations and ethical guidelines. They must maintain data privacy and confidentiality while conducting analyses.
Through performing these responsibilities, data analysts provide immensely valuable insights to their organizations. Those insights lead to strategic and operational decisions within organizations.
How to Become a Data Analyst
So you’re thinking about becoming a data analyst? Well, you’re in great hands. This guide will help you get clear on the steps to learn data analytics such that you can pursue a career in data analytics come 2024. Becoming a data analyst typically includes a combination of education, skill development, and practical experience. Let’s start with what qualities you need to be a successful data analyst.
Qualities of a data analysts
Effective data analysts possess a combination of technical expertise, analytical skills, and personal qualities that enable them to excel in their roles. Successful data analysts usually hold the following soft skills:
Attention to detail: being detail oriented is an absolute requirement given that a data analyst’s findings are only as strong as the integrity of their data. Meticulous attention to detail is critical in ensuring the accuracy and reliability of data and findings.
Critical thinking: a data analyst’s ability to think critically, analyze information, and solve problems using data-driven approaches is crucial to their success.
Time management: effective time management skills are crucial given that data analysts must prioritize tasks, meet tight deadlines, and manage multiple projects simultaneously.
And on the other hand, data scientists should balance those soft skills with the following hard skills:
Statistical analysis: data analysts must possess a solid understanding of statistical methods, hypothesis testing, and repression analysis to analyze data and draw meaningful conclusions.
Data manipulation: being proficient in data tools like SQL for querying databases and data manipulation libraries like Pandas (Python) and dplyr (R) is critical for data analysts to be successful in processing and reaching conclusions based on data sets.
Data visualization: data scientists must possess data visualization skills in order to create clear and compelling visualizations that can be presented to non-technical stakeholder. Knowing tools like Matplotlib, Seaborn, ggplot2, and Tableau is crucial for data analysts to visualize complex data sets.
In all, if you want to be a data analyst, it's crucial to possess a balance of hard and soft skills. Data analysts must be able to handle the complexity of the data analysis process and communicate their findings to non-technical stakeholders.
Becoming a data analyst
Knowing that the field of data analytics only continues to expand, 2024 may be your year to jumpstart your career as a data analyst. But how long does it take to become a data analyst? That all depends on your education, your experiences, and your willingness to apply for data science positions. First off, education is important. The majority of data scientists enter the field possessing:
Bachelor’s degree: many data analysts have a bachelor’s degree in fields such as mathematics, statistics, computer science, information technology, or engineering. A strong foundation in mathematics and statistics is essential.
Online courses and certifications: if you didn’t study a related field in university, don’t worry: it’s easy to acquire strong data analytics skills through a variety of online courses and certifications.
Apply for entry level data analyst jobs
Are you wondering how to become a data analyst with little to no experience? We have answers for that too. There are many entry level data analyst jobs for which you undoubtedly qualify. That said, Ironhack has a couple recommendations for you as you pursue data scientist jobs, especially at entry level:
Design a compelling resume: craft a resume that communicates your technical proficiency, analytical skills, relevant certifications, and any practical experience you have gained. Emphasize your transferable skills, and show off projects, internships, or volunteer work that demonstrate your data analysis capabilities.
Cast a wide net: apply for entry-level data analyst positions even if you don’t meet all of the listed requirements. The tech industry is transforming so quickly that employers often value soft skills like a willingness to learn, adaptability, and flexibility more than specific technical skills. If you’re a good fit for the company, an employer will invest in your training. That said, don’t be intimidated by what’s written in the job listing.
Show up prepared: be well-prepared to discuss your technical skills, experiences, and problem-solving skills during the interview. Ask individuals in your professional network about what you should expect from the interview process. And be ready to communicate your knowledge and passion for the field.
By following these steps, you will be in a great position to secure an entry-level data analyst position. If you want to gain more knowledge or experience, there are many online courses and certificate programs that can equip you with the necessary skills to be successful in an entry level position. Our number one recommendation for folks interested in entering the field is bootcamps.
Ironhack offers both online and in-person bootcamps in data analysis. Our bootcamps are specially designed by professionals in the field in order to equip our students specifically with the skills and knowledge that are in high demand. We invest in your success through our career coaching programs and we count on a strong alumni network to support our students entering the field. That said, what are you waiting for?
