How to become a data analyst?

How to become a data analyst?

Today, data plays a prominent role in numerous organizations and businesses. Consequently, the development of data-driven strategies has gained significant importance. The result of data-driven strategies varies depending on experts, such as data analysts. Data analysts are responsible for various data analysis tasks, making their roles in this field multifaceted. To understand who a data analyst is, gaining insight into data analysis and the associated responsibilities is essential. Thus, in this article, we will delve into the world of data analysis and explore the roles and responsibilities of each person in this field and the career path of this profession.

Who is a data analysis expert?

Are you detail-oriented and organized? Do you enjoy working in orderly and structured environments? Do you have a passion for working with numbers and data? If your answer to all these questions is yes, then the data analyst role may be the perfect fit for you.

Data analysts are responsible for analyzing various types of data, which can encompass market research, sales figures, logistics, or shipping costs – data essential for every business’s continued operation. Data analysts examine numbers and data to aid organizations and companies in making informed and improved business decisions. A data analyst’s responsibilities often include:

  • Collecting different data using various methods and organizing them.
  • Ensuring the accuracy and validity of the data.
  • Analyzing data using software or various analytical tools.
  • Collaborating with other data analysts and experts in the relevant field.
  • Communicating the findings or analysis results to relevant authorities, managers, stakeholders, and customers.
  • Monitoring the results and the effectiveness of the findings.

In addition to the above, data analysts also have the following duties:

  • Collaborating with the IT team, management teams, and data scientists to establish goals throughout the organization.
  • Extracting data from both primary and secondary sources.
  • Processing data to eliminate irrelevant information.
  • Identifying trends and patterns in data sets.
  • Recognizing new opportunities for process improvement.
  • Designing, creating, and maintaining databases and data systems.
  • Fixing code and data issues.

The difference between a data analyst and a data science expert

How to become a data analyst?

An important thing to know first is the difference between a data analyst and a data scientist. While the boundary between Data Science and Data Analytics can appear somewhat vague, generally, a “data scientist” primarily focuses on machine learning and predictive modeling. They use data to predict the future and usually have a strong mathematics, statistics, and computer programming background. But which one is right for you? Data Science or Data Analytics?

Data science professionals usually deal with artificial intelligence and machine learning. Machine learning is a larger, automated version of what a data analyst does. Within machine learning, unique algorithms sift through extensive datasets, enabling you to identify elements, particularly in images, comprehend natural human language, or make data-driven advertising decisions. As a data scientist, you must use the Python and SQL programming languages to access and use your desired data. Learning Python for data science is one of the most critical tasks in this field. To learn more about Python and its use, refer to the article “What is Python?”.

How to start a data analyst career?

Starting a career in data analysis, especially without prior experience, can be achieved by following specific steps. If you’re seeking answers to the question, ” How to become a data analyst with no experience?” then continue reading this article. Employers usually require data analysts to possess a bachelor’s degree, professional certifications, relevant work experience, and essential technical skills. In the following sections, we will provide a comprehensive guide to the career path in data analysis.

1. Get a degree

Data analysts typically require a minimum of a bachelor’s degree to pursue a career in this field. Some individuals opt to further their education with a master’s degree, which can lead to higher earning potential. Common fields of study that prepare individuals for a data analyst role include:

  • Computer Science: This major can prepare candidates for analytics and equip them with basic computer programming skills. Most universities’ computer science programs include algorithms, operating systems, software engineering, and general computer architecture coursework.
  • Mathematics: A degree in mathematics is highly suitable for data analysts as it teaches problem-solving principles, mathematical modeling, and data analysis fundamentals. Many universities offer specialization options in business or engineering within the mathematics major, along with courses covering discrete mathematics, differential equations, and analysis.
  • Statistics: This field helps data analysis enthusiasts build a solid grounding in data analysis, computation, and probability. Students in statistics can strengthen their skills and become data analysis experts, with coursework typically covering statistical learning, data technology, and numerical analysis.

However, one of the advantages of pursuing a career in data analysis is that it can be learned outside of traditional academia. To do so, you can enroll in the best course to learn data analysis, providing you with the foundational knowledge needed for this profession.

2. Learn important programming languages

To excel in data analysis, it’s essential to master key programming languages such as JavaScript and Python. You can acquire these skills during your undergraduate studies by enrolling in online courses and engaging in self-directed learning.

3. Gain relevant work experience

Seek opportunities for hands-on experience in data analysis. Consider pursuing internships that provide valuable exposure and allow you to build a professional network with experts in the field. Additionally, data analytics freelancing can help you gain experience in this field, enabling you to advance your career through freelance projects.

4. Get training on the job

Upon starting a new position, data analysts typically undergo on-the-job training programs. Moreover, many individuals without experience in the data industry often leverage internships during their college years, making on-the-job training more readily accessible.

5. Obtain Professional Certification

Certification can significantly enhance your chances of success and employability in data analysis. Earning a professional certificate shows your proficiency in essential software tools and underscores your commitment to this profession.

6. Apply for data analytics job opportunities

Following your internship and college education, it’s time to start looking for a job with your resume and internship experience. Most companies and employers in the data analysis field expect candidates to possess at least one project-based experience.

The demand for skilled data analysis experts is substantial, presenting numerous opportunities for individuals to approach their job search with strategic thinking and foresight.

What do you need to be a data analyst?

How to become a data analyst?

Acquiring specific skills is invaluable and well worth the effort. Fortunately, the internet is an excellent resource for acquiring new skills, and you can earn your degree from the comfort of your home. Some skills that every data analyst should have are:


Although it may seem unusual, many data analysts dedicate significant time to creating tables and performing intricate calculations in Excel. Proficiency in Excel is a crucial skill when applying for job interviews or internships in data analysis.


SQL, which stands for Structured Query Language, is designed for storing and retrieving information from databases. For instance, if you need to retrieve specific user information from a website, you’ll likely use SQL to access a database stored on the server. While SQL may appear daunting initially, it has a relatively short and manageable learning curve that will quickly familiarize you with its power and utility.

Google traffic analysis (Google Analytics)

Google Analytics is a web service offered by Google, designed to help you analyze the performance of a website or application. It provides insights into metrics such as the number of visitors, their traffic sources, the pages they visited, and more. This tool enables you to track the pages visitors have viewed and assess their purchasing behavior.”


Learning programming fundamentals, particularly in a language like Python, is essential to advance your skills as a data analyst or data scientist and reach a higher proficiency level. Proficiency in programming helps you efficiently extract data from various sources, harness data effectively, and create visually appealing presentations for clients. Python is one of the most popular programming languages among data analysts and data science professionals due to its flexibility and versatility.

Apache Hadoop

Hadoop constitutes a comprehensive suite of open-source software provided by the Apache Foundation. This software suite proves invaluable when handling extensive datasets distributed across multiple servers. Apache Hadoop efficiently manages large volumes of data that require multiple servers. It is particularly well-suited for highly skilled data analysts and data science professionals.

Apache Spark

Apache Spark is a cluster processing framework featuring a robust application programming interface (API) that facilitates the creation of high-performance programs in languages like Java, Python, and others. This advanced tool seamlessly integrates with the Apache Hadoop suite, enhancing its capabilities.

Of course, specific skills may vary based on your particular role as a data analyst, but it is crucial to align your skill set with the job requirements when applying for a data analyst position. Carefully review the job requirements to ensure a good fit.

How Long Does It Take to Become a Data Analyst?

The time required to become a data analyst can vary significantly, ranging from several months to several years. The duration depends on factors such as your existing skill set, the educational path you pursue, and the amount of time you dedicate each week to honing your data analytics skills. According to Coursera, this timeframe can be flexible and tailored to your circumstances.

The labor market and salaries of data analysts in the world

How to become a data analyst?

Data analysts’ job market and income can vary significantly depending on factors such as the country, employment organization, job responsibilities, contract type, and more. For instance, there can be a substantial disparity in income and salary between remote data analysts and those working in traditional in-person roles. The salaries of data analysis professionals are also influenced by their educational qualifications and work experience. Typically, more advanced countries offer more profitable labor market conditions and wages for these positions as they allocate substantial budgets for the growth and expansion of data-driven businesses.

Take Dubai, for example, as one of the United Arab Emirates’ key cities, where data-driven organizations and businesses are experiencing significant growth. In general, the demand for data analyst recruitment in Dubai is rising. Similar trends can be observed in countries such as the United States of America, Germany, England, and others. Forecasts indicate that the demand for data analysts will continue to expand, leading to a growth in job opportunities in the coming years.

According to the  U.S. Bureau of Labor Statistics, the employment of data scientists is projected to increase by 35 percent from 2022 to 2032, which is significantly faster than the average growth rate for all occupations.

Is being a data analyst right for you?

Data analysis is an excellent choice for individuals seeking a career with promising growth prospects in the future. With the Internet of Things and machine learning set to significantly shape the future job market, opting for a role like that of a data analyst is forward-thinking and highly relevant. Data science is experiencing rapid expansion, and data analysts often have the flexibility to work online from the comfort of their homes. What are your thoughts on data analysis?

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