Feb 20, 2023

How to Become a Data Analyst in 2023?

Today, with the increase in data generation, the term "data analyst" is finding its meaning. However, becoming a data analyst requires a set of data analyst skills and a few steps to follow. In this How to Become a Data Analyst article, you'll gain a deeper understanding of what you need to do to become a data analyst and discover the skills needed for the position data analyst.

Becoming a data analyst requires basic data analysis skills and the ability to derive insights from large data sets. Data analytics is an area of​​ great opportunity as companies across all industries embark on big data initiatives and expand their analytics sector, especially in the fields of telecommunications, insurance, advertising, financial services, healthcare and technology.

This growth is expected to continue into the future as industries that have lagged behind the adoption of big data analytics, such as education, government and manufacturing, have pledged to ramp up their big data analytics activities to strengthen their future.

Before we discuss in detail How to become a Data Analyst, let us check out what is data analytics? Who is a data analyst? And what does a data analyst do, etc?

What is Data Analytics?

Data analytics is the process of inspecting raw data to extract valuable insights that can be used to improve businesses. Think of it like putting together a jigsaw puzzle - collecting the pieces and fitting them together to create a full picture. Similarly, data analytics involves gathering data from various sources, cleaning it, and transforming it into understandable information.

The data can be structured, semi-structured, or unstructured, and the results of the analysis can be represented visually through graphs and charts. To do this, data analysts use tools and frameworks. The demand for professionals who can help companies turn data into insights is high, making data analytics a promising career path.

To become a data analyst, one can start by understanding the necessary skills and education, as well as gaining experience through internships or personal projects.

Who is Data Analytics?

A data analyst is a professional who works with data to identify patterns, trends, and insights that can help businesses make informed decisions. They are responsible for collecting, organizing, and analyzing large sets of data from various sources and presenting their findings in a way that is easy to understand.

Data analysts use statistical analysis, data mining, and machine learning techniques to extract insights from data, and they also create reports, dashboards, and visualizations to communicate their findings to other members of the organization. Data analysts work in a variety of industries, including finance, healthcare, marketing, and IT, and they are in high demand due to the increasing amount of data that companies collect.

You may also be interested in exploring: How to Become a Data Scientist in 2023?

What Does a Data Analyst Do?

Data analysts are responsible for collecting, analyzing, and interpreting data to provide insights and recommendations to the business. Their primary role is to extract valuable insights from large data sets and present their findings in a way that is easy to understand.

Data analysts can work with data from a variety of sources, including customer behavior, market trends, and financial reports to identify patterns, trends, and relationships. They also use statistical analysis, data mining, and machine learning techniques to extract information and make predictions. Data analysts are also responsible for creating reports, dashboards, and visualizations to communicate their findings to others in the organization. They collaborate with other departments to identify business problems and provide data-driven solutions.

Data analysts also work on projects to develop and implement strategies to improve business operations and increase revenue. In addition to technical skills, data analysts must have strong communication and problem-solving skills. They must be able to communicate complex data and ideas effectively to non-technical audiences and work with team members from different departments.

How to Become a Data Analyst: A step-by-step guide

A good data analyst must have certain technical skills to make every day interventions easier, dealing with various situations that may arise both in the process of analyzing information and in the organizational relationships . So, if you are thinking of starting a career in data analytics, the tips below can help you –

1.  Get a Degree in a relevant field

A degree program can help you develop a strong foundation in the skills needed to become a data analyst. There are also specialized programs such as a Master’s degree in Data Analytics, which can provide advanced training in data analysis. However, to work as a data analyst, you must have an undergraduate or a postgraduate degree in a relevant discipline, such as:

  • Computer science
  • Economics
  • Information management
  • Mathematics
  • Statistics
  • Marketing
  • Finance
  • Business information systems

Companies prefer to hire candidates with advanced degrees in data analytics or data science. Several popular graduate degrees are offered in subjects, such as the Master of Data Science, the Master of Business Analytics, the Master of Data Science and Analytics, and the Master of Big Data., among other subjects. Candidates must also have an excellent knowledge of basic math, statistics, and programming, as well as a desire to analyze data.

2.  Build your technical skills.

Getting a job in data analysis typically requires having a set of specific technical skills. Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired.

  • Python or R (especially their specialized libraries for data analysis)
  • SQL
  • The command line
  • Statistics
  • Data cleaning and wrangling
  • Data analysis
  • Data visualization
  • Web scraping
  • Debugging
  • Data storytelling
  • Dealing with unstructured data

In addition to these hard skills, hiring managers also look for workplace skills, like solid communication skills—you may be asked to present your findings to those without as much technical knowledge—problem solving ability, and domain knowledge in the industry you’d like to work.

The best way to find out the specific requirements of a certain employer is to read the corresponding job description. If, at this point, you feel that you lack some crucial skills, consider upskilling.

Finally, don’t forget that there are also some important data analyst soft skills:

  • Analytical thinking
  • Multitasking
  • Curiosity
  • Creativity
  • Communication skills
  • Flexibility
  • Ability to work both independently and in a team
  • Decision making
  • Business domain knowledge

You may also be interested in exploring: Machine Learning - A Complete Guide.

3.  Build a Portfolio

You will have abundant opportunities to put your new skills to work by doing various exercises and completing the data analysis projects suggested by your curriculum. Practicing your skills and solving mock or real-world problems will give you a solid basis for your future work experience.

To start with, you will need to undertake individual projects where everything will be your responsibility: selecting the topic, fetching the necessary data, contemplating the direction of your research, designing the project structure, making and checking hypotheses, effectively communicating your findings, and laying out the way forward.

Once you done with your portfolio, you can keep your portfolio of data analysis projects on DataCamp Workspace, GitHub, or Kaggle free of charge. They are not the only free platforms for hosting such portfolios, but these two are widely popular and are the best choice for an entry-level data specialist as they ensure good visibility for your projects. You might also want to consider creating a personal website.

Dataset for your project : Free Datasets to start building your Portfolio

4.  Gain Work Experience

Gaining work experience is a crucial step towards becoming a successful data analyst. It not only helps you develop practical skills but also enables you to put your theoretical knowledge to use. Work experience helps you gain insights into how data analysis works in real-world scenarios and helps you understand how you can best use your skills to solve problems.

One way to gain work experience is through internships. Internships allow you to work with experienced professionals and learn from them. You can also apply your knowledge and skills in a real-world setting, which can enhance your understanding of how data analysis works. Internships can be an excellent way to develop your skills and gain experience in the field of data analytics.

Another way to gain work experience is by taking entry-level jobs in the field. These jobs may not be glamorous or high-paying, but they can provide you with valuable experience. Entry-level jobs give you exposure to data analytics tools and software, allow you to work on data-related projects, and give you an opportunity to learn from experienced professionals.

5.  Keep Learning and Improving

Data analytics is a dynamic field that is constantly evolving. To stay up-to-date with the latest trends and technologies, it is essential for data analysts to keep learning and improving. This not only helps them stay competitive in the job market but also ensures that they are equipped with the latest tools and techniques needed to provide valuable insights to their organizations.

There are many ways in which data analysts can continue to learn and improve their skills. One way is to attend conferences, seminars, and workshops related to data analytics. These events provide opportunities to learn from experts in the field and network with other data analysts.

Another way to learn is to take online courses or certifications that focus on specific areas of data analytics. These courses are often self-paced and provide a structured way of learning new skills and techniques. They are also cost-effective and can be completed while working full-time.

Reading books, articles, and blogs related to data analytics is also a great way to stay informed about the latest developments in the field. These resources provide insights into real-world applications of data analytics and offer different perspectives on how data can be analyzed and interpreted.

Networking with other data analysts can also be helpful in learning about new techniques and technologies. Joining online communities or attending meetups related to data analytics can provide opportunities to connect with other professionals in the field.

6.  Apply for jobs

After gaining the necessary skills and work experience, the next step in becoming a data analyst is to apply for jobs. When applying for a data analyst position, there are several things to keep in mind to ensure that you stand out from other applicants.

First, make sure to tailor your resume and cover letter to the specific job you are applying for. Highlight your relevant experience, education, and skills that align with the job requirements. Use industry keywords and jargon to show that you have a good understanding of the field.

Second, provide a portfolio of your data analysis projects to showcase your work and abilities. This portfolio should highlight your analytical skills, problem-solving abilities, and the insights you have provided through your data analysis. Make sure that your portfolio is easy to understand and visually appealing.

Third, prepare for the interview by researching the company and the position. Show enthusiasm and demonstrate that you are eager to learn and contribute to the organization. Be prepared to answer questions about your analytical skills, experience with specific data analysis tools and software, and your ability to work with other teams and stakeholders.

Finally, follow up after the interview with a thank-you note or email. This will show your interest in the position and your appreciation for the opportunity to interview.

We at Alphaa AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists, who bring in data first approach to their work, core specialisation, and the organisation.With Saurabh Moody and Preksha Kaparwan you can start your journey as a citizen data scientist.

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