If you’re looking for a job that offers both security and excitement, then you should definitely consider a career in data analytics. According to the Bureau of Labor Statistics, the demand for data professionals is growing at a rate of 35% per year, which is much, much higher than in other fields. It’s common for data analysts to earn salaries that are well over $100,000, and even entry-level data analysts enjoy good benefits and high salaries.
However, if you want to become a data analyst, you'll need to learn data science fundamentals, master data analysis tools and programming languages, build your technical skills and become proficient at analyzing complex data sets. It’s a lot of work, but far from impossible. In this article, we'll show you how to become a data analyst in ten steps.
Is Data Analytics a Good Career?
Becoming a data scientist or data analyst can be an extremely rewarding and lucrative career. Data scientists use their skills to solve real-world problems and make a positive impact on the world. Of course, no job is perfect, and data science is no exception. The work can be demanding, and data scientists often have to work long hours, especially when they are on a deadline. Additionally, data scientists need to be able to keep up with the latest trends and technologies to stay competitive. If that sounds right up your alley, it may be time to consider a career in data analysis.
How To Become a Data Analyst
Now that you know a bit more about data analysis, it's time to dig deeper into the profession. In this next section, we'll show you how to become a data analyst, what it means to analyze data, what kind of data analytics skills you'll need to land a data analyst job at a leading company, and how to build your data analyst portfolio.
Learn the Fundamentals
Data analytics is a complex field. When you become a data analyst, you may be tasked with:
Data collection and preparation involve gathering data from a variety of sources, such as databases, log files, and sensors. Once the data has been collected, it needs to be cleaned and prepared for analysis.
Data exploration, which entails examining the data to identify patterns and trends. Data exploration can be done using a variety of techniques, such as data visualization, statistical analysis, and machine learning.
Data modeling, which is a complex process that involves building mathematical models of the data to represent the relationships between different variables. Data models can be used to make predictions and to develop decision support systems.
Data communication, which entails presenting the findings of the data analysis to stakeholders in a clear and concise way. This may involve writing reports, giving presentations, or creating data visualizations.
Data analysts need a combination of technical skills, solid analytical thinking, and soft skills to succeed. For example, many data analyst roles require data analysts to have the ability to code to carry out data cleaning, build data models, and perform statistical analysis. Popular programming languages for data analysis include Python, R, and SQL.
Data analysts need to be able to create data visualizations to communicate their findings to stakeholders. Popular data visualization tools include Tableau, Power BI, and Matplotlib. Data analysts also need to be able to work with databases to store and retrieve data. Popular database management systems include MySQL, PostgreSQL, and Oracle Database. You'll need a solid foundation in statistical and computational techniques, machine learning, and experience solving complex problems using data.
Most entry-level data analyst jobs require a bachelor's degree in a related field, such as computer science, statistics, mathematics, or information systems. Popular degrees that you can pursue may include a bachelor’s degree in computer science, mathematics, information systems, data science, or analytics.
Degrees can increase your earning potential and make your resume more attractive to recruiters, but there is a downside, too. First off, they’re expensive! You should expect to spend at least $80,000 across four years, not including the cost of your accommodation, travel, books, and other expenses college students have come to expect. Second, they take time! It will take at least four years to earn your degree and become a data analyst, and you'll spend the early years paying off student loans.
While a degree is a good way to enter the field, it's not the only way to become a data analyst. There are numerous free and paid courses in data analysis, statistics, machine learning, and programming languages available online.
You can, for example, enroll in a data science bootcamp from the University of South Florida. You'll learn the data science fundamentals you need to kickstart your career in months, not years, and you'll have the option to study part-time at your own pace. This means you can work while you study! It's also far cheaper than pursuing a college degree.
If that doesn't appeal, start by learning the essential programming languages. Python, R, and SQL are the most popular programming languages for data analysis. If you already work in tech and have the right technical skills, upskill by mastering data visualization tools like Tableau, Power BI, and Matplotlib and database management systems like MySQL, PostgreSQL, and Oracle Database.
Remember, coding skills alone aren't enough. Data analysts need to have a strong foundation in statistics in order to understand and apply statistical methods to data analysis. This includes knowledge of probability distributions, hypothesis testing, and regression analysis.
Learn a Programming Language
Before you can start analyzing data, you have to speak its language. That means learning the programming languages that data analysts use day-to-day.
Python is a general-purpose programming language that is widely used in data analysis. It is known for its simplicity and readability, making it a good choice for beginners. Python also has a large and active community of developers, which means that there are many libraries and resources available for data analysis.
R is a programming language and software environment for statistical computing and graphics. It is widely used in data analysis by statisticians and data scientists. R is known for its powerful statistical capabilities and its wide range of graphical tools. SQL is a programming language designed for managing data in relational databases and is the most popular language for querying and manipulating data in databases.
Learn the Data Analytics Tools
Data analysts use software applications that help data analysts collect, clean, prepare, analyze, and visualize data. These tools can be used to perform a wide range of tasks, like data cleaning, data extraction, data preparation, data analysis, and data visualization. No-code/low-code tools are designed to make data analysis accessible to users with limited or no programming skills. These tools provide a graphical user interface and pre-built templates and workflows to simplify the data analysis process.
Some of the tools you'll use as a data scientist include:
Tableau, a visual analytics platform that helps you see and understand your data
Power BI, a business intelligence and data visualization tool that helps you analyze data and create reports
Google Data Studio, a data visualization tool that helps you create and share interactive reports
Qlik Sense, a business intelligence and data visualization platform that helps you analyze data and create insights
AWS SageMaker, a cloud-based machine learning platform that provides a variety of tools and services for data analysts to build, train, and deploy machine learning models
Azure Machine Learning, a cloud-based machine learning platform that provides a variety of tools and services for data analysts to build, train, and deploy machine learning models
Scrapy, Beautiful Soup, and SeleniumWeb, which are scraping tools that data analysts use to extract data from websites
The tools you'll use will depend on the nature of your work and your field of specialty.
Practice Your Skill Set
Once you've acquired the data skills you need and are confident in your knowledge of data analytics fundamentals, you need to start practicing. Here are a few ideas to get you started:
Work on personal projects. This is a great way to gain experience with different tools and techniques and to build a portfolio of your work. You can find data sets to work with on websites like Kaggle and UCI Machine Learning Repository.
Contribute to open-source projects. This is another great way to gain experience and build your portfolio. There are many open-source data analytics projects that you can contribute to, such as TensorFlow, scikit-learn, and Pandas.
Participate in hackathons and competitions. Hackathons and competitions are a great way to challenge yourself and learn from others. There are many data analytics hackathons and competitions held throughout the year, both online and in person.
Read books and articles about data analytics. There are many books and articles available on the topic of data analytics. Reading these resources can help you learn about new techniques and tools and stay up-to-date on the latest trends in the field.
It's a great way to find out where your strengths and interests lie and can help you with our next point, building up a portfolio.
Build Your Portfolio
Building a portfolio of volunteer, open-source, or freelance data analytics projects is a great way to demonstrate your skills and experience to potential employers. It is also a great way to learn new things and gain experience working on real-world projects. Choose projects that are relevant to your interests and career goals. This will help you to stay motivated and develop your skills in the areas that you are most passionate about. Make sure to document your work carefully. This will help you to explain your projects to potential employers and to learn from your mistakes. You should also take the time to publish your work on a public platform, such as GitHub or Kaggle. This will make it easier for potential employers to find your work and to learn about your skills and experience.
Here are a few ideas for projects you can use to build your portfolio:
Build a machine learning model to predict customer churn. This is a common problem that businesses face, and it is a good project for beginners. You can find data sets on customer churn on websites like Kaggle.
Analyze social media data to identify trends and patterns. This can be used for a variety of purposes, such as marketing, customer service, and product development. You can use tools like Twitter Analytics and Facebook Insights to collect and analyze social media data.
Segment customers based on their behavior. This can be used for targeted marketing and product development. You can use tools like Google Analytics and Amplitude to collect and analyze customer behavior data.
Optimize a website for search engines. This can help to increase traffic to the website and generate more leads and sales. You can use tools like Google Search Console and SEMrush to analyze website traffic and identify opportunities for improvement.
You won't be compensated for your work in the short term, but it will pay off in the long run when you're applying for different data analyst positions.
Build Your Network
Networking is an important part of any career, but it is especially important in data analytics, where competition can be fierce. Build your skills by attending industry events, as these are a great way to meet other data analysts and learn about new opportunities. Be sure to bring business cards and be prepared to introduce yourself and your work to others.
There are many online communities dedicated to data analytics, such as forums, discussion boards, and social media groups. Joining online communities is a great way to connect with other data analysts from around the world and learn about the latest trends and developments in the field.
Finally, don't be afraid to reach out to data analysts whom you admire and ask them for advice or guidance. Many data analysts are happy to help others who are new to the field.
Find a Mentor
A mentor can provide guidance and support on a variety of topics, such as career planning, professional development, and technical skills. They can also introduce you to other professionals in the field, which can help to expand your network and create new opportunities.
There are a number of ways to find a mentor in the field of data analytics, including:
Ask your network. Talk to your friends, family, colleagues, and former professors to see if they know anyone who would be a good mentor for you.
Attend industry events. Industry events such as conferences, meetups, and workshops are a great way to meet other data analysts and potential mentors.
Join online communities. There are many online communities dedicated to data analytics, such as forums, discussion boards, and social media groups. Joining online communities is a great way to connect with other data analysts from around the world and to find potential mentors.
Reach out to people you admire. Don't be afraid to reach out to data analysts whom you admire and ask them if they would be willing to be your mentor. Many data analysts are happy to help others who are new to the field.
Consider a Data Analytics Internship
Data analytics internships are a great way to gain experience in the field and to make connections with potential employers. Internships can be found at a variety of organizations, including tech companies, startups, non-profit organizations, and government agencies.
There are a number of websites that list data analytics internships, such as Indeed, Monster, and LinkedIn. You can also search for internships on the websites of specific companies that you are interested in working for.
If you're studying, your professors and classmates may know of companies that are hiring interns. They may also be able to put you in touch with people who work at those companies. If you are interested in working for a specific company, even if they are not advertising internships, you can reach out to them directly to inquire about internship opportunities.
Polish Your Resume, Prepare for Interviews, and Start Applying
By the time you reach this step, you'll have the relevant data skills and experience to start work. Your resume is your first impression, so make sure it's polished and tailored to each job you apply for. Use keywords throughout your resume and quantify your accomplishments whenever possible. Proofread carefully and get feedback from others.
Once you start landing interviews, research the company and position, practice answering common questions, dress professionally, be on time, and be confident and enthusiastic. To start applying, network with people in your field, use job boards and company websites, tailor your cover letter to each job, and follow up after submitting your application.
Stay positive and persistent, and you'll be well on your way to landing your dream job!
What You'll Be Doing (and How Much Can You Can Earn)
Here’s how your salary and job description will change as your career progresses:
Entry-level data analysts typically focus on fundamental tasks such as gathering, cleaning, and preparing data for analysis. They also excel in identifying trends and patterns in the data and skillfully craft data visualizations to effectively communicate their findings to non-technical audiences.
According to Indeed.com, an entry-level data analyst can make between $65,437 and $130,918 per annum.
Moving up the career ladder, mid-level data analysts take on more complex responsibilities. They are involved in the development and maintenance of data pipelines, as well as the construction and training of machine learning models. They may lead a team of fellow data analysts and collaborate closely with stakeholders to identify and address business challenges using data-driven solutions.
A mid-level data analyst with 3-5 years of experience can expect to earn $81,594 or more. However, these statistics only pertain to generalists. Specialists will likely earn a far higher salary, even at mid-level.
Senior data analysts occupy the highest tier of this profession, playing a strategic role in organizations. They establish the overarching direction for data analysis within their company, mentoring and coaching other data analysts to elevate their skills. They are instrumental in developing and implementing data-centric business strategies and collaborating with senior leaders to make crucial decisions based on data-driven insights.
According to Indeed.com, a senior data analyst can expect to earn between $67,685 to $138,336 per year.
Examples To Learn From
It's easy to feel overwhelmed when you realize how much effort and work goes into becoming a data analyst. If you need a little inspiration, head over to YouTube. There are hundreds of videos by people just like you who have launched their careers in data analytics without experience or a degree. Here are two of our favorites:
Lore So What
YouTuber Lore So What became a data analyst without any experience whatsoever, and he's teaching others to do the same. You can view one of his incredibly popular how-to guides on YouTube.
Chidinma Afam is another popular content creator who launched her data analytics career without going back to school. She offers help to aspiring data analysts on her channel.
Where To Start Your Data Analytics Journey
Where to start your data analytics journey depends on where you are in your career. Here’s where to start if…
If You Have No Experience
If you have no experience in data analytics, the best place to start is by learning the basics. There are many online resources and courses available to teach you the fundamentals of data analysis, such as:
Python for data science
SQL for database management
Data visualization tools like Tableau or Power BI
Statistical analysis tools like R or NumPy
Once you have a basic understanding of data analytics, you can start to gain experience by working on personal projects, i.e., building a machine learning model to predict something, such as customer churn or product sales.
You can also gain experience by contributing to open-source data analytics projects or participating in data science competitions.
If You Have Some Experience
If you have a little bit of experience in data analytics, you can start to focus on developing your skills in specific areas of specialty, such as machine learning or natural language processing, data engineering, or data mining.
You can also start to look for entry-level data analyst jobs. Entry-level data analyst jobs typically require 1-2 years of experience, and they are a great way to get your foot in the door and start building your career.
If You Have Related Experience
If you have related experience, such as in business intelligence, statistics, or research, you may be able to transition into a data analytics role without any prior experience in data science. To do this, you will need to highlight your transferable skills in your resume and cover letter. You may also want to consider taking some data analytics courses or participating in data science competitions to demonstrate your skills to potential employers.
FAQs About Becoming a Data Analyst
We’ve got the answers to your most frequently asked question.
What’s the Difference Between a Data Analyst and a Data Scientist?
Data analysts and data scientists are both professionals who work with data, but their roles are distinct. Data analysts collect, clean, and analyze data to identify trends and patterns. Data scientists build and train machine learning models to make predictions and solve complex problems.
What Should I Learn To Become a Data Analyst?
To become a data analyst, you should learn the following:
Python or R for data programming
SQL for database management
Data visualization tools like Tableau or Power BI
Statistical analysis tools like R or NumPy
Machine learning fundamentals
You should also have a good understanding of business intelligence and data warehousing concepts.
Do Data Analysts Need To Know How To Code?
Yes, data analysts need to know how to code. Python and R are the most popular programming languages for data analysis. Data analysts use code to collect, clean, and analyze data, as well as to build data visualizations and machine learning models.
Can You Learn Data Analytics on Your Own?
Yes, you can learn data analytics on your own. There are many online resources and courses available to teach you the fundamentals of data analysis. You can also learn by working on personal projects and contributing to open-source data analytics projects.
How Long Does It Take To Get Good at Data Analysis?
The amount of time it takes to get good at data analysis depends on your prior experience and how much time you are able to dedicate to learning. If you are starting from scratch, it could take you 6-12 months to learn the fundamentals of data analysis.
However, if you have prior experience in a related field, such as business intelligence or statistics, you may be able to learn data analysis more quickly.