The demand for data analysts has never been higher right now. According to the U.S. Bureau of Labor Statistics, employment of data analysts is expected to increase 25% by 2030, meaning data analytics will have one of the fastest growth rates in the country.
But how do you become a data analyst? What kind of experience and educational background do you need? And what are the soft skills of data analysts?
In this guide, we’ll cover everything you need to know to become a successful data analyst. This includes where to start, the specific steps you need to take, and the data skills you need to acquire along the way.
What does a data analyst do?
A data analyst is responsible for collecting, cleaning, and analyzing large data sets to extract meaningful information and make decisions. They use statistical and computational techniques to identify patterns and trends in data and present the results in clear and understandable terms to interested parties.
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Is It Hard To Become a Data Analyst?
Becoming a data analyst isn’t hard per se, but it does require some technical skills that may be harder for some than others. Additionally, due to continuous advancements in the field, data analytics is a career that requires ongoing education.
How To Become a Data Analyst
Complete a course
One of the quickest (and most effective) ways to learn all the skills you need to succeed as a data analyst is to get comprehensive online training. You’ll learn everything you need to know and have access to local mentors.
Understanding the basics
The first step on your journey is to make sure you’ve mastered the basics. You want to make sure you understand the key principles of data analysis, the different types of data analysis, and the tools used by data analysts.
Meets educational requirements
Data analysts spend a lot of time working with numbers. For this reason, many employers look for applicants with a degree in mathematics, statistics, computer science, or a related field.
However, you don’t need a formal college degree to get a job as a data analyst. In fact, companies often hire candidates who have completed an online data analytics course, a data analytics bootcamp, or who can demonstrate the necessary technical skills and knowledge.
Choose required programming language (or more)
Not all data analysts need to know how to code (although most data scientists do). In fact, at the beginning of a career, a smattering of the most common query languages should be sufficient. However, if you really want to become the best (most employable) data analyst possible, consider learning one (or more) of the essential programming languages (e.g. Python, R, SQL).
Get Familiar With Data Analytics Tools
Data analysts use a variety of tools and applications to collect, manipulate and analyze data. You should be familiar with the most commonly used analytics tools, including:
TableAu
A popular data visualization tool for performing data analysis, manipulation and visualization
Microsoft Excel
A popular spreadsheet commonly used by data analysts
Microsoft Power BI
A free data visualization and business insights tool ideal for collaboration
Google Analytics
A web analytics tool to monitor website traffic
Jupyter Notebook
An open source web application that enables exploratory analysis using “big data” tools (e.g. Python, R, and Scala ) allows.
Develop Your Data Analyst Skillset
To become a data analyst, it is essential to have the right skills. Make sure you have developed the following transversal and technical skills:
Statistical analysis and mathematical skills
data analysts work with numbers. Enough. Data analysts should have strong mathematical skills and be familiar with analyzing data sets.
Query and programming languages
Process data and analysts understand others and use a variety of programming and query languages, such as Python and R. As a data analyst, you probably won’t need to write many programs, but it’s important to become familiar with the commonly used languages and understand how they work.
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Microsoft Excel
MS Excel is a go-to tool for data analysts. It is widely available, easy to use, and has many useful features.
Data visualization
If your analytical information is garbled, what are their value? A good data analyst can generate meaningful insights and present those findings visually in the most understandable way.
Machine learning
One of the informal differences between Data Analysts vs. Data Scientists The difference between data scientists is that data scientists use machine learning to design new data modeling processes. As a data analyst, you probably don’t need to do this. However, to work effectively with data scientists, you need to be familiar with these processes and understand how they work.
Soft Skills
Analytical Thinking
Data analysis is the process of taking raw data and transforming it into meaningful information. After the data is refined, analysts analyze it to uncover deeper truths and values. This can only be achieved through analytical thinking.
Problem-solving skills
Well-developed problem-solving skills help students to recognize the data. analyst. nature. of the problem at hand, which data analysis tools and techniques are best suited to solve the problem and the solution to the problem itself.
Presentation skills
Data analysts need to present their ideas effectively and in a way that everyone, especially stakeholders they cannot understand. Strong presentation skills enable you to convey your ideas and predictions in an engaging way that engages your audience.
Interpersonal and communication skills
Data analysts rarely work in a bubble. Indeed, as an analyst, you are likely to interact with a wide variety of people, including peers, managers, and subject matter experts. Strong interpersonal and communication skills will help facilitate collaboration between different parties.
Pursue Volunteer, Open-Source, or Freelance Projects To Build Your Portfolio
Building a solid portfolio is one of the best and safest ways to showcase your analytical skills. Search for volunteer opportunities, find freelance work or work on a personal project to showcase your analytical skills, data analyst portfolio, but don’t have it available and don’t know where to start? Consider using a publicly available dataset. You can find all kinds of datasets on sites like Data.gov and Datahub.io.
It’s helpful to find a dataset that covers a topic that you already interested. interested. But above all, choose the data that will be analyzed best with the methods that are most familiar to you; This allows you to fully demonstrate your strengths as an analyst or data scientist.
Apart from personal projects, you also want to complement your portfolio with projects that demonstrate analytics of your skills. skills and demonstrate your ability to solve real-world problems within a team.
This is where volunteer work and freelance projects come into play. Look for opportunities that allow you to collaborate with others as you move through different stages of the data analytics lifecycle. Not only will this experience make a good impression on potential employers, but it will also help you develop the data analytics skills you need to be successful.
Build Your Network
A good network can help you get your foot in the door, build valuable relationships with professionals , find a mentor and more. Consider these resources to start building your professional network today:
The social networking service popular among professionals makes it easy to find and connect with data analysts and other people with similar interests.
Online Community
Online communities are not only great for meeting like-minded people, they are also a great source of support and useful information.
When you attend a conference or data analyst meeting, you can connect in person. It’s a great way to leave lasting impressions that you can benefit from in the future.
Find a Mentor
Whatever your profession, a mentor can offer you advice and support to help you achieve your career goals. Through them, you can gain a lot of valuable information, including how other analysts got started, the different career paths an analyst can take, and the pros and cons of each of these different paths. As a mentee, you’ll benefit from your mentor’s hands-on learning experience and gain practical insights you can use to plan your ideal career.
A way to find a good a mentor, use your professional network. Reach out to your contacts to let them know you’re looking for an experienced data analyst to guide you. Use professional networking platforms like LinkedIn to find potential mentors and reach out to them to offer assistance.
Before reaching out to someone to be your mentor, make sure they have a goal in mind. Also be prepared for detailed questions and answers to possible questions. Your potential mentor is likely to ask you questions about your personal and professional life, including your career goals, where you want to go, and why you want to be a mentor.
It’s also a good idea to come up with a plan or framework for how you think your mentorship would work. You need to be clear from the outset how much time you want to spend with your mentor, what kind of advice you are looking for, etc. To make the most of your mentorship, it is essential to know what you are looking for. they are after an advance search.
Consider an Internship
An internship as a data analyst can be one of the best ways to enter the industry. As an intern, you’ll work on real projects, develop your analytical skills, and gain valuable insights into the world of data analytics. Internships also offer great networking opportunities. Internships also give aspiring analysts the kind of hands-on experience that looks great on a resume.
Get your resume right, prepare for interviews, and send your question.
Now that you’ve honed your skills and built a professional portfolio, it’s time to start preparing your application. Talk to your mentor: Ask them to approve your portfolio and help you prepare for the interview. Leverage your network to find job opportunities and apply for entry-level data analyst jobs.
