With an increasing number of companies growing and expanding in Brazil, the demand for data analysts has never been greater. If you like numbers, solving problems and sharing knowledge with others, then a career as a data analyst might be perfect for you. Earn a college degree, learn important analytical concepts, and gain solid work experience to become a successful data analyst.
Part 1 of 4: Developing your training
Step 1. Get a college degree
Most data analyst positions require at least a college degree. To become a data analyst, you must earn a degree in areas such as math, statistics, economics, marketing, finance or computer science.
Step 2. Consider getting a master's or doctorate degree
Higher-level data analyst positions may require a master's or doctoral degree, which usually guarantees a higher salary. If you are interested in this, consider what the ideal graduate degree is to achieve your career goals.
Examples of postgraduate degrees include a Masters in Data Science or Business Analysis
Step 3. Enroll in subject-specific courses
If you feel you need help with calculus, or if you want to learn about programming, look for courses that will provide you with the knowledge you need to become a data analyst. These courses can be face-to-face or online.
When looking for courses, see if any local universities are offering seminars or classes on your desired subject. Also see if there are any workshops or training in your area
Part 2 of 4: Acquiring Necessary Skills
Step 1. You must master higher level algebra
Data analysts work with numbers every day, so get used to working with math. Having a solid knowledge of higher-level algebra is very important. You should know how to do things like interpret different functions and their graphs, and be able to work with real-world problems.
Knowing multivariable calculus and linear algebra will also help
Step 2. Understand Statistics
To become a data analyst, you're going to need to interpret data, which is where the statistic comes in. Start with high school or college level statistics subjects, then look for more advanced areas that may be essential for your desired job.
- Mean, median, and mode, as well as standard deviation, are examples of statistical concepts you learn in high school and college.
- Having a good grasp of descriptive and inferential statistics will also be very helpful.
Step 3. Develop your programming skills to gain more prominence
While you don't need to be a programming expert to start out as a data analyst, you should be comfortable working with languages in specific situations. Start by learning languages like Python, R and Java, and then look for other types of languages.
- Programming in SQL is another common task among data analysts.
- You can take online courses to learn programming languages and concepts.
Step 4. Develop your communication and presentation skills
After analyzing your data, you should be able to talk about it with others. Know how to explain complicated information in a way that people from other areas can understand. Practice with programs that display data more visually.
You must be able to transmit data visually and verbally. Learn to use tools like ggplot and matplotlib to illustrate your findings
Step 5. Familiarize yourself with Microsoft Excel
As a data analyst, you will need to organize data and calculate values, so you need to master Excel. There are many free online tutorials and websites that can teach you everything you need to know about Excel and its features.
Step 6. Learn about machine learning
Teaching a computer to generate predictions or make decisions on its own based on the data received is what defines machine learning and is something very important in the area of data analysis. Look for online courses that can teach you everything you need to know about machine learning. Take advantage of the many free courses.
- To understand machine learning, you must have a solid foundation in programming and statistics.
- There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
- An example of supervised learning is filtering your emails in your inbox, automatically sorting out what is spam. Unsupervised learning is when Netflix suggests series and movies you might like. Reinforcement learning, on the other hand, occurs with an autonomous vehicle and its ability to see and adapt to the environment.
Part 3 of 4: Getting Work Experience
Step 1. Look for areas of the industry that need data analysts
Focus your searches on areas that need more data analysts than others. Marketing agencies, technology companies and financial institutions tend to hire data analysts to help interpret data and explain it more simply.
Visit the websites of companies you're interested in and see if they're hiring, or do a more general online search. If you already have a friend who works in one of these areas, ask them if they know anyone who is hiring
Step 2. Apply for an internship as a data analyst
Internship is a great opportunity to work for a large company. Many data analyst internships require you to be at university to apply. Depending on the area of the company, you should know how to program in Python, R or SQL – knowing all three is even better.
Many of these internships are unpaid or only last a few months, so check all the details before applying
Step 3. Join a business organization
Business organizations are a great way for you to access workshops, networking opportunities, or online help centers. There are several organizations related to data analysis, such as TechAmerica or the Association for Computer Machinery. Do a quick online search to find out if you are interested in participating in any of them.
To join a commercial organization, go to the website of the organization of interest and review membership plans. You are often able to create a free account with access to a limited number of features. There are usually different membership levels that give different benefits depending on the amount paid
Step 4. Search for beginners' openings
These openings allow you to gain the knowledge and experience essential to earning higher-level data analyst positions. Starter openings pay very well, and companies are always looking for people for positions such as Statistical Data Analyst or Business Analyst.
Beginner positions generally require a college degree, without the need for a master's or doctorate degree
Part 4 of 4: Being interviewed for a position
Step 1. Put together a professional resume and write a cover letter
Your resume and cover letter are the first impression a potential employer will have of you. Spend some time describing your skills and work experience to show that you are the right person for the job. When everything is ready, do a good review of the resume and cover letter to make sure there are no mistakes.
Step 2. Research the company before the interview
Doing good company research allows you to come to the interview prepared for a real discussion about the job. Go to the company's website and read about current projects or the programs they use.
If the company is present on social networks, check their profiles to follow the publications
Step 3. Practice answering the most likely questions
Do online research to discover top interview questions. Practice your answers with a friend, or record yourself so you can watch and find out what you need to improve.
Likely questions include "How do you define big data?" or “Talk about issues that data analysts might face during analytics.”
Step 4. Get ready to show off your technical skills
Depending on the position, you may be asked to demonstrate your skills. Find out the types of programs the company uses before the interview, and be prepared to show that you are capable of using those programs.
Technical skills include knowing how to program, knowing several programming languages, or being able to analyze data using different resources
Step 5. Think of questions to ask the interviewer
At the end of the interview, ask questions like "What types of projects will I be involved in?" or “Which program do you prefer to use for data visualization?”. Asking this type of question shows that you are interested in the job and can make you more prominent as a candidate.