Big data analysis jobs are in almost every industry and have attracted many people to follow this path because of the benefits and potential it brings.
So, how to become a big data analyst? From my experiences in this field, I have built an eight-step roadmap, from the first step of gaining the basics to the final milestone of professional development.
Don’t worry if you know nothing about this field because I will show you everything to succeed from scratch right now!
Overview
First, let’s learn about big data analytics in general, including concepts, required skills, roles, and working environment!
Who Is A Big Data Analyst?
A big data analyst works with vast and complex datasets (big data) to help companies and organizations make decisions using data.
These analysts work in many fields as organizations increasingly recognize the value of data-driven decision-making, including:
- Finance
- Healthcare
- Marketing
- E-commerce
These roles are important because they help companies make better choices based on facts, not just guesses.
Required Skills
Big data analysts need the following skills to handle complex data tasks, work with different data storage systems, and apply advanced tools to extract meaningful insights from big datasets:
- Data handling: Be good at organizing and cleaning up lots of data from different places.
- Computer skills: Use coding languages like Python, R, SQL, and Java.
- Data warehouse skills: Know how data is stored and managed in data warehouses, including knowledge of database systems and data warehousing concepts (ETL processes, data modeling, and integration).
- Math skills: Understand math and statistics to find patterns in data.
- Data visualization: Create clear and meaningful pictures and graphs to show data.
- Computational framework skills: Be familiar with Hadoop, Spark, and other popular frameworks to process and analyze large datasets.
- Domain knowledge: Understand the industry to make sense of the data.
- Problem-solving: Be good at solving tricky data problems.
- Communication: Explain findings and insights to non-technical stakeholders.
- Ethics: Know about data privacy and act responsibly with sensitive information.
Roles and Responsibilities
As a big data analyst, I handle big data to extract valuable insights for informed decision-making. Specifically:
- I collect and clean data from different sources.
- I analyze the data using computer programs (Python, SQL, etc.).
- When I find essential things in the data, like trends, patterns, and anomalies, I make pictures and graphs (data visualizations) to show what I found.
- I build computer programs to make predictions.
- I use data to solve business problems.
- I work with data engineers and scientists to optimize data pipelines and contribute to building ML models for predictive analysis.
- I keep learning about new data tools.
During work, I always observe and monitor the quality of work. If there are any errors, I must fix them immediately to ensure work progress.
Work Environment
Big data analysts typically work on computers in fast-paced settings and are often part of teams with other data experts and business pros. They can work from home or in an office (depending on the job).
One thing that impresses me is the quick changes in everything in the field. There is more and more data to process, and working tools are increasingly modern. So, I have many chances to learn and develop.
How To Become A Big Data Analyst
Now, we’ll dive into the part you’ve been waiting for: The eight steps to becoming an analyst in big data! You will definitely get this career if you follow these steps!
1. Get A Bachelor’s Degree With A STEM Background
The first step is getting a Bachelor’s degree. Most companies prefer candidates with degrees in STEM (science, technology, engineering, and mathematics) because these subjects teach skills that are super useful for this field, like math, programming, and statistics.
Getting a degree in STEM areas also helps you become a better problem solver and a logical thinker, which are vital for understanding data.
To pick the right place to get this degree, consider these factors:
- The location
- How much it costs
- Reputation
- The expertise of the teachers
- What resources the program offers
While your Bachelor’s degree is crucial, I recommend taking other project and database management classes. These skills are handy for handling data projects efficiently.
2. Build Technical Skills
To become an analyst in this field, here are some vital technical skills you should focus on:
- Statistics: You should understand basic statistics because it helps you make sense of data.
- Python or R programming: Learn either R or Python (or both). They are computer languages used for data work.
- SQL: This is for managing and getting data from databases.
- Data cleaning: Learn how to clean data up for analysis.
- Data visualization: You should know how to make pictures and graphs from data. It helps people understand the data better.
Besides these vital skills, you should look at job ads for the data jobs you want. They will often mention the specific skills they need. Then, focus on learning those skills.
Apart from these technical skills, being a good analyst involves soft skills. You should be able to communicate well because you might need to explain things to many people. If you understand the industry you want to work in, that’s a plus.
3. Practice Data Analyzing Skills
After learning the basics, the next step is to practice data analyzing skills. And one of the best ways to practice is by doing your own projects.
While you’ll learn some basics from your classes, personal projects let you practice and get really good at analyzing data.
To begin your project, pick a subject you’re interested in. It’s easier to learn when you care about the topic. Then, think about how you’ll analyze the data. What questions do you want to answer? How will you do it?
To get the data you need for your project, you can use online datasets, including:
- DataCamp Workspace: It’s an online tool that gives you data to practice with.
- Kaggle: This website has tons of data on many topics you can use for your projects.
- UCI ML Repository: They have clear and documented data you can use.
- FiveThirtyEight: They offer data related to their articles on different topics.
- Dataset Search by Google: It’s like Google but for datasets. You can search for more than 25 million datasets for free.
After having the data, you need to practice analyzing it by using your skills to clean it up and find patterns or answers to your questions. Remember to make charts or graphs to make it easy for others to understand.
4. Master Tools and Techniques
To become a skilled analyst, you’ll need to learn various tools and techniques to work with big data. Here are some popular ones:
- MapReduce is used to handle large datasets.
- Hive makes data analysis easier.
- Sqoop helps move data between your computer and Hadoop (the Hadoop ecosystem is the heart of many Big Data projects).
- Impala is for quickly asking questions and getting answers from your data.
- Pig simplifies writing programs for data analysis.
- HBase: A database for handling lots of data.
- Apache Spark: Known for speed, you can use it for data processing and analysis.
- HDFS is where you store your data in Hadoop.
- YARN helps manage and organize resources for data processing.
- Flume is for gathering data from different places to one central location.
While you can learn these tools by yourself, I recommend getting formal training to ensure you know how to use them correctly. It can boost your confidence and help you avoid mistakes when working on real projects.
5. Take Part In An Internship
If you’re still in college, ask your college’s career office about internship programs. An internship on your resume shows employers you have practical skills and can work in a real job.
Experience working in this position will help you be more confident in applying for entry-level positions in the next step.
Besides technical skills in data analysis, you also develop vital workplace skills like teamwork, communication, and problem-solving.
During internships, you can figure out what parts of data analysis you like the most. Then, you can choose a specialization that suits your abilities and interests.
6. Gain Experiences In Entry-Level Jobs
Typically, the experience you gain in a short-term internship program isn’t enough to really impress an employer. So, the next step is to apply for entry-level positions:
- Junior BA
- Statistician
- Data Analyst
These jobs let you use your data skills in real situations. You will be involved in more tasks and projects to develop your skills than in your internship program.
As a result, you’ll get better at working with data and making decisions because you’ll use these skills daily.
These entry-level jobs are the starting point for your career. You can show you’re good at your job and move to more advanced roles.
7. Consider Advanced Certifications Or Degrees
As you gain experience in your job, it’s good to think about how to move up. You can consider getting certificates like CAP or CCA Data Analyst. These show you’re skilled and can help you get higher-paying jobs.
Otherwise, consider doing a degree online in data science from a respected university. So, you can keep working and earning money while you learn more.
If you want to get into specialized roles, such as data scientist or data engineer, a Master’s degree in a related field can be a big boost. It’s not always needed, but it can open doors to better jobs and higher pay.
8. Networking and Professional Development
To grow in the field, it’s crucial to network and develop your expertise because this can lead to jobs, collaborations, and mentorship, providing insights and guidance.
I recommend attending data-related events, both online and in person, and joining industry groups. Also, engage in online forums and consider finding a mentor.
You should define clear career goals to focus your efforts. Also, share your work and thoughts on LinkedIn or a blog.
Because employers often value pros who actively invest in their career growth, these tips will open doors to better roles.
Why Should You Choose A Career As A Big Data Analyst?
Data analysis is a career choice that offers stability, good pay, variety, and interesting challenges.
First, many jobs are available because many industries need big data analysts. The global market for big data analytics was worth $51.81 billion in 2021 and can grow to $142.5 billion by 2030, with an annual growth rate of 11.9%.
Second, data analysts often get paid well. According to ComputerCareers, a big data analyst can earn between $81,000 and $129,000 per year, with a base salary of $70,000 to $109,000 (in 2023).
Third, you can work in different fields, including healthcare, finance, retail, and marketing. So you won’t get bored! What’s more, the skills you learn as a data analyst, like problem-solving and understanding data, can be used in other jobs.
FAQs
Is Big Data Analysis A Rewarding Career?
Yes, this career is rewarding. It offers job security, good pay, interesting work, and chances to grow your career.
Is It Hard To Become A Big Data Analyst?
It can be challenging but doable with effort. You need technical skills, statistics knowledge, and the ability to work with lots of data.
Can You Become A Big Data Analyst Without A Degree?
Yes, it’s possible, but challenging. Many employers prefer degrees in subjects like computer science or data science. But if you don’t have a degree, certifications and experience can help.
What Skills Does A Data Analyst Need?
Data analysts need skills in data analysis, using tools like Python or R, SQL, and stats, and knowing the industry. Good problem-solving and communication skills are also important.
Is Data Analyst A High Salary Job?
Yes. Data analysts can earn good money, especially with experience. Salaries vary by where you work and your skills, but many data analysts earn above-average pay, around $102,000 per year.
Conclusion
Now you know how to become a big data analyst. You can start with a strong STEM foundation and learn essential tech skills. Then, keep learning and gain experience.
The field has growing demand and good pay, and it’s satisfying to solve data puzzles. By following these steps and staying flexible to changes in data analysis, you will have a successful and fulfilling career in this role in the coming year!