Your secondary schooling years can be fun, particularly when you get to spend time with your friends, play sports, and find your true talents. However, it’s also during these years that you usually start thinking about the career path you’d like to embark on for the rest of your life.
For many people, the most challenging part about choosing a career isn’t deciding what captures your interest, but instead how you’re going to enter that job and what steps you can take to reach your career goals.
For anyone interested in the relatively new field of data science, this can be a multi-step process. Learn more about some of the potential steps required to become a data scientist below.
Step 1: Gain Qualifications
Some people enter the field of data science with a set of self-learned skills, but others decide to study, become qualified, then look for master of data science jobs that suit their newfound qualifications.
While you don’t always need to enter a formal educational facility to gain employment in data science, it may help you land the position of your dreams. You can find out what’s involved in such a career, decide if it’s suitable for you, and learn the skills that data scientists need to be familiar with, such as programming, statistics, and data analysis.
Step 2: Develop Skills
Whether you do or don’t sign up for formal education, becoming a data scientist requires you to have a background in the skills that help you enter the field and make you qualified or experienced in various roles.
The name of the job is generally a clue of the high position of this role, so broad knowledge of engineering, statistics, mathematics, programming, data analysis, or information technology can be paramount.
With this foundation of knowledge, you can be in a much better position to expand your knowledge and learn about other data science fundamentals like Excel, SQL, Tableau, and Python. Such programs have endless potential in the field of data science and are used by big names.
For example, image processing from the Hubble Space Telescope has seen significant improvement thanks to Python, while Google powers its internal infrastructure with it. Essentially, the more you work on your skills, the easier it may be to advance to the role of a data scientist.
Step 3: Fine-Tune the Fundamentals
Having a basic understanding of data science and what it requires can be your opening into a data science career, but most businesses looking for a data scientist want someone who has a qualification or the necessary experience.
At this point, understanding what tools feature in the toolkit of a data scientist can be paramount. At a minimum, you will need to know how to repackage data into more user-friendly forms and build models to analyze behaviors using R or Python.
You may also be required to understand and be proficient in visualization programs like PowerBI and Tableau, among others. Gaining a qualification can assist with building up your knowledge, but you can also self-teach and build up your own set of skills as software and programs evolve.
Step 4: Understand Tools and Programs
As a data scientist, you will quickly learn that many tools and programs are helpful for various parts of your job. Early on, you will need to understand what these programs and tools are and how they serve you well while you perform different tasks in your current or future role.
For example, if your role focuses on machine learning, you might excel in this position using tools like TensorFlow, Accord.Net, and h20.ai. However, for statistical modeling, machine learning functions, and even data viz, open-source Jupyter Notebook might be your tool of choice.
Familiarizing yourself with statistical programming languages such as Hive, R, and Python may also contribute to your success in this new field.
Step 5: Combine Theory With Practice
One of the most monumental occasions for data scientists in training is combining their knowledge and skills of programming and tools with practical tasks. You might be proficient in the theory of the most appropriate tools to do particular jobs, but completing projects using those tools can be a turning point in your potential career.
Practice makes perfect, so tackle projects that allow you to tap into as much of your newly acquired knowledge as possible. For example, you might begin by researching a market sector or company, collect data for the task, clean and test that data, then create and use algorithms to analyze the data. The final step might be to package it into a more user-friendly visual that users can interact with.
The more real-life data science situations you put yourself in, the more proficient you may become in turning your theoretical knowledge into real-life experience.
Step 6: Build a Portfolio
With education, study, and practice, you can eventually gain enough experience in the real world to build a portfolio. This can help you demonstrate your skills, show off your completed real-world projects, and highlight the many programs and tools you’re now competent in using.
The more attractive your portfolio, the easier you may find it to land your dream data science job. Once you do, on-the-job learning may be able to help you reach new heights.
Step 7: Apply for Jobs in Data Science
After gaining your master of data science or becoming experienced in the industry, you may now be ready to apply for jobs in this field. Fortunately, many jobs allow you to put your newfound skills to the test.
You might decide to become a data analyst working with programming languages like HTML, SQL, R, and Python or a data scientist with the skills of a data analyst plus experience in machine learning. Data engineering, where you get to build and optimize systems for analysts and scientists, may also appeal, or a career as a machine learning engineer or data and analytics manager.
The options are truly endless, and the more you learn and the more experience you gain in the field, the easier you may find it to secure a role that you’re passionate about and can learn and grow within.
Becoming a data scientist can require passion, hard work, and dedication, but it can ultimately put you on the path to success. Consider your education options, familiarize yourself with the tools you’ll need, and get as much practice as possible. You may then be able to enjoy the fruits of your labor in fulfilling and challenging roles around the world.
Also Read: Dark Secrets of Data Science Which You Should Know
