How to Become a Data Scientist in 2024?

Data science has gained widespread popularity with recent years' rapid technological advances and digitization efforts. It is a multidisciplinary field that combines analytical tools, scientific principles, and statistical algorithms to derive hidden patterns and meaningful insights from data.

Consequently, it involves a wide range of expertise, such as data engineering, programming, mathematics, statistics, visualization, and IT infrastructure.

The primary objective of data science is to create as much business impact as possible using data, no matter how complex the tools or models involved are.

One after the other, companies across the globe have been turning to data science to solve the most diverse problems out there. As a result, data scientists have gained an advantageous position regarding employment and payment.

As a result, many people are becoming interested in finding out how to become data scientists.

If you are one of them, look no further than this article. Here, I will help you learn everything you need to know about becoming a data scientist in 2024, including a simple and effective way to learn what you need to enter the data science world successfully.

What exactly does a data scientist do?

How to Become a Data Scientist
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Data scientists investigate, extract, and report significant insights into their organization’s data. They communicate these insights to non-technical stakeholders and have a solid understanding of machine learning workflows and how to tie them back to business applications. In addition, they possess mathematical knowledge, programming skills, and business expertise to effectively span the business and technology industries and make a notable impact.

Apart from industry knowledge and technical expertise, a data scientist must be inquisitive and result-oriented while having efficient communication skills to present findings to technical and non-technical audiences.

In a business environment, they must work across multiple teams to lay the foundations for powerful analytics. For that, they need to develop strategies to capture, collect, and clean data from various sources.

After organizing and exploring this data, they can build solutions and show their findings to the wider business.

It is essential to point out that data scientists do not work alone. Successful data science projects need an effective team-based approach involving close partnerships with data engineers, IT architects, application developers, analysts, and business stakeholders to create a proper solution.

Why should I become a data scientist?

There are many valid reasons to pursue a career in data science. First, it is an up-and-coming and rewarding industry that can provide an intellectually challenging and stimulating environment. In addition, data scientists must constantly strive to stay ahead of the latest technological trends and developments, working in a frequently changing setting.  Thus, if you are naturally inquisitive, have an analytical mind, and enjoy working with data and technology, then becoming a data scientist could be the right choice. 

Moreover, the job market offers many opportunities for talented data scientists.

Considering data from Statista, we can expect the big data market size to grow significantly in the coming years.

It is predicted to be worth $103 billion in 2027 (compared to $70 billion in 2022). Likewise, the US Bureau of Labour Statistics predicts a 36% rise in data science industry jobs between 2021 and 2031 – much higher than the national average of 4%. 

This growth is mirrored in the growing popularity of data science careers, with organizations such as the U.S. News & World Report ranking “data scientist” as the 3rd best job in technology, 6th best in STEM jobs, and 6th best overall job.

Glassdoor similarly considered it the 3rd best job in America for 2022. Finally, the average data scientist receives a generous median salary of $102k annually.

What are the qualifications a data scientist needs?

There is some debate regarding the necessity of a degree to become a data scientist. Although many professionals have entered the industry through other routes, a university qualification can benefit you.  Graduate schemes and jobs usually look for individuals with qualifications in computer science, data science, engineering, statistics, mathematics, and even physics. However, some schemes are willing to train anyone with a degree to become a data scientist.

A working knowledge of programming languages such as Python, R, SQL, and Julia can be helpful. However, some people may feel they can rely on their own self-directed learning, developing the required skills and experience at their own pace and convincing employers during interviews.

Also, you can get certified as a data scientist with DataCamp and prove your data science knowledge to potential employers.

How to become a data scientist in 2024?

woman using computer on table

After reading thus far, you must be excited to start your journey to becoming a data scientist. However, the question remains: where to begin? Below, I have listed eight steps you must take to become a data scientist from scratch. As discussed above, the exact data scientist requirements will depend on various factors; nevertheless, these are some of the most commonly cited steps.

1. Learn data visualization, data wrangling, and reporting 

Anyone who works to become a data scientist comes across vast and complex datasets. To make sense of this information for yourself and others, you must learn how to deal with it. Having skills in data wrangling will allow you to clean, organize, and transform raw data into a format you can analyze and draw conclusions from. 

Although there are many tools for data wrangling, people tend to opt for libraries like pandas in Python. To present your data effectively, you must master reporting and data visualization.

2. Try to hone your machine learning, math, and statistical skills

Although you exactly require a degree in these fields, you do need to have a basic knowledge of these areas. I recommend you work on essential areas such as calculus, linear algebra, and statistics.

However, we also need to assess your intent behind learning these things. For example, understanding calculus can help you create optimization algorithms for machine learning. Similarly, knowing about gradient descent can enable you to measure the change in a function’s output on tweaking the inputs. That, in turn, can help you refine machine learning models. 

3. Learn how to code 

Two of the best languages that data scientists should learn are Python and R by virtue of their versatility and ubiquity. Since working with data invariably means working with databases, SQL is another vital programming language for you. Fortunately, it is a relatively straightforward language after learning Python and R. 

Julia is a decent choice for people who have learned Python, R and SQL. It is a language specifically optimized for data science, making it fast and intuitive.  You might require additional languages if you start working with large data sets; until then, these four will suffice.

Java is an open-source language that excels in terms of efficiency and performance. For data science, Java Virtual Machines provide a reliable framework for popular big data tools such as Hadoop, Scala, and Spark. Other coding languages that can help you with data science with enormous data sets include C/C++, Go, JavaScript, MATLAB, SAS, and Swift. 

4. Work hard to understand databases 

Relational databases help data scientists store structured data quickly and efficiently. When collecting and organizing data, you will often discover that SQL is your tool of choice. It enables you to handle structured data, query databases, wrangle, prepare, and experiment with data. Moreover, data scientists often deploy it alongside Python with libraries such as MySQL, PostgreSQL, and SQLite, helping you connect different data sources. 

5. Learn to work with big data

I have already mentioned that, as a data scientist, you will frequently have to work with huge sets of data. In the modern era, where data is simply overflowing everywhere, these data sets are becoming increasingly larger. That makes them more challenging to collect, process, and maintain. 

Nevertheless, a talented data scientist can extract new and profound insights from these massive data sets. Thus, learning to use cloud platforms such as AWS, Google Cloud, and Microsoft Azure gives you much to gain. Likewise, tools such as Apache Spark can help you out with big data processing, analysis, and machine learning.

6. Try to get more experience, practice, and meet fellow data scientists

Like every other career, you will require as much experience and practice as possible to become a good data scientist. Fortunately, there are several ways for you to become a part of communities, take up projects, and build on your data science skills. 

For example, DataCamp Workspace offers a collaborative cloud-based notebook that lets you analyze data, collaborate with others, and share insights. It is designed to take people from learning data science to doing it. It also features built-in datasets enabling you to analyze data within minutes.

Also, you can apply your knowledge to various data science projects and solve real-world problems from your browser.

7. Apply for a job or an internship

After developing all the necessary skills, you need to start applying them in more professional settings. Then, when you are confident enough you have the crucial data scientist skills required to meet the expectations of a role, you can start applying for jobs or internships. You will likely need a comprehensive portfolio demonstrating a wide range of skills, and you must prepare for the data scientist interview well ahead of time. 

8. Follow and engage with the data science community 

To become a data scientist, you must strive to keep up-to-date with a fast-paced industry. Engaging with the generous and dedicated data science community is the most efficient way to stay informed about developments in the field. 

Apart from social media platforms such as LinkedIn, Discord, Twitter, and Reddit, there are various niche blogs, websites, and data science leaders that you can follow. Try to look for people interested in the same areas as you, contribute to discussions, ask them for advice, and get involved with what’s happening. Also, you should check out the DataFramed Podcast to get industry news from a group of experienced data professionals.

Conclusion

Working as a data scientist can be intellectually challenging and analytically satisfying. As big data continues becoming increasingly crucial to how organizations make decisions, data scientists have become more in demand. Although becoming a data scientist will require some training, a rewarding career will be waiting for you. 

About Gaurav Tiwari

Gaurav Tiwari is a blogger, influencer and designer with expertise in brand regeneration and growth hacking. He is the co-founder of Gatilab, a successful digital agency focused on content and design.