Your Complete Guide to Becoming a Data Scientist in 2024

The modern business landscape has evolved into a technological engine where success depends on how soon and well you adopt the newest technologies.

At the forefront of this rapid change and technological evolution is data science, leading advancement in virtually every sphere of human life. From entertainment, healthcare, and supply chain to manufacturing, financial services, and retail, it has changed the way these businesses have operated for hundreds of years by simply furnishing valuable insights from large chunks of data.

According to a recent report by the US Bureau of Labor Statistics, data science industry jobs are predicted to grow 35% by 2032, which is 4% more than the US national average. Similarly, the industry is projected to grow $103 billion in 2027 compared to only $70 billion in 2022.

These numbers do not do justice to the immense growth potential that the industry holds and, therefore, are fairly modest in their outlook. However, according to our estimates, data science will virtually influence every business market and field by 2030 and will be the cornerstone of the modern decision-making process along with big data analytics, machine learning, and artificial intelligence (AI).

So, if you want to get ahead of the game, then this is probably the right time to do so, and the road to success might not be as difficult as you think it is. All you have to do is embrace change, have faith in your learning abilities, and dive into the world of data science.

In this article, we will provide a complete guide to kickstarting your data science career from scratch by clearing the basics of what data science is and what it is not while providing So without further delay. Let’s get into it.

What’s Data Science?

In simple words, data science is the process of generating actionable insights from large volumes of data. From finding the right customer base for your products to setting the right prices for consistent sales, minimizing costs, and streamlining errors, data science is pushing business innovation and strategic decision-making to the next level.

Termed the “sexiest job of the 21st century” by Harvard Business Review, data science is a combination of math, statistics, and specialized programming, as well as AI, machine learning, and subject matter expertise in a field. A top data scientist should be well-versed in every step of the pipeline; however, you don’t need all of these skills to get started.

Difference Between Data Science and Data Scientist?

In its entirety, the data science lifecycle is very vast and involves various roles, tools, and processes that are divided between data engineers, data scientists, and data analysts. For instance, data pipelines are managed by data engineers and further create machine-learning-powered predictive models and algorithms in collaboration with or on the recommendation of data scientists.

So, the primary job of a data scientist is somewhat similar to a data analyst in terms of exploratory data analysis and data visualization, with the only exception that a data scientist does it on a much larger scale, with data sets and overall problem scope.

This brings us to the next important question: what is the difference between the average business and data analyst and a data scientist?

What is the difference Between a Data Analyst and a Data Scientist?

Comparatively speaking, both a data analyst and a data scientist’s job is effective management, organization, and communication of unstructured data. So, a data scientist’s role, in plain terms, encompasses the data analyst in its entirety, but the required skillset is technically much broader.


For instance, a data analyst leverages tools like Microsoft Excel and SQL to organize small sets of data and leverage historical data insights and trends to promote informed decision-making.

Contrarily, data scientists leverage complex predictive models and develop algorithms in combination with advanced tools such as Python and R to generate deep insights, solve complex issues, make future predictions, and create data-driven innovations.

Now that you are clear on what the field is and what is not, let’s get you started.

A Complete Roadmap to Becoming a Data Scientist in 2024

The journey may sound challenging, but as we have discussed, there is no field right now that is more rewarding and intellectually stimulating than data science. The simple way of building a career in data science runs through a professional degree, but there are many other ways you can enter the field while continuously building and polishing your skillset on the job, which we believe to be a much better option.

For this purpose, we have combined a total of five steps that would be enough to start your journey without a professional degree. However, before you start your online learning expedition, make sure you have a fast and reliable internet at hand for a smooth and seamless learning experience, as polishing your skills as a data scientist can be a data-intensive process.

For this purpose, we recommend Xfinity as the provider of choice that is already quite popular among the data science community for its blazing-fast speeds, advanced security features, and extreme reliability. For more information, contact the Xfinity customer service team to sign up for the plan of your choice today.

Build Basic Programming Skills

As a data scientist, you will tackle many complex and large data sets on a daily basis.

So, to manipulate databases, you will need to learn Python and R for starters and slowly work your way to SQL and Julia to build your own models as an advanced practitioner.

Acquire Data Wrangling, Visualization, and Reporting Skills

The very basics of data science involve the ability to clean, organize, and transform raw data into digestible formats to draw intelligible and impactful conclusions.

This is where data wrangling, data visualization, and reporting tools come in handy. Surprisingly, most of it can be achieved by just one tool, Python.

Build an In-depth Understanding of Databases

Your first step in building a solution set for any given problem begins with storing and organizing large amounts of data in a fast and efficient way.

Herein, basic knowledge of SQL and Python will help you structure relational databases and manipulate them to generate outcomes in several ways.

Learn the Basics of Cloud Platforms

Your role as a data scientist will frequently put you against large data sets unlike anything you have seen before.


So, the basics of cloud platforms such as AWS, Microsoft Azure, and Google Cloud can come in handy in collecting, maintaining, and processing large sums of data in an agile, secure, and effective way.

Practice, Get Experience, or Take an Internship

Once you have developed the preliminary skillset and are quite confident, the next step involves scenario-based learning, where you apply your skills in a professional or real-world setting.

This can be achieved through a data camp or an internship, where you can directly collaborate and learn from industry leaders how to perfect your skills and take them to the next level.

Final Thoughts

The influence of data scientists is gradually expanding into all sectors, disciplines, and industries. To advance your expertise within your domain or embark on a career in data science, which offers lucrative salaries and significant impact, we recommend getting started on a data science course today.