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Why Study Data Science? (Top 5 Reasons)

With the evolution of technology and the expansion of businesses globally, data science is a more relevant field than ever. Companies rely on data scientists’ information to make informed decisions about how to operate their companies.

If you’ve ever enjoyed mathematics, statistics, or working with data and information, data science could be an excellent career path for you. Now could be the perfect time to begin learning data science.

What is Data Science?

Data science is an interdisciplinary field where data scientists capture and work with data to make sense of otherwise unorganized and jumbled-up information. Data science involves a combination of scientific methods, algorithms, and systematic processes to unravel the raw, unstructured data they first receive.

Why is Data Science Important?

Without data science, companies of all sizes, especially large organizations, would have difficulty making informed decisions.

Data scientists extrapolate the data they collect to uncover trends in every area of the business. They help leaders and C-suite executives make decisions backed by data to continue growing their company and make the best decisions for their consumers.

Businesses are all about analytics and metrics. Without those, you don’t have a clear picture of the health of your business. Data science is essential for every business because of the value and insight it provides.

Why Study Data Science? 5 Top Reasons

1. Demand: Data Scientists are in High Demand

According to the Bureau of Labor Statistics (BLS), employment of Data Scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations.1 With the utter importance of this career, data scientists are highly sought after, making for a great potential career choice.

2. Growth: Data Science Careers Have High Earning Potential

According to the BLS, the median annual wage for data scientists was $100,910 in may 2021.1 A career in data science may not only be lucrative but can be fulfilling, rewarding, and challenging.

3. Job Security: Data Science is a Fast-Growing Field

This year, LinkedIn released its annual  jobs on the rise report—and Machine Learning Engineers came out on top at 4th place. While it didn’t rank as the #1 fastest-growing field, it certainly cropped up pretty quickly.

4. Opportunity: Data Science Has a Range of Potential Job Opportunities

According to BuiltIn, studying data science can give you access to a diverse range of job opportunities. Some of these employment options may include:

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Architect
  • Business Intelligence Analyst
  • Database Administrator
  • Machine Learning Engineer

5. Flexibility: Data Scientists are Needed in Various Sectors

Data scientists are in demand across a range of industries and sectors. The need will only grow as businesses integrate more data environments into their operations. 

Some of the popular industries for data science professionals may include:

  • Healthcare
  • Financial Industry
  • Manufacturing
  • Logistics
  • Retail Industry
  • Telecommunications
  • Automotive Industry

Data Science vs Data Analytics: What’s the Difference?

Data Science

Data science focuses mainly on raw, unstructured data. Generally, the job of a data scientist is to organize, store, and analyze data, creating modeling and uncovering trends based on the extracted data. They look to find meaningful connections between large amounts of data from an organization.

Data Analytics

Data analytics is a branch of data science. Data analysts dive deeper and look at more specific answers to questions brought forth by data scientists.

Top 10 Essential Skills for Data Science

Data Science Fundamentals

The fundamentals of data science are mathematics, computer science, and domain expertise. Coding Dojo offers a part-time data science bootcamp to teach you the foundations of data science (and beyond!), so you can begin your career path in this expansive field.

1. Data Modeling and Analytics

Data modeling requires the visualization and plotting of data to better understand the data you’re working with. It’s also imperative to know how to distill this information down in a way that can be shared with–and communicated to–important stakeholders of the business.

Analytics are an inherent part of data science, as well. Analytics involves defining a goal for discovery, collecting the proper data to reach this goal, prepping it for interpretation, and finally, plotting and presenting it.

2. Programming Knowledge

Data scientists need to know Python, and other languages such as SQL, JavaScript, Scala, and C are also helpful. Our data scientist bootcamp begins by teaching the foundations of Python that are needed to succeed as a data scientist.

3. Statistics and Probability

Learning more about probability as it relates to data science will help you better explore and understand data. Probability can also help you make further, more accurate estimates and analyses. Statistics are interwoven with probability, making these two skills go hand-in-hand.

4. Advanced Mathematics

Aside from statistics and probability, linear algebra and calculus are valuable advanced math skills to have, as they’ll make it easier to work with data.

Linear algebra techniques in data science allow you to transform and manipulate data, and calculus is often used in machine learning. Calculus techniques are also used in almost every data model, so understanding this math – which involves studying the rates of change – is integral.

5. Data Visualization

Arguably one of the most important technical skills of any Data Scientist, data visualization is all about translating and visually communicating data, usually through graphs or charts.

A Data Scientist needs to be able to easily share findings and data from their work to high-level decision-makers in the organization, so knowing how to plot data and create straightforward visuals is an exceptionally important skill.

6. Machine Learning

Machine learning helps data scientists make quality predictions. Understanding how to use machine learning will benefit an organization greatly, as it can adequately output more real estimates without human intervention–it’s all A.I.-based, giving you more time and freedom to work on other things.

7. Deep Learning

A type of machine learning, deep learning also uses A.I. to help develop statistics and predictive modeling, which are incredibly important for a data scientist.

8. Data Wrangling

Essentially, data wrangling is the process of cleaning up messy data sets. Data is complex and tangled in its rawest form, so data scientists must wrangle the data, making sure it’s easy to access and analyze.

9. Big Data Processing

Especially in large organizations, data scientists work with large data sets. They need to extrapolate raw data and distill large data sets into smaller ones that are easier to review and work with.

10. Database Management

A database holds a structured set of data. They are used to store, retrieve, access, and update data, allowing data scientists to keep their information secured in one place.

Learn Data Science at Coding Dojo

At Coding Dojo, we’re all about learning. Our bootcamps are specifically designed to help you prepare for a career path of your choice – including data science – in a short amount of time. They’re intensive but are designed to provide you with all the foundations and concepts you’ll need to know to make a career path out of your chosen field.

In the first four weeks alone, our data science bootcamp you’ll study foundations in Python, understanding and manipulating data, data modeling, machine learning, and statistics. After 16 weeks of part-time learning, you’ll be ready to enter the data science field.

Kickstart your data science career with Coding Dojo.

Data Science FAQ

Is Data Science a Good Career path?

Data scientists are sought after, especially in a world where businesses and tech are so prevalent. Data will always be relevant to businesses because it helps them make informed decisions that could impact their company significantly.

Where to Learn Data Science?

While there are short, free courses on things like Python, machine learning, and SQL, these are standalone courses and may try to cram hours’ worth of knowledge into a 30-minute video.

With the Coding Dojo data science bootcamp, our course material is completely interwoven, all directly related to data science, and is 100% relevant for beginning a career in data science.

How Long to Learn Data Science?

Our bootcamp is only 16 weeks long, meaning you could begin your new career path in only 4 months!

Is Data Science Hard to Learn?

While many data science concepts can be challenging to learn, our courses are beginner-friendly and are designed to teach you the basics of everything you need to know. As long as you come into it ready to learn, apply your knowledge, and really take the time to understand the concepts, you should be able to learn data science in no time.

1Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Data Scientists,
at (visited April 12, 2023). This data represents national figures and is not based on school-specific information. Conditions in your area may vary.

Coding Dojo cannot guarantee employment, salary, or career advancement. Not all programs are availalble to residents of all states. REQ1919909  4/2023