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Data Science vs Machine Learning: The Key Differences

If you’re exploring a career in data science, there are probably a few terms or phrases you have come across that are unclear. Most likely, one of those is machine learning, or machine learning with Python. 

Often, machine learning might come off like it’s the same thing as data science. But, that’s not exactly the case. So, what are the key differences between data science vs. marching learning?

Let’s take a dive into the world of one of the most in-demand and fastest-growing careers—data science—to figure out the answer.

What is Data Science?

A relatively new career path, data science is an evolving field that allows companies to not just capture and organize their data, but analyze that data and make long-term decisions based on it.

How do data scientists help that process? Previous iterations of the job called for much more rote skills like mining, organizing, and analyzing data. Today, data scientists are expected to visualize and present data, as well as use coding to produce predictions and models that help guide business decisions.

That leads to companies, from start-ups to conglomerates, relying on data scientists and artificial intelligence (AI) to make decisions that change the trajectory of a business. In short, data scientists are important to the future of business.

What is Machine Learning?

For decades now, the next wave of technology that’s promised to change the way humans live and interact is AI. That reality is creeping closer and closer. A big reason for that is machine learning.

To create AI, a computer or program needs to have as much data as possible to predict things accurately. Hence, machine learning. 

In essence, machine learning is the process of plugging internal data into algorithms to allow a program to make predictions and classifications to discover insights into a business’s data and performance. In most cases, machine learning is used to make predictions about key growth metrics for companies.

The term was coined back in the early 1960s, but we are truly entering the golden age of machine learning. That means, it’s an extremely valuable skill to have in today’s tech world.

How are Data Science vs. Machine Learning Different?

The simplest lens to view the two through is thinking of machine learning as just an aspect of data science. In this case, though, machine learning happens to be the most important aspect of being a data scientist in today’s workplace.

Like mentioned above, in previous years, a data scientist was expected to extract, manage, and analyze a company’s data. It’s not until recently did the expectations of data science change.

While not always the case, many companies are looking for data scientists that can do it all. That means extracting and organizing data, as well as using programming language to create algorithms or prediction models which in turn offer predictions for various different types of business models. 

Do you need to Know Machine Learning to be a Data Scientist?

For now, no, you can become or be a data scientist without needing to know machine learning, AI, or predictive analytics. While the answer is increasingly changing, there are still companies, many of them larger corporations, who have traditional data scientists who don’t dabble in machine learning and focus simply on extracting and organizing data.

That allows machine learning specialists to build algorithms, organize the results, and present them to executive teams or other stakeholders.

Having said that, if you’re learning data science today, you should do everything you can to add machine learning to your skillset.

The future of data science is machine learning. When learning to become a data scientist, start with the building blocks of data mining and organizing. Once you’ve mastered those, dive into the world of AI and machine learning. As the data science career grows and changes, those who do not know machine learning will be left behind,

What is Machine Learning With Python?

If you’re looking for information about machine learning, it’s likely you’ve come across the term machine learning with Python. Machine learning with Python is simply the most popular way to code algorithms for machine learning.

Python is one of the fastest growing, most in-demand, and most popular programming languages at the moment.

All data scientists looking to master machine learning should at least have a basic understanding of Python. Fortunately, Coding Dojo offers a free Intro to Python workshop so you can get to grips with the language before attempting to master machine learning.

Data Science Jobs

One of the fastest growing careers in the country, data science is a great option for numbers-minded individuals looking for a challenging and rewarding career. According to Mint, data science is the 11th fastest growing job in the country and has a projected 31 percent growth rate.

Data Science Salary

The current nationwide median salary for a data scientist is $98,000.

On top of that, there are several other jobs that involve the same skills that are growing even quicker. According to Mint and the U.S. Bureau of Labor Statistics, statisticians are one of the most-in-demand roles across the country. The projected growth rate is 35 percent and the median salary is $95,000.

Machine Learning Jobs

Now that you’ve learned that machine learning can be classified as its own job, let’s see what the machine learning job market looks like currently. 

The most common job title for someone who is focused on machine learning is machine learning engineer. These jobs aren’t as plentiful as data science jobs as they’re more specialized. That means they’ll be more difficult to find, but also more lucrative.

As you may have figured out, a machine learning engineer spends their time designing and creating AI algorithms that will learn from the data and make predictions based on different models and ideas. A machine learning engineer works with data scientists who do the data mining and organizing, allowing the engineer to focus on creating the best algorithms possible.

Machine Learning Engineer Salary

According to Indeed, machine learning engineers make on average $111,000 per year across the U.S. That’s one of the highest average salaries found across the tech space. Many companies with machine learning engineers on staff, those like eBay and Snap, Inc., have a reported average salary of well over $300,000 and $200,000, respectively.

That certainly sounds enticing, but it’s important to note that machine learning engineers have generally been working in the data science sphere for years and have graduated to those roles due to experience and skill. It’s very likely if you’re just starting out in data science you won’t become a machine learning engineer right away.

However, it’s just another example of a route you can go in this growing tech space and that you could end up in a role making decisions at a large company and being compensated well for it.

Data Science Machine Learning Bootcamp

Like what you read? 

One of the most enticing prospects about becoming a data scientist or machine learning engineer is that you don’t need a degree. Like other tech-based roles, companies simply want to know that you can do the work, not so much how you learned the work. 

That’s where a data science bootcamp comes into play.

Coding Dojo offers an online, part-time data science course where students will spend 14 weeks going from data science novice to pro. Using machine learning with Python, you’ll learn the basics of coding, data organizing and modeling, machine learning, and so much more. 

After graduation, Coding Dojo provides free career services so you can beef up your resume, get help with interviews, and build a portfolio. Our job placement rate is currently 83 percent within 180 days of graduation.

Interested in learning data science? Apply now!