When you think of data science, what comes to mind?
For many people, when that question is asked, machine learning is the first thing they think about.
However, data science is a much broader field than machine learning.
This blog post will discuss the different aspects of data science and what they entail.
We will also explore the ideas behind “data science without machine learning” and discuss if it’s possible and, if so, how to do it.
Data science vs. Machine Learning
Data science and machine learning are all the rage now, as data science was voted the hottest job of the 21st century.
What many seemingly get wrong is assuming that these terms are interchangeable, but they refer to two different things.
Data science is a huge field that encompasses everything “data.”
This includes simple data collection and cleaning to much more sophisticated topics like statistical analysis and mathematical modeling.
On the other hand, machine learning is a subfield of artificial intelligence that deals with training algorithms on historical data to allow models to learn rules and trends from data to improve their performance.
And remember, artificial intelligence and machine learning are not the same things.
What many confuse is that machine learning is also a subfield of data science.
When machine learning engineers (or data scientists) want to create accurate models, they leverage insights from historical data.
To get this data into a usable form, many need to utilize techniques taught within data science to ensure their data can be used for modeling.
So while data science is concerned with understanding data, machine learning is concerned with using data to improve predictions.
But the two can work together to create some incredible systems.
Does Data Science Require Machine Learning
With the amount of data produced daily, companies need people who can make sense of it and help find the information businesses need to make the correct decisions.
I know many people think that data science requires machine learning, and while machine learning is a part of data science, it’s not the only thing (or the biggest).
A large part of data science is simply exploring data, ensuring it’s accurate, and providing analytics and visualizations for real-time tracking.
Dashboarding is also a huge part of data science, allowing users to see the information they need at a glance.
Personally, in my data science career, I’ve spent about 60-70% of my time doing the above, and many clients I’ve worked with have found the most value in analytical-based dashboards.
So if you’re interested in data science but don’t know anything about machine learning, don’t give up!
It’s only a tiny piece of the pie.
Can I Learn Machine Learning Without Data Science
Machine learning is a branch of artificial intelligence that is all about teaching computers how to learn from data.
But to learn from data, that data needs to be in a format that both the computer and the model can understand.
That’s where data science comes in.
Data science is all about sourcing, cleaning, and understanding data and getting it into a usable form.
And since most machine learning models rely on data science techniques to get their input, if your data isn’t always clean and perfect, you will need to know some data science to use machine learning correctly.
So if you’re planning on studying machine learning, it might be a good idea to brush up on your data science techniques.
Can I Learn Data Science Without Machine Learning?
Data science is a broad field that encompasses various disciplines, from statistics and computer programming to machine learning and artificial intelligence.
While it probably seems like everyone knows machine learning, most jobs within data science do not require machine learning experience.
These jobs are usually analyst-based and are seen throughout the finance and technology domains.
I’ve worked on plenty of data science and machine learning teams, and for every data scientist or machine learning engineer, we had 5-6 analysts working with us.
While you can get a job in data science without machine learning, if you want a job as a data scientist but don’t know machine learning, you will have a tough time.
It is becoming increasingly important for data scientists to be familiar with this cutting-edge technology.
Machine learning is a powerful tool that can be used to identify patterns and make predictions, and it is already being used in basically every single industry.
However, because machine learning is still in its early stages of development, there is a limited number of qualified people to work with it.
As a result, data scientists that can build production-grade systems are in high demand, and these are the jobs that are making the headlines whenever you hear “data science.”
Data Analyst vs. Data Scientist vs. Data Analytics
Data Analytics is about decision-making. These decisions can be derived in many different ways.
Analytical systems can be built with simple formulas or something more in-depth, like machine learning systems.
Both Data Scientists and Data Analysts perform Data Analytics every single day.
While data analytics is the heart and soul of both data analyst and data scientist jobs, there are some slight differences between the two.
Usually, this comes down to the difference between data analysts and data scientists regarding coding ability.
Data scientists will have coding abilities equivalent to software engineers, while most analysts know little or no code.
This difference is because data scientists are responsible for developing algorithms and models, while analysts primarily use existing tools to examine data.
As a result, coding ability is essential for data scientists but less important for analysts.
However, both roles require strong analytical and problem-solving skills.
Do I Need To Learn Machine Learning To Become a Data Scientist
To become a data scientist, you’ll need to learn machine learning.
Technically, the job doesn’t need coding, but most companies hiring data scientists will want to explore machine learning opportunities.
Machine learning is a fast-growing field, and data scientists who know how to use machine learning algorithms are in high demand.
If you’re interested in becoming a data scientist, start by learning the basics of data, then slowly move into some of the more straightforward ideas of machine learning.
Some many online courses and resources that can help you get started, and we have some articles here that can help you get started.
Once you have a solid understanding of the concepts, you can start applying them to real-world datasets.
With practice, you’ll be able to use machine learning algorithms to solve complex problems.
And with the correct skill set, you’ll be poised for a successful career as a data scientist.
Do Data Scientists Use Deep Learning?
Just like machine learning, data scientists do sometimes use deep learning. While most of the steps in the data mining pipeline are understanding and providing insights from data (usually data visualizations) – pipelines progress to more other complex models like deep learning models.
Other Articles In Our “Without” Series
Here at Enjoymachinelearning.com, we’re exploring how things are interconnected. We have other articles in our “Without” series where we explore the technical differences between some of the common jargon within the data science and machine learning realm.
A few more of those articles are here:
- Artificial Intelligence Without Machine Learning
- Face Recognition Without Machine Learning
- Can You Learn Deep Learning Without Machine Learning
- Operationalization In Machine Learning Production [Why It’s Hard] - April 27, 2023
- Lasso Regression vs PCA [Use This Trick To Pick Right!!] - April 24, 2023
- Is SVG a Machine Learning Algorithm Or Not? [Lets Put This To Rest] - April 6, 2023