If you’re looking to learn data science then you might be wondering whether or not you will need to learn machine learning to do data science.
This post will show you whether or not you do, how machine learning is used in data science and the other things that you will need to learn to do data science.
So, is machine learning required for data science? Machine learning is a key part of the data science process. If you want to be able to do data science effectively it will be necessary for you to learn how the different machine learning algorithms work and how to make use of them.
Many people actually consider data science and machine learning to be the same thing. However, the truth is that machine learning is just one aspect of the data science process and there is much more that goes into being a data scientist.
How machine learning is used in data science
Data science is the process of organizing, analyzing and helping people to make decisions based on large amounts of data.
Machine learning is where algorithms are able to learn from data and predict future outcomes based on that data without being explicitly told how to do so.
Being able to use data to make predictions is a key part of data science but it is not all that data science is.
The data science life cycle involves:
- Acquiring and storing data
- Asking about how that data might be useful
- Cleaning the data
- Doing exploratory data analysis which is where you summarize the main characteristics of the data
- Choosing and applying machine learning models to the data
- Making sense of the results of the models and how accurate they are
- Making decisions based on the results of the ML models
Machine learning is just one piece of the data science puzzle. To use machine learning, in a data science context, you’ll need to know how the ML algorithms work and how to make use of them.
How machine learning engineers are different to data scientists
You might have seen job postings for positions as machine learning engineers. The role of a machine learning engineer is to take the models developed by data scientists and to make them work at scale with software that can be deployed.
Machine learning engineers need to be able to have a good understanding of how to make the machine learning models usable at scale. This is why having a good understanding of software engineering is a key part of being a machine learning engineer.
Skills that you will need to learn to do data science
In addition to learning machine learning, there are a number of other skills that you will need to learn in order to become a data scientist.
The skills that you will need to learn can include SQL, programming (commonly in Python or R), calculus, linear algebra, statistics, probability, data analytics and an understanding of the different machine learning algorithms.
You’ll also typically need knowledge of the industry that you will be doing data science in. This is because it will be necessary for you to be able to ask the right questions about the data and to be able to select the right features to use in order to implement the machine learning models.
If you are starting from scratch then there are many free online resources that you can use to learn the required material.
If you’re looking to learn to programme then the course Programming in Python by MIT is highly recommended. It teaches you computer science fundamentals in the Python programming language both of which will be important for you to learn if you want to learn data science. After taking that course I would recommend the follow-up course taught by MIT Introduction to Computational Thinking and Data Science.
MIT also uses Edx.org to teach courses on calculus, probability and deep learning and machine learning. You can also find a course on linear algebra taught by The University of Texas at Austin. Linear algebra is a very important subject to understand if you want to be a data scientist.
Additionally, a highly recommended course to learn machine learning principles with is the machine learning course taught by Andrew Ng on Coursera.
Background of a data scientist
Typically companies will be looking for people with a masters degree or a Phd. However, it is, sometimes, possible to get an entry-level position with a bachelors degree and the ability to show that you have relevant experience. If you haven’t previously worked in a data science or data analytics type position then two ways to get the experience would be in the form of internships and data science projects.
Typical degrees that data scientists will have will normally be quantitative in nature and common degrees would include computer science, statistics or mathematics.
Can I learn machine learning and not data science?
In order to be able to apply machine learning models to data it will be necessary for you to be able to prepare the data so that the machine learning models will work with the data.
Preparing data for machine learning models is a large part of what goes into data science so regardless of whether or not you want to be a data scientist you will need to learn much of what goes into data science in order to use machine learning.
Can I start learning machine learning before data science?
As mentioned above, in order to learn machine learning, it is necessary for you to understand how to prepare the data for machine learning models. This means that you will have to learn some aspects of data science before you are able to make use of the different machine learning models.
It would also be necessary for you to get a good grasp of exploratory data analysis beforehand so that you can make full use of the different machine learning models.
With that being said, there is nothing stopping you from learning what the different machine learning models are and the mathematics that goes into them. I would highly recommend the machine learning course taught by Andrew Ng on Coursera to do this.