Data Science and Machine Learning: What You Need to Know

by | Apr 1, 2022

Data Science and Machine Learning: What You Need to Know

If you want to make your data more actionable, you need to understand the basic principles of data science and machine learning.

The good news is that it’s not so complicated. You only need to know the basics to get started. And once you do, you’ll be able to harness the power of these two technologies in your day-to-day work.

In this post, we’ll cover what data science is, how it differs from statistics, and the basic concepts you need to get started with data science and machine learning.

Data Science And Machine Learning

Both data science and machine learning are amongst the top web searches today. Whether it is because people want to find out more about the subjects, or they are interested in starting (or changing) their careers, they show no sign of slowing down. 

The growth of these two subjects has been exponential, especially over the past decade. This is because of data, which the world is very heavily dependent on. In fact, the amount of data that we produce grows by 2.5 quintillion bytes everyday. That is a lot of data. 

Inside this data, we can find important insights about how to get better results in a reduced amount of time, be it manufacturing, medicine, or education.It is due to this reason that the demand for data scientists and machine learning engineers is always on the rise. 

What Is The Difference Between Data Science And Machine Learning?

Data science 

Data science is a vast field, and there are many specialties that can be followed within this field such as finance, health and government data science just to name a few. No matter which path of data science you go down, there are some core principles and skills that every data scientist needs to have. Here are the top skills that are required to be a data scientist:

  • Programming – Programming provides us a way to communicate with machines. Many data scientists choose the type of programming language they wish to specialise in such as Python, R, or Julia to name a few. 
  • Statistics and probability – statistics is a crucial skill for data scientists. This knowledge can start with descriptive statistics like mean, median, mode, variance, the standard deviation. Then come the various probability distributions, sample and population, CLT,  skewness and kurtosis, inferential statistics, hypothesis testing, and confidence intervals. 
  • Data visualisation – This is a skill required to present the findings of data research and consists of presenting data in a clear and understandable way. To start with you must be familiar with plots like Histogram, Bar charts, pie charts, and then move on to advanced charts like waterfall charts, thermometer charts. 

Machine learning 

Machine learning is a branch of computer science that studies how to enable computers to solve problems without being explicitly programmed to solve them step-by-step. It is essentially using patterns hidden inside of data to build predictive models for the future.

They work with large numbers of data sets, and so having the skills and experience to solve different data science problems that are based on predictions of major organisational outcomes is very beneficial. 

To build up machine learning skills, it is best to start small with simple linear and regression models. Once you feel that these skills have been grasped and the concepts have been well understood, you can gradually increase the complexity and move onto K-means clustering and Classification and regression trees (CART) models. Other skills required to be a machine learning engineer include: 

  • Domain knowledge –  to be able to design self-running software and optimise solutions used by businesses and customers, machine learning engineers need to understand both the needs of the business and the kinds of problems that their designs are solving. 
  • Communication skills – it is common practice for machine learning engineers to work with data scientists and analysts, software engineers, research scientists, marketing teams, and product teams. That means the ability to clearly communicate to stakeholders the project goals, timelines, and expectations is a very important part of the job.
  • Problem-solving skills – being able to problem solve is important for both data scientists and software engineers and essential for machine learning engineers. Machine learning focuses on solving real-time challenges, so the ability to think critically and creatively about issues that arise and develop solutions is a foundational skill.
Data Science and Machine Learning: What You Need to Know is displayed on a bright background next to a data scientist.

The Intersection Of Machine Learning And Data Science

Machine learning and data science are very similar fields, and there is quite a bit of overlap between the two. Both positions perform some form of engineering, whether that be a data scientist querying a database using SQL or the machine learning engineer using SQL to insert the suggestions or predictions from the model back into a newly labelled column/field.

Also, both machine learning and data science require data. They use maths, statistics, and algorithms to extract value from it. Data scientists and machine learning engineers require companies to have clear business goals specified beforehand and can result in process optimization, revenue increase, or cost reduction.

Difference Between Data Science And Machine Learning

Data science is the field that studies data and how to extract meaning from it while machine learning focuses on tools and techniques for building models that can learn by themselves by using data.

There are some other differences between the two respective fields that sets them apart from each other. For example, a data scientist focuses on statistics and algorithms whereas a machine learning engineer focuses more on software engineering and programming. Also, data science involves data acquisition, data cleaning, data investigation and machine learning includes more supervised, unsupervised, semi-supervised learning. 

How To Choose Between Data Science And Machine Learning?

If you are interred in taking your career to the next level, it can be difficult to decide whether to specialise in data science or machine learning. The good news is that they are very similar and many of the key skills required do overlap between the two respective fields. 

Data science is needed wherever there is big data. As more and more industries begin to collect data on customers and products, the need for data scientists will continue to grow. As a data scientist you can expect to generate and lead a wide range of key roles that help to solve problems faced by businesses worldwide.

As a machine learning engineer, working in this branch of artificial intelligence, you’ll be responsible for creating programmes and algorithms that enable machines to take actions without being directed. Machine learning engineers are in very high demand across a variety of sectors including technological, medical, engineering, or internet security.

Ella Crawford
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