As technology continues to improve, more and more people are learning about data science and machine learning. Both are relatively new technologies and both can help companies to be more efficient and competitive in the market. However, the two are different in their approach and function.
Data science involves tracking and analyzing data from customers, users, or the company’s internal operations. Machine learning can do these things as well, but it requires special programming to automate the process. In summary, data science is more manual and involves human analysis and interaction. That being said, both tools are becoming important and are being widely adopted in business.
While Data Science and Machine Learning share some characteristics, they serve unique purposes for companies. The upcoming sections of this article will provide a better understanding of the differences between both. Keeping these differences in mind will provide you a better perspective in understanding the utility of data science and machine learning.
Important Sidenote: We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is Data Science?
Data science is a skilled profession that involves data mining and data analysis. Data Science professionals make sense of vast amounts of structured and unstructured data and expose hidden solutions to complicated business problems.
While any business owner or manager can organize some data, data science professionals are specialists. They can manage and work with large amounts of data. Data science involves communication, collection, and organization of data.
A data scientist should be able to look at the data they have and interpret it. That way, they can advise their employer on potential solutions to targeted business problems with insights extracted from the analyzed data. For example, maybe an eCommerce company may want to understand how users interact with its website and how much time does an average customer browse through the website before making a purchase.
The data science team can take that information and tell the company how users are interacting with the site. If it is taking most customers a considerable time before making a purchase, the product managers may want to study the user experience and have the web designers work on making the website more user-friendly or to encourage users to make a purchase sooner.
What Is Machine Learning?
Machine learning also uses data, but it combines that with artificial intelligence (AI). It focuses on creating programs that can access and analyze data automatically. You don’t need a team of data scientists to interpret the information, but you will need engineers and programmers to set up the system.
You can use a few different types of machine learning algorithms. Supervised algorithms use past knowledge and experiences to predict future outcomes. On the other hand, unsupervised algorithms explore the data to help assume a function’s structure.
Reinforcement algorithms produce an action and discover a positive or negative result. These algorithms try new things, and feedback reinforces the results and whether the outcome is good or bad.
Lastly, there are semi-supervised algorithms, which combine supervised and unsupervised algorithms. They use labeled and unlabeled data to improve learning accuracy. All of these algorithms can help with machine learning, so you can use them for different tasks.
How Data Science and Machine Learning Work?
When comparing data science and machine learning, it’s important to look at how each system works. While both utilize data, they focus on different things. The process for data mining and data analysis by data scientists is different from machine learning.
These systems both work with a lot of data, but data science uses people that specialize in managing information. Machine learning requires people to specialize in programming so that AI can do data management.
Both systems can be useful to companies, so you shouldn’t just use one or the other. But consider the big picture functions and systems of each. Then, you can understand how both of them function on a basic level.
Data scientists work with data on multiple levels. At a minimum, a data scientist should be able to analyze and compare different points. Professionals can use statistics and other tools to measure and compare data.
The first thing data scientists do is collect and organize data. Data could be about anything from company sales to customer acquisition to marketing results. Depending on the data in question, scientists will need to organize the information so that they can better analyze it.
As a data scientist analyzes the information, they can determine how to use those results. In the case of a successful marketing campaign, they can tell the marketing team to continue that work. On the other hand, they can ask marketers to adjust their work if the data doesn’t show much success.
Over time, companies can use these results to note trends. Then, those trends can help predict future successes, so the company can make better decisions to improve business growth.
At a basic level, machine learning requires computer scientists to program the system to act as data scientists. The people can use coding languages to tell the computer what to do, and that includes working with data.
As people, we know how to learn and grow from our experiences. However, machines by default don’t know how to do that. This is what machine learning teaches them.
Depending on what you want AI to do, you can choose from the different machine learning algorithms. By programming the best algorithm for the job, you can watch the computer learn and work with data.
You can use machine learning in a variety of situations. It can help analyze customer data for an eCommerce company, and can also help with optimizing operations in a ton of industries, such as – health care, finance, oil & gas, etc.
Now that we have covered what Data Science and Machine Learning can do for a company, let us now dive deep into some smaller details. These details can be important to consider depending on individual needs and objectives behind using these tools.
While you should understand the big picture differences between data science and machine learning, you should also consider the details. The details are where the two systems start to diverge.
If you consider the smaller pieces of each, you can decide what to use and when. For example, you may want to opt for machine learning if you have a smaller company and can’t afford to hire people long-term.
On the other hand, if you’re looking to decide one process, the details might help you make your decision. Here are the detailed ways that data science and machine learning work.
While data scientists work with data, they need to verify the information. That can involve cleaning the data and getting rid of inaccurate or problematic pieces of data. Data cleansing could mean simple things like correcting any misspellings or changing the data format of various columns. The primary objective of this stage is to prepare your dataset for analysis.
Once you cleanse and validate your data, you can compare it using various models and algorithms. These algorithms differ from those of machine learning in that you are still managing the data.
After you finish analyzing the information, you can use your results to come up with a plan. You can find problems that your company is experiencing, and you can use past results to devise a solution.
Then, you can take that data and use it as evidence when sharing your solution with managers or business owners. If you can show that a particular marketing strategy or more capital can help grow the business, stakeholders might be more interested in the change.
You can create a visual graph or use another tool to get your findings and results across. That way, you can make sure the owners understand how your proposed plan will work and improve the business.
If you decide to give machine learning a try, you have to consider the different algorithms and how they work. Each works well with certain types of data, so you can combine them where needed. Here is how you can use various machine learning algorithms.
Unsupervised algorithms use a technique called clustering. If you don’t have much data to work with, this can be a good option. It requires a lot of tests to find what works. Good ways to use it include market research and gene sequence analysis.
Supervised algorithms have two main techniques. Regression techniques are great for working with continuous responses, such as temperature changes. They’re great for working in a data range and with real numbers.
Classification techniques are good for deciding the validity of something. You can use them to determine if a message is real or fake. Supervised algorithms are excellent for categorizing data, and you can use this information when running future tests.
Why Data Science and Machine Learning Matter?
Both data science and machine learning are becoming more important to companies and industries. As more people start using products and services, companies will need to manage more data.
Whether you prefer to have people work with data or a machine, this technology will continue to be important. Even if you have a small business or are a sole proprietor, you can utilize data science and machine learning to grow your company.
Both these types of data analysis tools are important, and in the following sections, we will discuss why each type matters.
Data science is essential for tracking data manually. While machine learning can be useful, you may need to track and manage data without technical assistance. If you’re new to the business, you may not have records to teach AI to track your data for you.
So you can start by hiring a data scientist to do the work. The data scientist can track your records for you as you get your company going. They can get rid of incorrect pieces of data, and you can use that information later if you decide to implement machine learning.
Data science has a cycle that is always going. If you’re not collecting data, you’re maintaining or processing it. And if you’re done with that step, you’re analyzing or presenting the information to your company. Then, the cycle repeats with any new data that comes in.
Data science is one of the best jobs, and its demand is expected to grow. So, whether you own a business or want a good job, you should look into data science. It’s a great way to start working with data, and you don’t need the infrastructure to support machine learning.
Machine learning is important for a variety of industries. You can use the process to process images and detect faces, which can be useful for finding individuals or allowing a face login on a phone or computer.
Similarly, machine learning can learn to recognize voices, which can also come in handy. If you’re building a voice assistant, you can use machine learning to improve the technology.
Companies in the manufacturing industry can use machine learning to help with predictive maintenance. Energy companies can use it for load forecasting. Medical professionals can use technology to detect health problems and find treatments.
You can also use machine learning for complex things. Fraud detection and constantly changing data are two other fantastic uses for machine learning. Financial companies can also use it for credit scores and other information.
What Professionals Does Data Science and Machine Learning Involve?
Another difference to consider is who works with data science or machine learning. While both technologies require people, their roles are different. People can help manage the systems, but data science involves more hands-on work.
However, machine learning requires some human direction. Without any human input, the AI may not learn the systems you want it to learn. At the start, human coding is essential.
Keep these differences in mind when deciding between the two. If you don’t have a huge team, you may want to choose one option over the other.
Many people work with data science, including data scientists. However, other related roles include data analysts and data engineers. In some cases, you could hire someone to fulfill all of these positions in one. But hiring specialists is useful if you want to ensure the data you receive is accurate and actionable.
A data scientist focuses on the problems your business has, and they will find solutions. You can use this role to mine and clean data. Meanwhile, a data analyst is a mix of a data scientist and a business analyst.
These professionals can help manage your data, and they will focus on the bigger strategy of your company. They work with more technical pieces of data, and they make the data easy to understand.
Lastly, data engineers work with data that is always changing. They can optimize data pipelines, and they will transfer the information to data scientists for analysis. While someone can do all of these roles, you can also separate them between people on your data science team.
With machine learning, the AI will act as your data scientist. However, you will need someone or a team of people to set it up. In that case, you will need computer programmers and engineers. The programmers should be able to input information to help the AI learn.
Over time, you may not need as much human help. The machine will eventually learn the systems you want it to use to carry out tasks. However, if you want to create multiple machine learning systems or use different algorithms, you’ll need more professionals.
It can also help to have computer programmers available to manage the system. If something goes wrong or the machine stops working, you can get a programmer to fix it. Then, you can get the machine back on track.
Your machine learning team can be part of your programming or engineering team, or it can stand on its own. If you don’t need regular help for your AI, you can contract a programmer or two when you need assistance. However, if you plan to do a lot with machine learning, you may want to hire someone full-time.
Required Skills and Training for Data Science and Machine Learning
If you’re looking to work in technology or hire someone for the job, you should consider skills and training. While data science and machine learning both fall under technology, they require different skills.
Both fields typically require a degree or relevant experience. You may also want a certificate if your degree isn’t directly related to data science or machine learning. If you’re hiring someone to work in these fields, you should consider their experience.
A willingness to learn is essential for a technology professional, but each path has unique requirements. Here are some skills and training that are essential for data scientists and machine learning engineers to possess.
To be a data scientist, you should have a degree in a field such as computer science, mathematics, or statistics. You can also get into the field with a marketing degree or management information systems training.
If you don’t have a degree in those areas, you may be able to substitute it with on-the-job training. Perhaps you got a degree in communications and worked for a marketing company. In that case, you can use your marketing experience to work in data science projects that solve marketing problems.
Since data scientists need to come up with solutions to business problems, it helps to have a diverse team. Someone with marketing experience can help come up with ideas for marketing campaigns based on the data, and similarly a supply chain professional can advise on the company’s operations.
To be successful in a data science role, you also need to be good at communicating and making complex topics easy to understand. Then, when you give a present your findings on a complex issue and propose solutions, you can get your message across easily. It also helps to be naturally curious since you’ll need to learn a lot about data and analysis.
Data science experience can help when it comes to being a machine learning engineer. You can also get a degree in mathematics or computer programming. A physics or statistics degree can also prepare you for a machine learning career.
A bachelor’s degree can help you get started in the technology field. However, you will probably need to get a master’s or Ph.D. to become a machine learning engineer. Graduate studies in software engineering or computer science can give you more experience and credentials.
If you want to hire someone for a machine learning role, you should also consider these requirements. Machine learning is very specialized, so it helps to have an expert. It’s also a relatively new field, so it can be hard to find people to fill the position.
Machine learning engineers should be able to solve problems. They also need some coding and programming knowledge. And like data science, machine learning requires professionals to be curious and to continuously learn new things.
As AI gets smarter and more refined, engineers will need to adapt and work with the new updates. That way, you can make better use of the technology as it evolves.
How are Data Science and Machine Learning Used?
Another aspect in which data science and machine learning differ is how you use them. Data science is great for a lot of things in business and technology. Machine learning can also help businesses, but it requires a computer program to work.
You can use both these types of technology in multiple ways. But each works a little differently, so one might be better for some things.
Here are a few ways you can use data science and machine learning and how they’re unique from the other.
Data science is a broader domain when compared to machine learning. Because machine learning requires some data science experience, you can do more as a general data scientist. A data scientist can work towards tracking, managing, and analyzing almost any type of data you a company records.
Additionally, if you want to track data manually, data science is the better choice. You or an employee can analyze sets of data from customers or the market in general. A data scientist can clean and validate the data and streamline the results.
Data science involves asking questions about the problems that your business is facing. The questions could focus on financial records and sales, or they could be targeted towards a specific marketing campaign. It would completely depend on the business goals you have, and you can alter your strategies accordingly.
Furthermore, if you need to shift your focus or goals, you can also ask your data science team to focus on the data affecting the new goal. That way, you can reach a solution more quickly. You can use the data to make decisions to improve your business, and you can use it to decide what goals to set.
You can use machine learning to help reach business goals. But switching the focus can require reworking the AI so that it works with the new data. Unless you have a huge team of machine learning engineers, you may want to start with data science.
If you just have one thing you want to use machine learning for, you won’t need a huge team. You can have someone code the system to get it started. Then, you can use machine learning to find patterns in the data.
Sometimes, it can be easy for people to miss patterns and other things. If you’re focused on one thing with the data, you can easily ignore something else that is important. Hence, machine learning is excellent when you’re working with a lot of data and multiple data variables.
Additionally, machine learning is also helpful if you have a lot of data, but you lack an equation or formula to make sense out of it. As the AI finds patterns, it can categorize your data for you. If you want to get more specific, you can combine machine learning with general data science to find the data and examples you need.
Future Growth of Data Science and Machine Learning
You should also compare data science and machine learning based on each one’s future growth and potential. Both technologies are becoming more popular, and they probably won’t go away any time soon.
However, each system’s growth will be different from the other. Hence, you should carefully consider how data science and machine learning might grow in the future.
Almost every business across all industries uses some sort of information. The information may be financial, or it could relate to customer behavior. However, information is key to running a business today and data science can help harness its power. Data scientists work with information in the form of data, and that data can translate to trends and other business information.
If a company wants to project its future revenue, it can use past revenue records. A data scientist can look at sales and income over a certain period. Then, they can compare that to the rate of growth and the date of the projection.
Data science can work well for short-term goals, but it’s fantastic over the long-term. It looks like data science is here to stay in a variety of businesses and industries.
While data science has first hit the technology industry predominantly, companies across all sectors can take advantage of data. Even a small online business can use website view analytics to grow their website. A larger company can use data to determine when to launch a product or where to set up a new store.
While it has taken a while for data science to become mainstream, machine learning is expected to grow faster in the next few years. Companies all over the world are adopting the technology, and the global machine learning market shows that.
The market was at $1.58 billion in 2017, and by 2024, it’s projected to reach $20.83 billion. In 7 years, the market would have grown by 13 times if that prediction is true.
Companies are also quickly implementing machine learning to solve specific problems. While data science can help with problem-solving in certain settings, machine learning algorithms can solve most data problems no matter how complex they are.
And in a world where automation is getting closer, it makes sense that solving data problems would also see some automation in the form of machine learning. While some companies may hesitate to use machine learning during these early years of technology adoption, there are many that are harnessing its power and helping it grow mainstream.
This quick growth will hopefully make machine learning more accessible in the future. Then, even more companies can use it without huge technology budgets.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Data science and machine learning have both become more popular in recent years. While they both involve tracking data, they work differently. If you want to understand the differences, you should consider everything from the big picture to the skills required to implement each system.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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