If you’ve been keeping up with technological advancements, you’ve probably heard of the terms “Data Science” and “Machine Learning.” So how can you choose among them when they are some of the most cutting-edge technology in the modern world, what is now referred to as the Fourth Industrial Revolution? For those unaware, the fourth stage of the Industrial Revolution marks a transition from steam power to electric power to electronic and automation in the three preceding sequential phases.

Both data science and machine learning are utilized equally in all relevant fields. In the world of technology, they are both among the most commonly used expressions. Therefore, it shouldn’t be surprising that choosing between Data Science and Machine Learning to prioritize first is one issue plaguing people seeking careers in technology. Many factors should be counted when determining whether to pursue a career in machine learning or data science. Although the two professions are closely connected, there are several significant contrasts between them as well.

What is Machine Learning?

The process of employing algorithms to gather data, learn from it, and then predict future trends for a topic is known as machine learning. Based on observed data, traditional machine learning software uses statistical and predictive analysis to find patterns and uncover hidden insights.

Facebook is an excellent illustration of machine learning in action. Facebook’s machine learning algorithms collect users’ behavioral data on a social media platform. The system anticipates interests based on prior activity and suggests articles and notifications for the news stream. Like Netflix, machine learning is at work when it offers movies or items based on previous usage.

What is Data Science?

You probably asked yourself, “What is Data Science?” Data systems and processes are the subjects of the large field of research known as “data science,” which aims to preserve data collections and derive meaning from them. To make meaning of seemingly random data clusters, data scientists utilize a variety of instruments, programs, theories, and algorithms. Monitoring and preserving this data is challenging since practically all enterprises globally produce exponential volumes of data. Data science focuses on data modeling and warehousing to keep track of the always-expanding data collection. Data science applications extract information that is then utilized to direct company operations and accomplish organizational objectives.

You may select various postgraduate programs and Data Science courses online. In addition, through online mentoring sessions and committed career assistance, get knowledge from subject-matter experts.

Skills needed for Data Science

Data scientists are knowledgeable individuals who can swiftly change jobs at any stage of a project’s life cycle. They can operate equally well with AI and machine learning. Data scientists need to be skilled in machine learning for the following purposes:

• Programming languages: R, Python, SQL, SAS, MATLAB, and STATA
• Cleaning, manipulating, and exploring data is known as “data wrangling.”
• Data visualization is the process of showing data through graphs and charts.
• Conducting statistical studies of information is known as data analysis.
• Building algorithms that learn from information is known as machine learning.

Skills needed for Machine Learning

The abilities listed below will help you launch a successful career in the quickly expanding field of machine learning:

• Knowledge of the foundations of computers
• Comprehensive programming expertise
• Understanding of statistics and probability
• Skills in data modeling and evaluation

So how do ML & Data Science vary from one another?

Data science studies how to analyze data to get knowledge and inform decisions. It includes a variety of techniques, tools, and practices. Since “data science” is broad, actual data scientists must have diverse abilities, including programming, math/statistics, and domain expertise in the application area they seek. Strong programming abilities, an understanding of arithmetic and statistics, and significant domain expertise in the issue they are addressing are necessary for successful data scientists. For an organization, data scientists carry out a variety of tasks, such as data gathering and processing, analytics modeling and machine learning, and data visualization. In addition, data scientists tackle issues in various industries, including the social sciences, agriculture, and consumer products. As a result, data scientists have a wide range of employment opportunities in business, government, and non-profit settings.

The capacity of computer software to “learn” or enhance performance through examples rather than set rules is typically referred to as “machine learning.” Machine learning is one of the most important technologies used by data scientists to evaluate and understand data. Additionally, software developers employ data science methods and tools to prepare data for use in machine learning.

While some firms have established specialized ML Engineer positions, others rely on software engineers or data science teams to build ML models. This role in an organization calls for a combination of solid mathematical grounding, comprehension of the theory of machine learning and its algorithms, and a good command of programming to implement models in code, whether one is a dedicated ML Engineer or a software developer tasked with doing so. ML engineers are most frequently found in web/tech firms and industry-specific software companies, while there is a demand for them in every area.

Difference between Data Science & Machine Learning

Data Science	Machine Learning
Data science aids in generating insights from data that address the complexities of the actual world.	By recognizing patterns in previous data, machine learning aids in precisely predicting or categorizing outcomes for new data points.
A preferred set of skillsets: Domain knowledge – powerful SQL NoSQL systems, ETL and data profiling, standard reporting, and visualization.	A preferred set of skillsets: Python/R programming, solid math skills, data manipulation, and model-specific SQL knowledge are all required. Visualization
Massive data handling is desired by horizontally scalable systems.	For computationally expensive vector calculations, GPUs are preferable.
A set of tools for working with unstructured raw data.	The mathematical principles and algorithms that underlie them have a significant amount of complexity.
The three most common tools in machine learning are Amazon Lex, IBM Watson Studio, and Microsoft Azure ML Studio.	The most used and well-liked tools for data science are SAS, Apache Spark, and Tableau
Most of the input data is in a format humans can use.	Data input is altered appropriately for the kind of algorithms being employed.
Facial Recognition and recommendation systems are common examples.	Common examples include healthcare analysis and fraud detection.

Are data science and machine learning very similar?

No, are two distinct technological fields focusing on two facets of global business. Data science focuses on leveraging data to assist organizations in analyzing and understanding patterns, whereas machine learning focuses on enabling automated systems to self-learn and carry out tasks. That does not imply that there is no crossover between the two areas, though. On the contrary, because data is essential and ML technologies are quickly getting integrated into most businesses. Machine learning and data science depend on one another for various applications.

Which one should I learn?

The solution to this issue mainly relies on your objectives. A good grasp of data science tools is a beautiful place to start for scientists and researchers working in various sectors using data analysis. Machine learning will make sense for developers who want to incorporate intelligence into software or hardware solutions.

It’s easier to start with data science fundamentals (along with reviewing your programming and calculus/linear algebra/statistics). Data is ultimately the key to success in all these disciplines, so no matter which path you choose, having a solid set of abilities in processing, cleaning, analyzing, and visualizing data, along with the statistical understanding necessary to do so, will serve you well.

What is better, Machine Learning or Data Science?

First, because they are two distinct fields of study, it is impossible to compare the two areas and determine which is superior. It’s like contrasting the arts and sciences. Nevertheless, the apparent popularity of data science today cannot be disputed. Data is used by almost all businesses to create more reliable business decisions. Companies now use data daily to analyze performance, implement data-driven strategies, or create data-driven apps. Contrarily, machine learning is still in its initial stages and has only begun to be accepted by some sectors of the economy. It only confirms that ML technologies will soon be more in demand. Therefore, there will be an equal need for specialists in these fields in the future.

Why shouldn’t we start with Machine Learning?

May compare machine learning and data science to a square and a rectangle. Like a square can be a rectangle, but a rectangle isn’t always a square, machine learning is a component of data science, but data science isn’t always machine learning.

In practice, machine learning modeling accounts for only 5–10% of a data scientist’s work. 

Machine learning is fundamentally based on statistics, arithmetic, and probability. Therefore, to write a decent essay, much as you must first understand English grammar, figurative language, and other fundamentals, you must have these foundational concepts well established before you can study machine learning.

Conclusion

When Data Science and Machine Learning are seen in this context, it is clear that although they are each employed in their meaning, they frequently overlap. What does this mean for our decision in which one to prioritize, data science or machine learning? Simple: it is entirely up to you. It would be best to decide which of these you want to study based more on what you like or want to learn than on any established process in the two fields. This is not explicitly stated in any rule, practice, or concept, and there is no set hierarchy either.