Data Science Vs Machine Learning
Did you know that by 2024, an astonishing 79 zettabytes of data will be created, consumed, collected, and duplicated globally? This showcases the exponential growth of digital information and its importance in today’s highly connected world. So, how does one make sense of this data or utilize it for improving operations and enhancing decision-making? The answer is with the help of data science and machine learning.
While both domains are interconnected, there are some significant differences between the two. This blog will explore what is data science and machine learning, the key differences between these domains, skills required by machine learning and data science professionals, career scope, and more.
What is Data Science?
Data science is the technique of interpreting a vast amount of data in a way that extracts valuable insights from it. It helps analyze, cleanse, prepare, and interpret data in many different ways. Data scientists employ a variety of data science tools to help with this process.
You can check out this data science course with a placement guarantee to gain the in-depth knowledge required to excel in this field.
What is Machine Learning?
Machine learning is a branch of artificial intelligence. It includes the development process of data science, which makes it a sub-part of data science as well. It uses algorithms to extract data and predict future trends. As machine learning is a branch of artificial intelligence, it does not need supervision repeatedly. In fact, it operates independently and comes in handy when applied to data science.
Difference Between Data Science and Machine Learning
Data science and machine learning have different purposes and goals. The following table explains how they are different from one another.
| Parameter | Data Science | Machine Learning |
| Objective | It helps extract meaningful insights from data to help grow the company. | It is a branch of AI; therefore, the machine learns from past data and gives predictions. |
| Domain | It is a stand-alone field. | It is a sub-field of data science. |
| Type of Data | It can be used on both structured and unstructured data. | It can only be used on structured data. |
| Methodologies | It is an umbrella term with many sub-parts, out of which machine learning is also a part. | It is a part of data science. |
| Operations | Data gathering, data cleaning, data manipulation, data engineering, etc. | Supervised learning, unsupervised learning, and reinforcement learning. |
| Skillset | It needs skills like SQL, math, statistics, Python, etc. | It needs skills like computer science, algorithms, data analysis, etc. |
| Job Responsibilities | The work of a data scientist mainly consists of handling the data. | ML engineers work on the implementation of algorithms. |
| Applications | Used for creating dashboards, generating reports, developing predictive models, and identifying trends. | Used for image recognition, recommendation systems, autonomous decision-making systems, natural language processing, and fraud detection. |
| Example | Netflix uses data science. | Facebook uses machine learning. |
Skills Required by Data Scientists
If you want to be a data scientist, there are certain skills that you must possess. Some of these skills are –
- Good knowledge of various programming languages such as Python, SAS, etc.
- Ability to work with structured and unstructured data.
- Ability to analyze the data and transfer the results in easy language for the rest to understand and work on, along with data visualization skills.
- An excellent grasp of math, statistics, and probability.
- Ability to work with machine learning algorithms and models.
- Knowledge of SQL database coding.
- Strong teamwork and communication skills.
- Teamwork and communication skills.
- Experience in using big data tools such as Hadoop.
Skills Required by Machine Learning Professionals
To excel in machine learning, you must have a good skill set. Following are the skills you require for machine learning:
- Exceptional skills in computer science.
- Good knowledge of algorithms.
- Proficient in probability and statistics.
- Knowledge of Python, R, SAS, etc.
- Ability to analyze data.
Data Science vs Machine Learning: Career Scope
Let us take a look at the job outlook and career prospects in data science and machine learning.
Career Scope in Data Science
Data science jobs are predicted to experience 36% growth between 2021 to 2031, projecting a positive employment outlook. This domain offers a vast scope and has opened more sub-fields than just one, expanding the careers around it. An online course in data science can give you the right skill set for the various job roles. Following are some of the career options in data science:
- Data Scientist
- Data Analyst
- Data Engineer
- Data Architect
- Business Intelligence Analyst
Career Scope in Machine Learning
According to the Future of Jobs Report 2023, the demand for AI and machine learning specialists is expected to grow by 40%. This highlights the positive career prospects in the machine learning domain. If you are keen on pursuing a career in this field, an online course in machine learning can help you kickstart your journey. Following are some of the career options in machine learning:
- Machine Learning Engineer
- AI Engineer
- Cloud Engineer
Where is Machine Learning Used in Data Science?
Machine learning is used in the data modeling step of the lifecycle of data science. This life cycle or development process includes the following steps:
- Business Understanding: This step involves realizing the business objectives for which the solution is required.
- Data Mining: Next, data is acquired from different sources. This data can be structured or unstructured.
- Data Pre-Processing: The raw data is transformed into a suitable format in this step. This is done to improve the accuracy and reliability of the results.
- Data Exploration: In this step, data scientists study the patterns and trends in data and find meaningful insights.
- Modeling: Machine learning is used in this step of the data science lifecycle. It includes processes like importing data, cleaning data, building and training the model, and testing it to improve efficiency.
- Deployment and Optimization: In the final step, the model is deployed on a real project, and the model’s performance is monitored and optimized accordingly.
Conclusion
The difference between data science and machine learning lies within their scope, objectives, techniques, and more. While data science encompasses a broader spectrum of disciplines like statistics, data analysis, domain expertise, etc., machine learning involves developing algorithms that enable computers to learn and make predictions based on data. Machine learning is an essential part of the data science lifecycle that deals with data modeling. For this reason, you can transition from one of these roles to another by upskilling yourself.
Did you find this blog helpful? Let us know in the comments below. If you are looking for project ideas, check out top data science projects for diverse professionals.
FAQs
No, data science and machine learning are not the same. While data science is a field that involves extracting meaningful insights from structured and unstructured data, machine learning is a field that focuses on learning from the data.
Each field is best suited for different individuals. Those interested in understanding data and deriving meaningful insights from it should choose data science. Individuals who prefer creating and training models by utilizing data can go for machine learning.
Though both professions offer a high earning potential, machine learning engineers are paid slightly more than data scientists. Machine learning engineers earn INR 16 LPA whereas data scientists earn INR 14 LPA.
You should make this choice based on your interests, goals, and skillset. The core concepts of data science include R programming, statistics, and neural networks. Similarly, the core concepts of machine learning include algorithms, data mining, and statistical pattern recognition.
No, machine learning will not replace data science. While machine learning models help in improving performance and informing predictions, data science is a field devoted to extracting meaningful insights from the data. Further, machine learning is only one domain among others that data science utilizes for its processes.
The field of data science is expected to grow in 2030 as more organizations recognize its importance in today’s digital landscape. The domain is expected to witness a growth of 36% with employment available in diverse sectors, such as finance, telecommunication, healthcare, banking, and more.
While AI has made tremendous advancements, including the automation of several data science-related tasks, it will not completely replace data scientists. This is because it lacks the creativity and critical thinking abilities that humans bring. So, AI will become an intelligent assistant to data scientists rather than replacing them completely.
Yes, data science is a safe career. With different industries hiring data science experts to improve their operations, the demand for these professionals has greatly increased. Additionally, the average salary of a data scientist is INR 14.4 LPA. Therefore, data science is a rewarding career path with several opportunities.
