More than 5.35 billion people are connected to the internet, and approximately 147 zettabytes of data are generated every day. If businesses want to succeed, they have to process this data and create data science and machine learning-based solutions.
Wondering Why? Here’s an interesting reason: Amazon’s recommendation engine, powered by both data science and machine learning, drives 35% of Amazon’s revenue. Data science and machine learning possess such an impact in the business space.
Most of the time, machine learning is considered a subset of data science, and many professionals differ in considering both of these disciplines as different components. This article on data science vs. machine learning exactly addresses this issue. If you want to know how both of these disciplines differ, keep reading this blog.
Table of Content
Check out this video from Intellipaat to start your journey of Data Science:
What Is Data Science?
Data science is a field of study that revolves around data. In simple terms, data science makes use of various tools and technologies to find meaningful information from raw data. It involves various techniques from statistics, mathematics, machine learning, and many more. Have you ever wondered how data science helps businesses? Let us try to understand with real-life examples.
Netflix is one of the examples. Whatever you watch on Netflix, it starts recommending similar types of movies or web series on your screen. Instagram also uses the same technique. Suppose you like to watch anime. Instagram’s algorithms are built in such a way that content related to anime starts appearing on your feed. Many big organizations, such as Amazon, Google, and Microsoft, use data science for their businesses.
What Is Machine Learning?
Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on providing machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human interaction. Machine learning programs are fed with fresh data, and they continue to learn, grow, evolve, and adapt independently.
For example, suppose we want a machine to recognize the difference between apples and oranges. In that case, we don’t provide it with specific instructions on what an apple or orange looks like. Instead, we provide it with thousands of images of both apples and oranges and let the machine learning algorithm figure out the common features and patterns between them that define apples or oranges. As time passes, the ML algorithm keeps processing more images and gets better at recognizing them.
Differences Between Data Science and Machine Learning
The table shows the description of data science vs. machine learning. We will be discussing only the key differences between data science and machine learning below. Further differences will be addressed in the later sections:
| Data Science vs. Machine Learning | Data Science | Machine Learning |
| Scope | BroadIt encompasses a variety of techniques for handling, analyzing, and visualizing data. | Narrow It focuses on the development of algorithms that can learn from and perform predictive analysis or other kinds of analytics automatically. |
| Goal | To generate actionable insights from data to support decision-making | To develop predictive models and algorithms to make predictions or decisions without being explicitly programmed |
| Tools Utilized | R, Python, SQL, Tableau, etc. | Python, R, TensorFlow, Scikit-learn, etc. |
| Techniques Employed | Descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics | Supervised learning, unsupervised learning, and reinforcement learning |
| Primary Focus | Extracting valuable information and insights from data | Creating and employing models that learn from data |
| Application Examples | Business analytics, decision-making, predictive modeling, etc. | Recommendation systems, image and speech recognition, autonomous vehicles, etc. |
| Outcome | Data-driven decision-making to enhance business operations | Automated decision-making to enhance system performance |
| Learning from Data | Utilizes machine learning as a tool, among many others | The core focus is on learning from data to improve. |
| Data Handling | It involves cleaning, preparing, and aligning data. | It is primarily concerned with the creation of algorithms to manipulate data. |
| Example | Netflix uses data science technology. | Meta uses machine learning technology. |
Relation Between Data Science and Machine Learning
Though there are some differences between data science and machine learning, there are some similarities too. In this section, let us understand what the relationship is between data science and machine learning. We use a combination of both data science and machine learning to build smart applications that provide techniques for business enhancement.
To understand the relationship between data science and machine learning, let us first look at the mechanism of data science for mobile applications and websites with the help of the below diagram:
- Collecting business requirements
- Data retrieval: User ratings, comments, and cart history
- Data processing: Missing values, fake reviews, and unnecessary data
- Data exploration: Understanding patterns and retrieving useful insights
- Data visualization: Graphical representation of information and data
- Prepare data for modeling
Now, the next part is machine learning.
After the data is prepared using the techniques of data science, we create machine learning models using the prepared data. Let us look at the below diagram to understand the mechanism of machine learning:
- Import Data: Gather the prepared data
- Build Model: Create a machine learning model using various algorithms that best suit the condition
- Feed Data: Provide data to the machine learning model
- Train Model: Train the machine learning model with the help of a variety of data
- Test Model: Test the machine learning model with new data and improve its efficiency
- Improve Efficiency: To improve efficiency, choose the right model architecture and framework
Data science and machine learning are separate fields that coordinate with each other to build systems that add value to a business. They improve and automate the internal workings of business processes and their software.
How Is Data Science Used in the Real World?
Data science is simply the proper study of raw data through analyzing and visualizing it. It helps extract the necessary business information from the data. Now, instead of talking about the theoretical aspects of data science vs. machine learning, let us understand how data science is used in the real world. Also, in this blog, we will discuss the use case of Zomato, which uses data science to boost its revenues.
Zomato is one of the examples that uses data science to increase revenue by providing personalized recommendations. The algorithm of Zomato is built in such a way that it provides personalized restaurant selections based on customer choices, order histories, and restaurant ratings. This ultimately leads to higher user engagement, higher order frequency, and a rise in overall income because users are more likely to order from recommended places.
How Is Machine Learning Used in the Real World?
Now, in this section of the blog on data science vs. machine learning, we will understand how merchants and banks use machine learning to give a personalized experience to customers and increase their profits.
As data science helps analyze and visualize data efficiently, machine learning helps in the prediction of events. Various merchants, such as Swiggy, Zomato, Flipkart, Amazon, and more, use ML-based software that helps them generate huge profits.
You must have observed how your preferred food-ordering application sends you notifications to buy food at the time you are likely to have your lunch or dinner. The application notifies you to buy dishes from your favorite restaurants. When you click on the notification, it takes you to the app and shows you the following:
- List of your favorite restaurants
- Previous orders
- Time of orders
- Frequent orders
- Payment gateway used
- Ratings you have given for food items and restaurants
- Amount spent on various orders
- Restaurants and dishes you may like
- Best selling delicious dishes with their pictures
- The best possible time required to deliver the food
In such scenarios, people clicking on these notifications may become potential customers if the food item is reasonably priced. To provide the food item at a reasonable price, merchants give multiple options, such as a ‘Promo Code’ consisting of offers that customers can apply for and get discounts.
These promo codes are given by various payment merchants for their promotions. This is a type of marketing that uses machine learning to allow businesses to generate huge profits.
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Data Science vs. Machine Learning: Scope of Job and Salary
Data Science Scope and Salary
According to McKinsey, there are 1 million jobs for a data scientist. In India, where startups are growing fast, there is a requirement for 15,000+ data scientists. Also, a report from Forbes says that the data science platform market size is expected to grow from US$37.9 billion in 2019 to US$140.9 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 30.0 percent during the forecast period.
Salary: According to Glassdoor, the salary of a data scientist ranges from ₹7 lakhs to ₹19 lakhs in India, with an average of ₹11 lakhs per annum.In the USA, it ranges from $1,00,000 to $2,00,000, with an average salary of $1,57,114 per annum, according to Glassdoor.
Source: Glassdoor
Source: Glassdoor
Different Jobs Offered Under Data Science
However, data science is an umbrella term under which many practices like data analysis, statistics, and machine learning engineering fall. So ideally, many professionals get started with small-scale job roles as highlighted below:
| Jobs | Average Salary in India | Average Salary in the USA |
| NLP Engineer | ₹7,00,000 | $1,09,038 |
| Data Analyst | ₹6,60,750 | $82,287 |
| Deep Learning Expert | ₹8,00,000 | $99,308 |
| Statistician | ₹4,25,000 | $1,09,038 |
Source: Glassdoor
Scope of Job and Salary in Machine Learning
According to LinkedIn, there are more than 21,000 jobs available in India; this number could be much higher if we consider other job boards. On the other hand, there are more than 58,000 machine learning jobs available in the USA, according to LinkedIn.
Salary: According to Glassdoor, the salary of a machine learning engineer ranges from ₹8 lakhs to ₹16 lakhs, with an average salary of ₹14 lakhs per annum in India. In the USA, it ranges from $1,00,000 to $2,00,000 per annum, with an average of $1,52,973 per annum.
Source: Glassdoor
Source: Glassdoor
Different Jobs Offered Under Machine Learning
Machine learning also offers a wide range of jobs and roles in the industry. A few are given below:
| Jobs | Average Salary in India | Average Salary in the USA |
| MLOps Engineer | ₹12,00,000 | $1,49,607 |
| Robotics Engineer | ₹5,00,000 | $96230 |
| ML Researcher | ₹16,00,000 | $172,364 |
Source: Glassdoor
Future Scope of Data Science and Machine Learning
The number of jobs available for a data science professional and a machine learning engineer is very high, as both profiles have to collaborate to provide automation and improve businesses. Both data science and machine learning apply techniques using various tools to prepare data, feed it to the machine learning model, and integrate the model with other software or applications.Today, every company uses applications and software that implement data science and machine learning. Thus, the demand and future scope of data science and machine learning will always remain high.
Conclusion
By this, we can conclude that the fields of data science and machine learning are both offering ample jobs and lucrative packages to aspirants to help them make a bright future. These technologies work together to develop much-advanced automation that is in the process of changing the whole world!
FAQs on Data Science vs. Machine Learning
Should I learn data science or machine learning?
It depends on your preference. If you are someone who wants to comprehend data and derive insights from it, you can select data science. However, if you are interested in creating models that increase performance with data, you can choose machine learning.
Does machine learning pay more than data science?
Although both professions offer high earning potential, the earning potential of a machine learning engineer is slightly higher than that of a data scientist. The average salary of a machine learning engineer is 14 lakh per annum, whereas the average salary of a data scientist is 11 lakh per annum.
Is data science harder than machine learning?
No, machine learning isn’t more complex than data science. The two fields are closely related, and many of the skills overlap. Data scientists have to learn to use machine learning models, and vice versa. Many data scientists and machine learning engineers have a background in statistics or mathematics.
Can machine learning replace data science?
Machine learning uses algorithms to learn from and make predictions or decisions based on data. On the other hand, data science is a field of study that combines statistics, computer science, and domain expertise to extract valuable information from data. So machine learning cannot replace data science; rather, it can be used as a tool for data science projects to build predictive models and analyze large datasets.
Can an average student become a data scientist?
The scope of data science is broad; even if you are an ordinary student, you can become a data scientist. Simply try to focus on building your skills. You can do so by going through the online data science courses and real-time projects offered by Intellipaat.