Table of Content
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Data Science is the study of large and complex data sets to build analytical models for identifying insights and unlocking value-driven business decisions. The scope of Data Science includes Machine Learning, Artificial Intelligence, Statistics, Modelling, Databases, and Programming. The professionals specializing in the domain are known as Data Scientists. The process typically involves obtaining raw data, converting it into the analysis-ready format, and finally feeding it into algorithms and models to draw actionable insights.
Whereas, Machine Learning refers to the process of collecting real-world data, extracting useful information from it, and then taking actions to perform certain tasks without manual programming. It helps systems improve over time on their own by exploring various types of real-world data. It also allows organizations to improve their business processes and strategies by knowing the insights that are extracted from the data analysis.
Difference Between Data Science and Machine Learning
Below-mentioned table shows Data Science Vs. Machine Learning that is the major difference between data science and machine learning:
| Data Science Vs. Machine Learning | Data Science | Machine Learning |
| Scope | Broad, encompassing a variety of techniques for handling, analyzing, and visualizing data. | Narrower, focusing 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. | Recommendation systems, image and speech recognition, autonomous vehicles. |
| 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 | Involves cleaning, preparing, and aligning data. | Primarily concerned with the creation of algorithms to manipulate data. |
| Example | Netflix uses Data Science technology. | Meta uses Machine Learning technology. |
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Relation Between Data Science and Machine Learning
Though there are some differences between data science and machine learning, there are some similarities too. Now, in this section, let us understand what is the relation between Data Science and Machine Learning. We use a combination of both Data Science and Machine Learning for building 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
Now, the next part that comes is Machine Learning.
After the data is prepared using the techniques of Data Science, we create Machine Learning models for using the prepared data. Let us look at the below diagram to understand the mechanism of Machine Learning:
- Gather the prepared data
- Create a Machine Learning model using various algorithms that best suit the condition
- Provide data to the Machine Learning model
- Train the Machine Learning model with the help of a variety of data
- Test the Machine Learning model with new data and improve its efficiency
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 working of business processes and their software.
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How is Data Science used in the real world?
Data Science is simply a proper study of raw data through analyzing and visualizing it. It helps extract necessary business information from the data. Now, instead of talking too much 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 that uses Data Science to boost its revenues.
Today, every transaction made by us is recorded and tracked by banks. But, the question is, what is the benefit of doing so? How are banks and merchants able to generate business with the help of recorded data?
Merchants such as Zomato use software, employing Data Science, to create a data product from the recorded data. The software includes functionalities such as data filtration, customer segmentation, transaction tracking, and many more.
It tracks every transaction made by a customer and filters it according to certain parameters. Also, it creates segments for payments made to other merchants as well, such as Swiggy, Dominos, and more.
With the help of Data Science, the software can efficiently analyze the data. The visualizations of the customer’s data help understand the patterns of transactions through various modes of payment.
By using the methodology of Data Science, merchants segregate customers according to partner banks. Then, this data is provided to banks. This enables banks to provide offers to customers on paying through debit\credit cards. By this strategy, both the banks and the merchants generate mutual profits.
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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 for giving a personalized experience to customers and increasing their profits.
As Data Science helps analyze and visualize data efficiently, Machine Learning helps in the prediction of events. Various merchants such as Paytm, Swiggy, Zomato, Flipkart, Amazon, and more use ML-based software that helps them generate huge profits.
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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 turn into potential customers if the food item is available at a reasonable price. For providing the food item at a reasonable price, merchants give multiple options, such as a ‘Promo Code’ consisting of offers that the customers can apply and get discounts.
These promo codes are given by various payment merchants for their promotion. 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
As we already know the demand and value of Data Science and Machine Learning, In this section on Data Science vs. machine learning, we will check out the scope of jobs and salaries in both of these fields.
Scope of Job and Salary in Data Science
According to McKinsey, there are 1 million vacant jobs for a Data Scientist. In India, where startups are growing fast, there is a requirement of 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: The average salary of a professional in Data Science in the United States is US$135,500, and in India, it is ₹1,015,385. Let us look at the salary of a Data Scientist as per the estimation of Glassdoor.
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Different jobs offered under Data Science
The following are the various roles offered under the Data Science domain:
- Data and Analytics Manager
- Database Administrator
- Data Analyst
- Data Engineer
- Data Architect
- Data Scientist
- Business Analyst
- Machine Learning Engineer
- Statistician
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Scope of Job and Salary in Machine Learning
As per the reports of Forbes, the number of job posting for Machine Learning Engineers in March 2020 was more than 8,000.
On the other hand, the overall job postings across the world for a Machine Learning Engineer in 2020 is around 1 million, similar to the job postings for a Data Scientist.
Salary: Salaries companies offer to Machine Learning Engineers lie between US$752,000 and US$1,534,000 per year. In India, the salary of a professional in Machine Learning lies between ₹825,000 and ₹2,450,000 per year. Let us have a glance at the salary of an ML Engineer by Glassdoor.
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Different jobs offered under Machine Learning
Machine Learning also offers a wide range of jobs and roles in the industry. Few are given below:
- Machine Learning Cloud Architect
- Machine Learning Engineer
- Robotics Engineer
- AI Systems Designer/Researcher
- Data Scientist
- AI Operations Engineer
- AI Engineer
- Cybersecurity Analyst
- NLP (Natural Language Processing) Scientist
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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 with each other 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.
By this, we can conclude that the fields of Data Science and Machine Learning both are 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!
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