In this post, your confusion will be clear about Data science vs Machine learning. We have included various comparison aspects to clarify the difference between Data Science and Machine learning.
INTRODUCTION: Data science vs Machine learning
Data science and machine learning both are the most demanding and very popular fields. Data Science and Machine Learning are relatively new fields of study but have huge demand in sectors like the academic, corporate, banking, finance, etc.
Data Science: It is a multi-disciplinary field that is used to extract insight from structured and unstructured data by using methods, algorithms, systems, and processes.
According to the data science definition, it is a field of research that used to derive significance and knowledge from data using theoretical methods. “A mix of computer technology, market management, and simulation ” is what data science is. University had recognized the value of data science and machine and extensively working on developing degree program in this area which can be served to students in both forms i.e online and offline.
Machine Learning is a method of data analysis that is used to automate analytical model building. It is a sub-branch of AI which is developed with an idea, that machine and systems can learn itself from the patterns, data and use them to make decisions with least human intervention.
Difference Between Data Science and Machine Learning
| Data Science | Machine Learning |
| It is helpful in understanding and finding hidden patterns and useful insights from the data, which can lead to making smarter business decisions. | It can be called a subfield of data science which helps the machine to learn from prior data. |
| It is used to discover insights from the data. | It is used to make predictions and classifying the result for new data points. |
| It can work with raw, structured, and unstructured data. | It requires structured data to work. |
| It needs an entire analytics universe. | It is a combination of Machine and Data Science. |
| It’s a branch which deals with data. | Machines Learning uses various data science techniques to learn about the data. |
| There are plenty of techniques used in data science i.e. data gathering, data manipulation, data cleaning, etc. | It is three types: Reinforcement learning, Unsupervised learning, Supervised learning. |
| Example: A good example of data science is Netflix | Example: A good example of Machine Learning technology is Facebook |
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Skills Needed for Data Scientists & Machine Learning Engineers
These are the following skills needed to be a Data Scientists or Machine Learning Engineers. There are lot more skills to learn but the below skills are the basic requirement to be good at Data Science or at ML.
| Skills Needed for Data Scientists | Skills Needed for Machine Learning Engineers |
| Data mining and cleaning | Computer science fundamentals |
| Statistics | Statistical modeling |
| Unstructured data management techniques | Understanding and application of algorithms |
| Data visualization | Data evaluation and modeling |
| Understand SQL databases | Text representation techniques |
| Programming languages such as R and Python | Natural language processing |
| Use big data tools like Hadoop, Hive, and Pig | Data architecture design |
Tools Needed for Data Scientists & Machine Learning Engineers
You must have good knowledge of the following tools to master Data Science or Machine Learning.
- Python
- R
- Big Data
- Hadoop
- SQL
- NoSQL
- Numpy
- Pandas
- Tableau
- Tensorflow
Apart from the above tools, you need to be good at the following skills.
- Mathematics
- Statistics
Applications of Data Science
Here, are the application of Data Science
- Internet Search
- Online Price Comparison
- Image & Speech Recognition
- Recommendation Systems
- Gaming World
Applications of Machine Learning
Here, are Application of Machine learning:
- Automation
- Finance Industry
- Government Organization
- Healthcare Industry
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Data science vs machine learning jobs & career
CAREERS IN DATA SCIENCE
The majority of businesses are integrating data analysis to help their business grow and to increase efficiency thus Data scientists are in high demand and it is predicted to grow immensely.
For aspirants, some of the lucrative Data Science careers include:
Data Scientist
The major role of data scientists is to investigate different trends to assess their effects on a business. The Responsibility of a Data Scientist is to clarify the significance of data that everyone should understand.
Data Analyst
The job role of a Data Analyst is to analyze the data and determine the current industry trends, He/She also involves to develop a simple view of the company’s business place.
Data Engineer
A data engineer is the heart of the organization and may be considered as a backbone of a company. The major responsibility of a Data engineer is to create, manage, and design a database.
Business Intelligence Analyst
The major role of the Business Intelligence Analyst is to study the f=gathered data to improve the company’s income and productivity. Their job is much more difficult than its looks as they play a major role in the company’s growth.
Marketing Analyst
The major role of a marketing analyst is to help the marketing division to analyze and suggest production and marketing factors. They are also responsible for monitoring customer satisfaction and use customer feedback to improve existing products and decide the target audience and also assist the company to decide the price of the product.
CAREERS IN MACHINE LEARNING
Here are the top five roles to start your career in ML once you mastered the skills
Machine Learning Engineer
As per the report, Machine learning Engineer is one of the most coveted and existing job roles in the Data Science Field. The main responsibility of a Machine Learning Engineer is to make improvements in ML systems and applications.
They use Python Language to perform and operate numerous tasks.
Developer/Engineer of Software
The main responsibility of the Software Developer/Engineers is to create Intelligent Computer Program with a specialty in AI/ML. They are also responsible for creating complex programming functions, flowcharts, graphs, tables, layout, and planning product documents. Some other things like composing and evaluating the code, creating technological specification update and manage programs, etc also come under their responsibility.
Human-centered machine learning designer:
The primary responsibility of them is to create or improve the ML algorithm which is designed for humans and their choices. The prime example of this can be Netflix, based upon your choices or watch history how it suggests you related movies. It uses techniques like pattern recognition and Information process techniques for making a human-centric experience.
Natural language processing or NLP scientist:
An NLP scientist is responsible for the training and development of machines. They work for machines that machines can learn and understand the different human languages. So they have to understand how machine learning works.
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Roles and Responsibilities of a Data Scientist
Here, are an important skill required to become Data Scientist
- Knowledge about unstructured data management
- Work with professional DevOps consultants to help customers operationalize models
- Data mining used for Processing, cleansing, and verifying the integrity of data used for analysis
- Obtain data and recognize the strength
- Hands-on experience in SQL database coding
- Able to understand multiple analytical functions
Role and Responsibilities of Machine Learning Engineers
Here, are an important skill required to become Machine learning Engineers
- Knowledge of data evolution and statistical modeling
- Understanding and application of algorithms
- Implement appropriate machine learning algorithms and tools
- Design machine learning systems and knowledge of deep learning technology
- Data architecture design
- Knowledge of probability and statistics
- Natural language processing
- Text representation techniques
- In-depth knowledge of programming skills
Difference between a Data Scientist and a Machine Learning Engineer
Data Scientist: A Data Scientist is someone who knows the way to extract meaning from and interpret data, which needs both tools and methods from statistics and machine learning likewise human being. He/She spends lots of your time within the process of collecting, cleaning, and munging data because data is rarely clean
Machine Learning Engineers: Machine Learning Engineers sit at the intersection of software engineering and data science. They leverage big data tools and programming frameworks to confirm that the data gathered from data pipelines are redefined as data science models that are able to scale as per need.
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Salary
Data Scientist Salary
it varies from place to place and company to company
| Company Salary |
| Microsoft ₹ 15,00,000 PA |
| Accenture ₹ 10,55,500 PA |
| Tata Consultancies ₹ 5,94,050 PA |
| Experience Level Salary |
| Beginner (1-2 years) ₹ 6,11,000 PA |
| Mid-Senior (5-8 years) ₹ 10,00,000 PA |
| Expert (10-15 years) ₹ 20,00,000 PA |
Machine Learning Engineer Salary
| Company Salary |
| Deloitte ₹ 6,51,000 PA |
| Amazon ₹ 8,26,000 PA |
| Accenture ₹15,40,000 PA |
| Experience Level Salary |
| Beginner (1-2 years) ₹ 5,02,000 PA |
| Mid-Senior (5-8 years) ₹ 6,81,000 PA |
| Expert (10-15 years) ₹ 20,00,000 PA |
Data Science vs Machine Learning – Which is Better?
The machine learning method is ideal to analyze, understand, and identify the pattern in the data. One can use this method to develop or train a machine to automate tasks that would be almost impossible for a human being. Also, we can say that machine learning is a technique that requires minimal human interactions for taking decisions.
On the other hand, data science can help in detecting fraud activities using advanced machine learning algorithms. It also helps in preventing any significant monetary losses. It also helps in performing sentiment analysis to gauge customer brand loyalty.
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