Data Science vs Machine Learning – An overview

Key takeaways:

• Starting with statistics, data science has expanded to incorporate ideas and methods from the Internet of Things, machine learning, artificial intelligence, and many other fields.

• Data science employs machine learning, artificial intelligence, and data mining methods.

• Machine learning is a type of AI that employs algorithms to extract data and forecast future trends.

• Both data science and machine learning have grown to become the top career choices across the globe.

Data science and machine learning are two ideas in the technology field that help us create and invent new goods, services, infrastructure systems, and other things by using data. Both represent highly sought-after and lucrative career opportunities. The two are related in the same manner: squares are rectangles, but rectangles are not squares. Data science is the all-encompassing rectangle. However, machine learning is a distinct square. 

Data Science vs Machine Learning – An overview

The significant difference between machine learning and data science is that, on the one hand, data science focuses on improving data visualisation and presentation, whereas machine learning focuses on learning algorithms and learning from real-time data and experience. Let us take a closer look at some other differences between the two with the help of the following table –

Parameters	Data Science	Machine Learning
Definition	Data science is an intriguing area in which unstructured data is cleaned, filtered, and analysed, with the end result being business breakthroughs.	Machine Learning is a branch of data science in which tools and techniques are utilised to construct algorithms that allow machines to learn from data through experience.
Tools	SAS, Apache Spark,BigML, D3  MATLAB, Excel, ggplot2, Tableau	TensorFlow, PyTorch, PyTorch Lightning, Scikit-learn, Catalyst, XGBoost, LightGBM, CatBoost
Application	Healthcare , Video games, Recognition of images, Suggestions Systems, Logistics, Fraud detection	Image recognition, Speech Recognition, Traffic prediction, Self-driving cars, Email Spam and Malware Filtering, Virtual Personal Assistant
Job roles	Data Scientist, Director of Data Science, Data analyst, Data engineer, Data architect	Machine learning engineer, AI engineer, Cloud Engineer, Designer of human-centred AI systems, Senior Software Engineer
Skills	Understanding of probability, statistics, and mathematics, skills in data visualisation and data manipulation, excellent interpersonal and cooperative skills	Computer science knowledge, such as data structures, algorithms, and architecture, excellent knowledge of statistics and probability, Programming skills, including Python, R, and others
Payscale	INR 4.5 Lakhs to INR 25.0 Lakhs*	INR 3.5 Lakhs to INR 21.0 Lakhs*
Top employers	IBM, Wipro, Cloudera, Splunk.	Amazon Web Services, Databricks, Dataiku, Veritone

What is data science?

Data science is the study of data and how to derive insights from structured and unstructured data using various approaches, algorithms, systems, and tools. This expertise can be used in enterprises, government agencies, and other organisations to boost profits, develop new goods and services, enhance public systems and infrastructure, and more.

Data science processes

The systematic approach to solving a data problem is known as the data science process. It offers a methodical framework for formulating your issue as a question, choosing a course of action, and subsequently outlining the proposed action to stakeholders.

• Discovery

Acquisition of data from all designated internal and external sources is part of the discovery stage, which aids in responding to the business issue.

• Preparation

Data must be cleaned of irregularities, such as missing numbers, blank columns, or erroneous data formats. Before modelling, data needs to be processed, explored, and conditioned. 

• Model planning

You must choose the approach and strategy to illustrate the relationship between the input variables at this stage. Among these programmes are SAS/access, R, and SQL Analysis Services.

• Model Construction

In this stage, the real model construction process begins. Here, a data scientist distributes training and test datasets. The training data set uses methods including association, classification, and clustering. 

• Put into practice

In this stage, you submit the baselined model in its final form along with reports, code, and technical documents. After extensive testing, the model is introduced into a live production setting.

READ MORE: The data science roadmap explained

Application of data science

There are many fantastic applications in the field of data science as stated below. Data science plays a significant role in the development of organisations in the current era of digitisation and not just in businesses.

• Healthcare 
• Video games
• Recognition of images
• Suggestions Systems
• Logistics
• Fraud detection

Data Science skills

To advance your career path in data science, you will need to learn programming and data analytics. Here are some of the skills that you must have in order to become a successful data scientist –

• Proficiency in Python, R, SAS, and other programming languages

• Working experience with vast volumes of organised and unstructured data

• Capable of collecting and analysing data for business purposes

• Knowledge of arithmetic, statistics, and probability

• Data visualisation and data manipulation abilities

• Familiarity with machine learning algorithms and models

• Excellent communication and teamwork abilities

Data science career

According to the data science career roadmap, there are many data science jobs available:

Job roles	Salary
Data Scientist	INR 10,00,000
Director of Data Science	INR 59,76,044
Data analyst	INR 6,98,413
Data engineer	INR 8,83,000
Data architect	INR 13,50,000

What is machine learning?

Machine learning is a kind of artificial intelligence that uses algorithms to extract data and forecast future trends. There are several types of machine learning which are used and implemented accordingly. Models are coded into software, enabling engineers to undertake statistical analysis to understand significant  trends in data.

The machine learning algorithm is a set of techniques and concepts used in data science but also emerges in domains outside of data science. Machine learning is frequently used by data scientists in their work to help acquire more information faster or to assist with trend analysis.

Machine learning processes

The process of creating systems that learn and develop on their own through carefully designed programming is known as machine learning. Designing algorithms that automatically assist a system in gathering data and using that data to learn more is the ultimate goal of computer vision.

• Data collection

As you know, machines initially pick up knowledge from the data you provide them. It is crucial to gather trustworthy data so your machine learning model can identify the proper patterns. 

• Preparing the data

You must prepare your data after you receive it. This is possible by –

• Combining and randomising all of the data you have. This ensures that the distribution of the data is balanced and that the learning process is unaffected by the ordering.
• Cleaning the data removes unnecessary information, missing values, duplicate values, rows, and columns, data type conversion, etc.

• Choosing a model

After applying a machine learning algorithm to the data you’ve gathered, a model you choose for machine learning will determine the results. Selecting a model that applies to the current task is crucial. 

• Training the Model

In machine learning, training is the most crucial stage. You feed your machine learning model the prepared data during training to detect patterns and generate predictions. As a result, the model acquires knowledge from the data to complete the given task. 

• Evaluation of the model

After training your model, you must assess its effectiveness. To do this, the model’s performance is analysed with the help of data that has never been seen before. 

Applications of machine learning

Many sectors can benefit from deploying and enhancing machine learning (ML) procedures. ML is currently used without restrictions in many different disciplines and companies.

• Image recognition
• Speech Recognition
• Traffic prediction
• Self-driving cars
• Email Spam and Malware Filtering
• Virtual Personal Assistant

Machine learning skills

To be a competent machine learning engineer, you should be knowledgeable in the following areas –

• Computer science knowledge, such as data structures, algorithms, and architecture

• Excellent knowledge of statistics and probability

• Software engineering and system design knowledge

• Programming skills, including Python, R, and others

• Data modelling and analysis abilities

Machine learning careers

There are various alternatives available to you if you pursue a machine learning career path and artificial intelligence.

Job roles	Salary
Machine learning engineer	INR 7,20,701
AI engineer	INR 8,00,000
Cloud Engineer	INR 12,80,000
Designer of human-centred AI systems	INR 16,00,000
Senior Software Engineer	INR 11,03,638

Boost your careers with an M.Sc. in Data Science from Online Manipal

With the Master of Science in Data Science programme offered by the Manipal Academy of Higher Education (MAHE) through Online Manipal, you can develop a standout career in analytical and leadership roles across various industries. The curriculum gives you experience using real-world data to solve problems by combining machine learning, big data analytics, and statistics.

Course duration	Fee structure
24 months15-20 hours/week	INR 2,60,000INR 65,000 each semester

Eligibility criteria

The eligibility criteria for the MSc Data Science course are as follows – 

Indian students

• Graduates in computer science, mathematics, or statistics with a B.Sc., BE, B.Tech, or BCA degree, or a degree comparable, and at least two years of study in the discipline at an Association of Indian Universities (AIU)-recognised institution, and with a minimum of 50% of the grade or equivalent.

• It’s ideal to have work experience.

International students

• Graduates in computer science, mathematics, or statistics with a BSc, BE, BTech, or BCA degree, or an equivalent degree, and a minimum of 50% overall or an equivalent grade from colleges or universities recognised by the Association of Indian Universities (AIU).

• To apply to any Indian university, candidates must have received their graduation degree and the certificate of equivalency from the Association of Indian Universities.

• These individuals are qualified to enrol in MAHE’s online degree programmes. All such students and professionals will be charged an international price for their respective degrees and may be required to present documentation during the application process, including but not confined to a CV, address verification, valid visa, PR card, and a copy of their passport.

Placement assistance

The placement cell strives to improve students’ employability levels who are interested in pursuing careers after completing their programmes. Online Manipal provides sessions on skill development, resume building, and industry connect to help students improve their career prospects.

Wrapping up 

As can be seen, both data science and machine learning are excellent career possibilities with numerous chances. So, instead of knowing the difference between data science and machine learning and debating which is better, it is preferable to know and learn data science because if you learn data science, you can master both and have a career as either a data scientist or a machine learning engineer.
You’ll require programming and statistical expertise to get a job in data science or machine learning. The MAHE’s master’s in data science programme offered at Online Manipal is intended to help you find work as a data scientist or in a related field. You’ll study Python and SQL, as well as data analysis and visualisation, as well as how to create machine learning models.