Machine Learning vs Data Science – Are they really different?

Every data lover must have heard the terms data science and machine learning together and got confused in both.

Surely you are one of them, that’s why you are here. But don’t worry, DataFlair is providing you the best guide for identifying the difference between data science and machine learning quickly.

I am explaining the concept in simpler terms so that a beginner can understand both the topics clearly. Hope this Data Science vs Machine Learning article will help you.

Machine Learning and Data Science are two vast domains, with each field harboring a vast amount of knowledge and expertise. Data Science can be understood as a vast ocean that contains various intrinsic data operations.

Machine Learning is one of the major data operations that form this ocean of data science. Before exploring the difference between data science and machine learning, first, get a brief overview of both.

What is Machine Learning?

The scientific study and analysis through statistical modeling and computing algorithms that allow systems to make autonomous decisions without explicit intervention is called Machine Learning.

There are two main operations in machine learning – classification and regression. Based on these operations, machine learning makes use of predictive models that calculate the likelihood of the occurrence of events.

In general, machine learning facilitates computers to learn without explicitly feeding instructions.

For example, we provide standardized instructions to the computers for it to execute operations. These instructions can be in a high-level programming language or a low-level machine language. Based on the set of instructions, a computer provides you with the right output.

Therefore, there is a constant exchange of inputs and outputs. What if you could train your machine to provide you with the output based on all inputs you provide to it? That’s way, you will not have to undergo a tedious process of feeding it with inputs again and again.

This methodology of training your machine on historical data to provide you with the output is called Machine Learning. Data is the life and soul of machine learning algorithms. A machine learning algorithm searches and identifies intrinsic patterns within the data.

The types of machine learning algorithms are as follows –

Supervised Learning

When the machine learning model is fed data that is organized in labels, we call it supervised learning. With the help of this, a machine learning model is able to map input-output pairs and learn patterns associated with them.

With the abundance in instances of the input-output pairs, machine learning is able to grasp the vast streams of information and underlying patterns. Therefore, more the data, better the results. Some of the supervised learning algorithms are –

• Linear and Multivariate Regression
• Logistic Regression
• Decision Trees
• Naive Bayes
• Linear Discriminant Analysis
• K-nearest neighbour
• Artificial Neural Networks

Unsupervised Learning

Unlike the data present in the case of supervised learning, some data is not organized in labels. There is no proper mapping of input-output pairs. In such cases, we make use of unsupervised learning algorithms. Self-organization is the key principle in unsupervised learning.

The model is able to identify patterns within the data and learns the input-output pairs during the training phase. They are an advanced form of machine learning algorithms and are currently being researched. Some of the unsupervised learning algorithms are –

• Anomaly Detection
• Clustering Analysis
• Principal Component Analysis
• Hierarchical Clustering

Learn everything about Machine Learning – Check the DataFlair Machine Learning Tutorials Series

Reinforcement Learning

It is a more recent and specialized branch in machine learning. In this type of learning, we train agents in scenarios where they have to reach a goal. Based on the rewards and penalties, the agent is able to navigate itself to its destination.

It does not need input-output mapping but relies on exploration of environments and learning from the previous mistakes. Self-driving cars and autonomous robotics are two of the most popular applications of reinforcement learning. There are two commonly used reinforcement learning algorithms –

• Q-Learning
• SARSA (State-Action-Reward-State-Action)

Several industries around the world are making use of machine learning for generating predictions, identifying patterns and for making autonomous decisions. Furthermore, industries like healthcare, banking, finance, manufacturing, and transportation make heavy use of machine learning algorithms.

Machine Learning Tools

Some of the popular tools and packages for Machine Learning are –

a. Scikit-learn

scikit-learn is an open source machine learning library that is written in Python. With scikit-learn, you can perform classification, clustering, regression and can also implement support vector machines.

b. Tensorflow

TensorFlow is a symbolic math library that is used for various machine learning applications like the implementation of neural networks. It has multiple applications like speech recognition, image classification that is supported by GPUs and TPUs.

c. CARET

CARET is an R language based library that offers a variety of classification and regression solutions to the problems.

d. Mlpack

mlpack is a fast and dynamic machine learning library that is written in C++. It also provides bindings in Python and R and can be executed with a single line command in the terminal.

e. Shogun

Shogun is a popular, open-source machine learning software. It is also written in C++. It supports various languages like Python, R, Scala, C#, Ruby etc. Some of the algorithms supported by Shogun are –

• Support Vector Machines
• Dimensionality Reduction
• Clustering Algorithms
• Hidden Markov Models
• Linear Discriminant Analysis

f. Weka

Weka is a machine learning suite that is written in Java. It offers a wide range of machine learning tools that allow users to perform classification, clustering, regression, and visualization.

All of these operations can be performed without writing a single line of code as Weka offers an interactive GUI interface to its users.

Do you know which is the best among R, Python or SAS? 

What is Data Science?

The rise in the quantity of data has led to the creation of a new discipline called Data Science.

Data has become a necessary fuel for the industries, bringing about the fourth industries revolution. Data Science has been crowned as the most coveted jobs of this century. All the growth and evolution in the field of computer science has led us to the current field of Data Science.

It is a vast umbrella term that incorporates all the underlying data operations, statistical models as well as mathematical analysis. The supermassive explosion and exponential increase of data have created an opportunity for businesses to capitalize on.

Using this data, industries are able to make careful decisions and implement useful business strategies. Data is everywhere around us. Every day, our mobile phones, gadgets, sensors generate quintillion bytes of data. It has become a source of energy that is utilized in every sector of our society.

A Data Scientist must be proficient in various underlying fields like statistics, math and computer programming. In order to be proficient at Data Science, you must be able to understand various trends and patterns in data with the use of statistical.

Data Science poses a steep learning curve for beginners and in order to master it, you must have the individual proficiency of its underlying fields. A data scientist is also required to have versatility over structured and unstructured data both.

Recommended Reading – Why you should learn Data Science?

There are various steps involved in data science. These steps are data extraction, data manipulation, visualization of data, implementing predictive models and optimizing them for better performance and accuracy.

We all know that with the emergence of data science, industries are seeking proficient data scientists who can take important decisions for improving their performance and providing better services to their customers.

Following are the steps involved in Data Science processes –

• Gathering Data

Retrieval and extraction of data is the primary step in the data science process. A Data Scientist must be able to handle all types of data like structured and unstructured data. Furthermore, knowledge of database queries like SQL and NoSQL are essentially important.

• Data Preprocessing

Data Cleaning and transformation is the second step in the data science process. In this process, we also replace missing values that might be embedded in our dataset. This is the most important step as it organizes the data and makes it useful for further analysis.

• Data Analysis

The two most important data analysis techniques are descriptive statistics and inferential statistics. Using them, we are able to draw insights and understand the underlying patterns that are hidden in the data.

• Generating Predictions

In the next step, we generate predictions using various machine learning algorithms. For this, we make use of several predictors and classifiers. We use wide arrays of machine learning algorithms to generate predictions and perform classifications on the data. We forecast future events and also capture hidden patterns within the data.

• Optimizing Models

In the final step, we optimize the machine learning algorithm and improve its performance through several experimentations. This allows the machine learning model to improve its performance and give us accurate results.

Some of the important tools used in data science are –

• Python

Python is the most beginner-friendly programming language for data science aspirants. It is also used for application development. This makes Python a widely used programming language. It is also supported by a large number of libraries that allow various data science operations.

Do you know how Python is used for data science? 

• R

R is a statistical modeling language that is also open-source. Using R, you can visualize as well as analyze data.

• SAS

SAS is abbreviated for Statistical Analysis System. This software tool was developed by SAS Institute for facilitating various statistical operations. Unlike the two tools mentioned above, SAS is closed source. However, it is widely preferred by large scale corporations due to its stable features and extensive reliability.

• Apache Spark

Apache Spark is a powerful tool for Big Data operations. Spark is used for large scale data processing and its analysis. Spark is also capable of processing real-time data in streams, which is in contrast with the batches that were processed by Hadoop.

It is the time to upgrade your skill and start learning Apache Spark with industry experts. 

• D3.js

Using D3.js, you can create interactive visualizations for the users. It is written in JavaScript and is a robust library for embedding graphs in front-end applications.

Machine Learning vs Data Science

Data Science and Machine Learning are the two terms that share a lot of similarities. A Data Scientist makes use of machine learning in order to predict future events. However, there are other important procedures that are also involved in the field of Data Science.

Procedures like data pre-processing, data visualization, data transformation, data cleaning, data analysis, etc. Machine Learning is one of the final stages of the steps involved in data science.

A Data Scientist makes the most use of supervised machine learning algorithms to engage in predictive analysis. However, machine learning in itself is a vast field.

It consists of other important sub-fields like unsupervised learning and reinforcement learning. Data Science, through an umbrella term, is not mandated to deploy such advanced learning models.

One of the main reason is the industry where a Data Scientist works. For companies, decision making is the key requirement for Data Scientists to deliver. For this, they analyze the data and develop predictions mostly through Supervised Learning.

Some cases may involve usage of Unsupervised Learning but they are mostly limited. Furthermore, for working on advanced machine learning algorithms like reinforcement learning, industries hire Artificial Intelligence Researchers/Engineers.

After reading machine learning vs data science, I recommend you check the difference between Data Science vs Big Data.

Conclusion

In this machine learning vs data science tutorial, we saw that Machine Learning is a tool that is used by Data Scientists to carry out robust predictions.

Machine Learning is a vast subject and requires specialization in itself. We also went through some popular machine learning tools and libraries and its various types.

Hope this comparison of machine learning vs data science helped to get a clear understanding of both the technologies. For any queries or doubt comment below.