Many a time, Data Science is mistaken for Machine Learning and vice-versa, the two terms are distinct and have an extensively broad meaning. Although the field of Data Science is interrelated with Machine Learning, there is a wide chain of differences between the two.
First, letâ€™s understand the meaning of the two terms and their implications individually, then we shall discuss their difference on various bases to get more clarity.
Data Science refers to the complicated study of the massive amounts of data stored in a companyâ€™s or organisationâ€™s repository. It includes tracking the origin of the data, the exact study of its content, and using it to accelerate the growth of the firm.
Data Science includes the entire process of data extraction, data visualisation, data cleansing and data analysis. The data stored in an organizationâ€™s repository can be grouped into two categories â€“ Structured and Unstructured.
After analysing these data sets, data scientists interpret some information that can be used to derive market trends, this helps the business in generalising the consumerâ€™s activity and noting their response towards the various price fluctuations and product changes for future reference.
Data scientists are experts who put raw data into use for handling crucial business matters. Data Scientists have a thorough knowledge of coding paradigms, numerical computation, statistics, and graphical representation of data for carrying out data visualisation and extraction.
The applications of Data Science have tremendously increased over the last few years, it is widely being used by companies such as Amazon and Netflix for generating recommendations for users. Data science is also widely used in the fraud detection sector, search engines, airline and banking software, the healthcare sector and so on.
Skills Required to become Data Scientist
There are several skills that are required to become a Data Scientist:
Programming Language: You should be good at programming language, there are some programming languages which are preferably used for data science such as Python, R, SQL, Scala, and JavaScript. This is a very basic skill to have in order to become a data scientist.
Statistics and Probability: While or After learning a programming language, you can read about statistics and probability because you will need to organize and present the data and this skill will help you a lot.
Linear Algebra and Calculus: You should be familiar with the mathematics topics such as linear algebra and calculus because while developing algorithms you may face problems if you are familiar with these topics.
Data Visualization Tools: You can learn the data visualization tools such as Power BI, Tableau, or Python libraries like matplotlib.
There are skills you can learn but you should always learn those skills while exploring the field of data science.
What is Machine Learning?
Machine Learning is centered on learning algorithms and using real-time data and experience to predict the future. It refers to the branch that assigns computers, the capability of performing without being given any instructions explicitly.
Machine Learning is implemented with the help of Algorithms for processing data and training it for carrying out future predictions without the intervention of human beings. The input for devising the training data for Machine Learning comes from a set of instructions or observations or data. Tech-Savvy companies such as Facebook, Google, and Skype widely use Machine Learning.
The skills you need for the machine learning sometimes collides with the data science. Here are the some of the skills you need to start with the machine learning:
Mathematics: There are some important topics such as linear algebra, calculus, statistics and probability which can help you in becoming a good machine learning engineer.
Programming: Before starting machine learning, you should be good at atleast one programming language because the algorithms for machine learning systems are created with these programming languages mainly.
Machine Learning Algorithms: There are some libraries and packages that you can use without writing the implementation of these algorithms such as scikit-learn, TensorFlow, and Pandas, etc.
Neural Networks: Neural Networks are very important concept in machine learning which are modeled after the neurons of the human brain.
Where is Machine Learning used in Data Science?
In data analysis, machine learning analyzes the data automatically, which automates the process of data analysis and makes predictions in real time. This is how a data model is created automatically, which can be trained to make real-time predictions which tell this is how machine learning is used in data science.
For example, there is an app called Google Lens that analyzes whatever you've clicked, and based on that analysis, it shows you the results. Let's assume you've clicked a photo of a clothing product, so Lens App will recognize the product based on its different characteristics, such as a jacket, jeans, or shirt.
After this analysis or recognition, Lens App will use the machine learning algorithm and shows you the similar products according to the given product photo.
Data science vs. machine learning: Whatâ€™s the difference?
Being a developer, you must distinctly understand the difference between the two widely used technical terms: â€śData Scienceâ€ť and â€śMachine Learningâ€ť. After reading the above-mentioned introduction, you must now go through the head-to-head comparison between the two through the difference table given below.
Basis Of Difference
Data Science
Machine Learning
Field of Study
Data science is centred towards data visualisation, extraction and a better presentation of data with the help of essential tools and libraries.
Machine learning is centred on learning algorithms and using real-time data and experience to predict the future.
Skills Required
StatisticsData mining and cleaningData visualisationData extractionData AnalysisUnstructured data management techniquesProgramming languages such as R Programming Language and PythonUnderstand SQL databasesUse big data tools like Hadoop, Hive and Pig
Computer science fundamentalsStatistical modellingData evaluation and modellingUnderstanding and application of algorithmsNatural language processingData architecture designText representation techniquesNatural Language Processing
Prerequisites
Mathematics and Statistics like Linear Algebra, Calculus, Probability, and Graphical Representation.
Data Science and a target machine.
SQL-based
SQL is an essential requirement for carrying out database operations.
SQL is not required, programming languages such as Python, and Java can be used.
Objective
Dealing with data by extraction, visualisation, cleansing and analysis.
Teaching machines to deal with data by devising algorithms.
Origin of Data
The â€śDataâ€ť understudy may or may not be related to a machine.
The Data undergoes various algorithms such as classification, regression, etc.
Scope of the term
Data Science has a wider scope. It not only deals with algorithms statistics but also includes data processing.
Machine Learning is confined to algorithm statistics.
Universality of the term
It can be used for numerous disciplinaries,
It is used with Data Science only.
Division
It includes all the operations of data science: data extraction, data visualization, data cleansing and data analysis.
It can be classified into three kinds: Unsupervised learning, Reinforcement learning, Supervised learning.
Origin
It is a field that works with some AI concepts and ML tools.
It is a subset of Artificial Intelligence.
Data Types
Structured and Unstructured text
Data normalised as vectors, lists, arrays and embeddings
Data Science is centered on data visualization and a better presentation of data with the help of essential tools and libraries, whereas Machine Learning is centered on learning algorithms and using real-time data and experience to predict the future. So It depends on the requirement of the developer and the project you are working on.
Is data science necessary for machine learning?
As data science is a wider term covering multiple disciplines, machine learning easily comes under data science. Machine learning uses various kinds of algorithms, such as regression, k-means algorithm, classification and supervised clustering. In contrast, the â€śdataâ€ť being studied in data science may or may not be evolved from a machine or a mechanical process.
Should I learn data science or machine learning first?
You will come across data scientists possessing a bachelorâ€™s degree in statistics and machine learning but it is not necessary to learn data science before machine learning. Although, being familiar with the basic concepts of Mathematics and Statistics like Linear Algebra, Calculus, Probability, etc. is essential to learn data science. Machine learning is one of the prime tools which data scientists use to analyse and interpret data.
What should I learn first in data science?
If you intend to become a data scientist, it would be great to start by developing your skill sets such as data cleaning, processing and analysis using data interpretation tools such as the Pandas library, usually included in data science courses.
Conclusion
Finally, after understanding both these terms we can conclude that both Data Science and Machine learning go hand in hand. Machine Learning depends on Data Science for model preparation for training the data set and Data Science can be studied more efficiently by using Machine Learning tools.
If you are thinking of building a career in Data Science or Machine learning you can learn about a few software including R, Python, SQL, this will help you in dealing with data sets better and devising the algorithms efficiently. Before getting enrolled in any course understand the technical terms distinctly, so that you get to learn exactly what you have been looking for.