Table of Contents
Introduction
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, yet there is a wide chain of differences between the two.
First, let’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.
Introduction to Data Science
Data Science refers to the complicated study of the massive amounts of data stored in a company’s or organisation’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 organization’s repository can be grouped into two categories – 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 consumer’s activity and noting their response towards the various price fluctuations and product changes for future references.
Understand the difference between Data Analyst Vs Data Scientist.
Data scientists are experts who put raw data into use for handling crucial business matters. Data Scientists have thorough knowledge about coding paradigms, numerical computation, statistics, graphical representation of data for carrying out data visualisation and extraction.
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The applications of Data Science has 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, healthcare sector and so on.
Introduction to Machine Learning
Machine learning is centred 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.
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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, Skype widely use Machine Learning.
Understand the Four Types of Learnings in Machine Learning Algorithms
Data Science & Machine Learning: A comparison
Being a developer, you must distinctly understand the difference between the two widely used technical terms: “Data Science” and “Machine Learning”. 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.
Difference between Data Science & Machine Learning
| Basis Of Difference | Data Science | Machine Learning |
| Field of Study | Data science is centered 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 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, 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, 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 “Data” 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, array and embeddings |
| Tools Used | R, Python, SAS, Scikit-learn, Keras, SPSS | Programming languages such as Python and Java are used. |
| Applications | Amazon and Netflix(Recommendation System) | Facebook and Google(Suggestions) |
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Frequently Asked Questions
At the first place, data science is centered towards data visualisation and a better presentation of data with the help of essential tools and libraries, whereas machine learning is centered towards learning algorithms and using real-time data and experience to predict the future. Both of them have their own use cases, it depends on the requirement of the developer and the project you are working on.
If you are just working on data extraction and visualisation, data science alone is sufficient. But, if you wish to integrate it with software models then machine learning is also required. The better of the two can be decided based on the developer’s use case.
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 “data” being studied in data science may or may not be evolved from a machine or a mechanical process.
You will come across data scientists possessing a bachelor’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.
If you intend to become a data scientist, it would be great to start by developing your skill set such as data cleaning, processing and analysis using data interpretation tools such as the Pandas library, usually included in data science courses.
No, Python is not dying, it will be the language of the future instead. Developers need to upgrade their skills and get hands-on knowledge about Python for learning AI and ML tools. In its initial years, Python might have not been this popular (it was launched in the year 1991) but it has seen a constant and note-worthy trend of growth in the 21st century.
In comparison to other programming languages SQL is very simple and distinct. It is widely used for carrying out database operations for Python as well as R. Hence, it is recommended that you start with SQL. After learning SQL, it will be better for you to learn any other database manipulation language. SQL is required even if you work on Android Development or iOS development. Hence, you must begin with SQL.
Yes, you can learn Data Science on your own. There are numerous open sources that teach you the dynamics of Data Science all available at Coding Ninjas.
1. Learn Python and Learn SQL
2. Introduction to Data Science Using Python
3. Linear Algebra for Beginners
4. Introduction to Machine Learning for Data Science, Udemy.
5. Machine Learning
6. Data Science Path
Data science derives solutions and results for a particular real-life problem using Artificial Intelligence as a tool. If we consider data science is to insights, then machine learning is to predictions and artificial intelligence is to actions. All three are correlated but they aren’t replaceable. Data Science can’t be automated, at least, for now, human intelligence is of great significance in the field of data science.
Key Takeaways
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.
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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.
By Vanshika Singolia
