- What is Data Science?
- What is Artificial Intelligence?
- What is Machine Learning?
- Difference between AI and Machine Learning
- Difference between machine learning and data science
- Relationship Between Data Science, Artificial Intelligence and Machine Learning
- Difference Between Data Science, Artificial Intelligence and Machine Learning
- Machine Learning vs Data Science Salary
- Data Science, Artificial Intelligence and Machine Learning Jobs
– Are Machine Learning and Data Science the same?
– Which is better, Machine Learning or Data Science?
– Is Data Science required for Machine Learning?
– Who earns more, Data Scientist or Machine Learning Engineer?
– What is the Future of Data Science?
– Can a Data Scientist become a Machine Learning Engineer?
While the terms Data Science, Artificial Intelligence (AI) and Machine learning fall in the same domain and are connected to each other, they have their specific applications and meaning. There may be overlaps in these domains every now and then, but essentially, each of these three terms has unique uses of its own.
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Here is a brief about Data Science vs Machine Learning vs AI in a shorter video version.
What is Data Science?
You must have wondered, ‘What is Data Science?’, Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Data scientists use a combination of tools, applications, principles and algorithms to make sense of random data clusters. Since almost all kinds of organizations today are generating exponential amounts of data around the world, it becomes difficult to monitor and store this data. Data science focuses on data modelling and data warehousing to track the ever-growing data set. The information extracted through data science applications are used to guide business processes and reach organisational goals.
Scope of Data Science
One of the domains that data science influences directly is business intelligence. Having said that, there are functions that are specific to each of these roles. Data scientists primarily deal with huge chunks of data to analyse the patterns, trends and more. These analysis applications formulate reports which are finally helpful in drawing inferences. A Business Intelligence expert picks up where a data scientist leaves – using data science reports to understand the data trends in any particular business field and presenting business forecasts and course of action based on these inferences. Interestingly, there’s also a related field which uses both data science, data analytics and business intelligence applications- Business Analyst. A business analyst profile combines a little bit of both to help companies take data driven decisions.
Data scientists analyse historical data according to various requirements, by applying different formats, namely:
- Predictive causal analytics: Data scientists use this model to derive business forecasts. The predictive model showcases the outcomes of various business actions in measurable terms. This can be an effective model for businesses trying to understand the future of any new business move.
- Prescriptive Analysis: This kind of analysis helps businesses set their goals by prescribing the actions which are most likely to succeed. Prescriptive analysis uses the inferences from the predictive model and helps businesses by suggesting the best ways to achieve those goals.
Data science uses a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc. However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, and more to extract meaning out of sets of data.
Data scientists are skilled professionals whose expertise allows them to quickly switch roles at any point in the life cycle of data science projects. They can work with Artificial Intelligence and machine learning with equal ease. In fact, data scientists need machine learning skills for specific requirements like:
- Machine Learning for Predictive Reporting: Data scientists use machine learning algorithms to study transactional data to make valuable predictions. Also known as supervised learning, this model can be implemented to suggest the most effective courses of action for any company.
- Machine Learning for Pattern Discovery: Pattern discovery is important for businesses to set parameters in various data reports and the way to do that is through machine learning. This is basically unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering.
What is Artificial Intelligence?
AI, a rather hackneyed tech term that is used frequently in our popular culture – has come to be associated only with futuristic-looking robots and a machine-dominated world. However, in reality, Artificial Intelligence is far from that.
Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the right information and self-correction is crucial. AI experts rely on deep learning and natural language processing to help machines identify patterns and inferences.
Scope of Artificial Intelligence
- Automation is easy with AI: AI allows you to automate repetitive, high volume tasks by setting up reliable systems that run frequent applications.
- Intelligent Products: AI can turn conventional products into smart commodities. AI applications when paired with conversational platforms, bots and other smart machines can result in improved technologies.
- Progressive Learning: AI algorithms can train machines to perform any desired functions. The algorithms work as predictors and classifiers.
- Analysing Data: Since machines learn from the data we feed them, analysing and identifying the right set of data becomes very important. Neural networking makes it easier to train machines.
What is Machine Learning?
Machine Learning is a subsection of Artificial intelligence that devices means by which systems can automatically learn and improve from experience. This particular wing of AI aims at equipping machines with independent learning techniques so that they don’t have to be programmed to do so, this is the difference between AI and Machine Learning.
Machine learning involves observing and studying data or experiences to identify patterns and set up a reasoning system based on the findings. The various components of machine learning include:
- Supervised machine learning: This model uses historical data to understand behaviour and formulate future forecasts. This kind of learning algorithms analyse any given training data set to draw inferences which can be applied to output values. Supervised learning parameters are crucial in mapping the input-output pair.
- Unsupervised machine learning: This type of ML algorithm does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. Algorithms with unsupervised learning can use both generative learning models and a retrieval-based approach.
- Semi-supervised machine learning: This model combines elements of supervised and unsupervised learning yet isn’t either of them. It works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive.
- Reinforcement machine learning: This kind of learning doesn’t use any answer key to guide the execution of any function. The lack of training data results in learning from experience. The process of trial and error finally leads to long-term rewards.
Machine learning delivers accurate results derived through the analysis of massive data sets. Applying AI cognitive technologies to ML systems can result in the effective processing of data and information. But what are the key differences between Data Science vs Machine Learning and AI vs ML? Continue reading to learn more.
Difference between AI and Machine Learning
|Artificial Intelligence||Machine Learning|
|AI aims to make a smart computer system work just like humans to solve complex problems||ML allows machines to learn from data so they can provide accurate output|
|Based on capability, AI can be categorized into Weak AI, General AI, and Strong AI||ML can be categorized into Supervised Learning, Unsupervised Learning, and Reinforcement Learning|
|AI systems are concerned with maximizing the chances of success||Machine Learning primarily concerns with accuracy and patterns|
|AI enables a machine to emulate human behavior||Machine Learning is a sub-set of AI|
|Mainly deals with structured, semi-structured, and unstructured data||Deals with structured and semi-structured data|
|Some applications of AI are virtual assistants such as Siri, chatbots, intelligent humanoid robot, etc.||Applications of ML are recommendation system, search algorithms, Facebook auto friend tagging system, etc.|
Difference Between Data Science and Machine Learning
|Data Science||Machine Learning|
|Data Science helps with creating insights from data that deals with real world complexities||Machine Learning helps in accurately predicting or classifying outcomes for new data points by learning patterns from historical data|
– domain expertise
– strong SQL
– ETL and data profiling
– NoSQL systems, Standard reporting, Visualization
– Python/ R Programming
– Strong Mathematics Knowledge
– Data Wrangling
– SQL Model specific visualization
|Horizontally scalable systems preferred to handle massive data||GPUs are preferred for intensive vector operations|
|Components for handling unstructured raw data||Major complexity is with the algorithms and mathematical concepts behind them|
|Most of the input data is in human consumable form||Input data is transformed specifically for the type of algorithms used|
Relationship between Data Science, Artificial Intelligence and Machine Learning
Artificial Intelligence and data science are a wide field of applications, systems and more that aim at replicating human intelligence through machines. Artificial Intelligence represents an action planned feedback of perception.
Perception > Planning > Action > Feedback of Perception
Data Science uses different parts of this pattern or loop to solve specific problems. For instance, in the first step, i.e. Perception, data scientists try to identify patterns with the help of the data. Similarly, in the next step, i.e. planning, there are two aspects:
- Finding all possible solutions
- Finding the best solution among all solutions
Data science creates a system which interrelates both the aforementioned points and helps businesses move forward.
Although it’s possible to explain machine learning by taking it as a standalone subject, it can best be understood in the context of its environment, i.e., the system it’s used within.
Simply put, machine learning is the link that connects Data Science and AI. That is because it’s the process of learning from data over time. So, AI is the tool that helps data science get results and the solutions for specific problems. However, machine learning is what helps in achieving that goal. A real-life example of this is Google’s Search Engine.
- Google’s search engine is a product of data science
- It uses predictive analysis, a system used by artificial intelligence, to deliver intelligent results to the users
- For instance, if a person types “best jackets in NY” on Google’s search engine, then the AI collects this information through machine learning
- Now, as soon as the person writes these two words in the search tool “best place to buy,” the AI kicks in, and with predictive analysis completes the sentence as “best place to buy jackets in NY” which is the most probable suffix to the query that the user had in mind.
To be precise, Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology — Deep Learning.
Deep Learning is a form of machine learning but differs in the use of Neural Networks where we stimulate the function of a brain to a certain extent and use a 3D hierarchy in data to identify patterns that are much more useful.
Difference Between Data Science, Artificial Intelligence and Machine Learning
Although the terms Data Science vs Machine Learning vs Artificial Intelligence might be related and interconnected, each of them are unique in their own ways and are used for different purposes. Data Science is a broad term, and Machine Learning falls within it. Here’s the key difference between the terms.
Data Science Vs. Machine Learning and AI
|Artificial Intelligence||Machine Learning||Data Science|
|Includes Machine Learning.||Subset of Artificial Intelligence.||Includes various Data Operations.|
|Artificial Intelligence combines large amounts of data through iterative processing and intelligent algorithms to help computers learn automatically.||Machine Learning uses efficient programs that can use data without being explicitly told to do so.||Data Science works by sourcing, cleaning, and processing data to extract meaning out of it for analytical purposes.|
|Some of the popular tools that AI uses are-
1. TensorFlow2. Scikit Learn
|The popular tools that Machine Learning makes use of are-1. Amazon Lex2. IBM Watson Studio3. Microsoft Azure ML Studio||Some of the popular tools used by Data Science are-1. SAS2. Tableau3. Apache Spark4. MATLAB|
|Artificial Intelligence uses logic and decision trees.||Machine Learning uses statistical models.||Data Science deals with structured and unstructured data.|
|Chatbots, and Voice assistants are popular applications of AI.||Recommendation Systems such as Spotify, and Facial Recognition are popular examples.||Fraud Detection and Healthcare analysis are popular examples of Data Science.|
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Machine Learning vs Data Science Salary
A Machine Learning Engineer is an avid programmer who helps machines understand and pick up knowledge as required. The core role of a Machine Learning Engineer would be to create programs that enable a machine to take specific actions without any explicit programming. Their main responsibilities consist of data sets for analysis, personalising web experiences, and identifying business requirements. Salaries of a Machine Learning Engineer vs Data Scientist can vary based on skills, experience and companies hiring.
Machine Learning Engineer Salary
|Deloitte||₹ 6,51,000 PA|
|Amazon||₹ 8,26,000 PA|
Salary by Experience
|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 scientists are professionals who source, gather and analyse huge sets of data. Most of the business decisions today are based on insights drawn from analysing data, this is why a Data Scientist is crucial in today’s world. They work on modelling and processing structured and unstructured data, and also work on interpreting the findings into actionable plans for stakeholders.
Data Scientist Salary
|Microsoft||₹ 1,500,000 PA|
|Accenture||₹ 10,55,500 PA|
|Tata Consultancies||₹ 5,94,050 PA|
|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|
This is one of the major differences between Data Scientist vs Machine Learning Engineer.
Data Science, Artificial Intelligence and Machine Learning Jobs
Data Science, Artificial Intelligence and Machine Learning are lucrative career options. However, truth is neither of the fields are mutually exclusive. There’s often an overlap when it comes to the skillset required for jobs in these domains.
Data Science roles such as Data Analyst, Data Science Engineer, and Data Scientist are trending for quite some time. These jobs not only offer great salaries but also a lot of opportunity for growth.
Some Requirements of Data Science associated Roles
- Programming knowledge
- Data visualisation and reporting
- Statistical analysis and math
- Risk analysis
- Machine learning techniques
- Data warehousing and structure
Whether it is report-making or breaking down these reports to other stakeholders, a job in this domain is not limited to just programming or data mining. Every role in this field act as a bridging element between the technological and operational department, it is crucial for them to have excellent interpersonal skills apart from the technical know-how.
Similarly, Artificial Intelligence and Machine Learning jobs are absorbing a huge chunk of talent off the market. Roles such as Machine Learning Engineer, Artificial Intelligence Architect, AI Research Specialist and similar jobs fall into this domain.
Technical Skills required for AI-ML Roles
- Knowledge of programming languages like Python, C++, Java
- Data modelling and evaluation
- Probability and statistics
- Distributed computing
- Machine Learning algorithms
As you can see, the skillset requirement of both the domains overlap. In most cases, courses on data science and AI-ML include basic knowledge on both apart from the focus on the respective specializations.
Even though the areas of data science vs machine learning vs artificial intelligence overlap, their specific functionalities differ and have respective areas of application. The data science market has opened up several services and product industries, creating opportunities for experts in this domain.
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Faqs about Data Science vs Machine Learning and Artificial Intelligence
1. Are Machine Learning and Data Science the same?
Ans: No, Machine Learning and Data Science are not the same. They are two different domains of technology that work on two different aspects of businesses around the world. While Machine Learning focuses on enabling machines to self-learn and execute any task, Data science focuses on using data to help businesses analyse and understand trends. However, that’s not to say that there isn’t any overlap between the two domains. Both Machine Learning and Data Science depend on each other for various kinds of applications as data is indispensable and ML technologies are fast becoming an integral part of most industries.
2. Which is better, Machine Learning or Data Science?
Ans: To begin with, one cannot compare the two domains to decide which is better – precisely because they are two different branches of studies. It is like comparing science and arts. However, one cannot deny the obvious popularity of data science today. Almost all the industries have taken recourse to data to arrive at more robust business decisions. Data has become an integral part of businesses, whether it is for analysing performance or device data-powered strategies or applications. Machine Learning, on the other hand, is still an evolving branch which is yet to be adopted by a few industries which only goes on to say that ML technologies will have more demand relevance in the near future. So, professionals of both these domains will be in equal demands in the future.
3. Is Data Science required for Machine Learning?
Ans: Since both Machine Learning and Data Science are closely connected, a basic knowledge of each is required to specialise in either of the two domains. Having said that, more than data science the knowledge of data analysis is required to get started with Machine Learning. Learning programming languages like R, Python and Java are required to understand and clean data to use it for creating ML algorithms. Most Machine Learning courses include tutorials on these programming languages and basic data analysis and data science concepts.
4. Who earns more, Data Scientist or Machine Learning Engineer?
Ans: Both Data Scientists and Machine Learning Engineers are quite in-demand roles in the market today. If you consider the entry-level jobs, then data scientists seem to earn more than Machine Learning engineers. An average data science salary for entry-level roles is more than 6 LPA, whereas, for Machine Learning engineers, it is around 5 LPA. However, when it comes to senior experts, professionals from both domains earn equally well, averaging around 20 LPA.
5. What is the Future of Data Science?
Ans: Putting it slightly differently – Data Science is the future. No businesses or industries for that matter will be able to keep up without data science. A large number of transitions have already happened worldwide where businesses are seeking more data-driven decisions, more is to follow suit. Data science quite rightly has been dubbed as the oil of the 21st century which can mean endless possibilities across industries. So, if you are keen on pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and fat pay cheques but also a lot of job security.
6. Can a Data Scientist become a Machine Learning Engineer?
Ans: Yes, Data Scientists can become Machine Learning. In fact, it will not be very difficult for data scientists to transition to a Machine Learning career since they would have anyway worked closely on Data Science technologies that are frequently used in Machine Learning. Machine Learning languages, libraries and more are often used in data science applications as well. So data science professionals do not need to put in a humongous amount of effort to make this transition. So yes, with the right kind of upskilling course, data scientists can become machine learning engineers.
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