- Difference Between Machine Learning, Data Science and Artificial Intelligence
- What is Data Science?
- What is the scope of Data Science?
- What is Artificial Intelligence?
- What is the scope of Artificial Intelligence?
- Are Machine Learning and Data Science related?
- What is Machine Learning?
- How are Data Science, Machine Learning, and AI related?
- Data Science Vs Artificial Intelligence Jobs
Difference Between Machine Learning, Data Science and Artificial Intelligence
Even though the terms data science, machine learning, and artificial intelligence (AI) 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 their own.
We will start with the term Data Science, as it assumes the top-most position in the hierarchy of data-related technologies.
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 can be used to guide business processes, which brings us to the next question-
What is the 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 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 lifecycle of data science projects. They can work with AI 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.
Sidetrade, a leading company in the domain of data-science, realized, early-on, the data exploitation challenges its clients faced and immediately set up a dedicated Data Scientist team to work with its Product Managers, aptly put it:
“Data Scientists, of course, have to work closely with IT development teams to guarantee the usability of any solution once it’s in production”
Jean-Cyril Schütterlé VP Product & Data Science, Sidetrade Group
Read Also: Artificial Intelligence and The Human Mind: When will they meet?
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.
What is the 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.
Are Machine Learning and Data Science related?
Artificial Intelligence, much like data science is 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.
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.
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.
How are Data Science, Machine Learning, and AI Related?
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.
Read Also: Difference Between Data Science & Business Analytics
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.
The diagram above is a helpful visual representation of the linkage between AI, Machine Learning and Data Science.
To be precise, Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is 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.
Data Science Vs Artificial Intelligence Jobs
Both Data Science and Artificial Intelligence are lucrative career options. However, truth is neither of the two fields are mutually exclusive. There’s often an overlap when it comes to the skillset required for jobs in these two 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. Let’s take a look at the requisites and demand of a typical data science role.
Data Analyst: The primary tasks of data scientists is to analyse data to understand trends. They use these data insights to help businesses make smart decisions. Some requirements of this role include:
- 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 data analyst’s job is not limited to just programming or data mining. Since data scientists 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 jobs are absorbing a huge chunk of talent off the market. Roles such as Machine Learning Engineer, Artificial Intelligence Architect, AI Research Specialist and more offer great opportunities for AI professionals. A quick look at a sample profile will help you set your expectations from the domain.
Machine Learning Engineer: ML Engineers are primarily responsible for developing and managing platforms for ML projects. A background in data science and applied research is the most conducive for a career as an ML engineer. Following are the technical skills required for the role:
- 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 artificial intelligence include basic knowledge on both apart from the focus on the respective specialisations.
Even though the areas of data science, machine learning and 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. Upskilling in any of these areas will take your career a step ahead. Explore a career in Artificial Intelligence.15