Data Science and Artificial Intelligence

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision-making and strategic planning.

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

The accelerating volume of data sources, and subsequently data, has made data science one of the fastest-growing fields across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review (link resides outside of IBM). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.

While the terms Data Science, Artificial Intelligence (AI), and Machine learning fall in the same domain and are connected, they have specific applications and meanings. There may be overlaps in these domains now and then, but each of these three terms has unique uses.

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 about data systems and processes aimed at maintaining data sets and deriving meaning from them. Data scientists use tools, applications, principles, and algorithms to make sense of random data clusters. Since almost all kinds of organizations generate exponential amounts of data worldwide, monitoring and storing this data becomes difficult. Data science focuses on data modeling and warehousing to track the ever-growing data set. The information extracted through data science applications is used to guide business processes and reach organizational goals.

Great Learning also offers various Data Science Courses and postgraduate programs that you can choose from. Learn from industry experts through online mentorship sessions and dedicated career support.

USE CASE PRODUCTS GOOD FOR
AI for Data Scientists
Vertex AI

 

Our new unified machine learning platform will help you build, deploy and scale more effective AI models.

 

  • Accelerating data preparation
  • Scaling data
  • Training and experimentation
  • Model deployment
Vertex AI Workbench

 

The single development environment for the entire data science workflow.

 

  • Rapid prototyping and model development
  • Developing and deploying AI solutions on Vertex AI with minimal transition.
AI for Developers
AutoML

 

Train high-quality custom machine learning models with minimal effort and machine learning expertise.

 

  • Building custom machine learning models in minutes
  • Training models specific to your business needs
Cloud Natural Language

 

Derive insights from unstructured text using Google machine learning.

 

  • Applying natural language understanding to apps with the Natural Language API
  • Training your open ML models to classify, extract, and detect sentiment
Dialogflow

 

Create conversational experiences across devices and platforms.

 

  • Creating natural interaction for complex multi-turn conversations
  • Building and deploying advanced agents quickly
  • Building enterprise-grade scalability
Media Translation (Beta)

 

Add real-time audio translation to your content and applications.

 

  • Delivering real-time speech translation directly from your audio data
  • Scaling quickly with straightforward internationalization
Speech-to-Text

 

Accurately convert speech into text using an API powered by Google’s AI technologies.

 

  • Creating automatic speech recognition
  • Transcribing in real time
  • Empowering Google Contact Center AI
Text-to-Speech

 

Convert text into natural-sounding speech using an API powered by Google’s AI technologies.

 

  • Improving customer interactions
  • Engaging users with voice user interface in devices and applications
  • Personalizing communication
Timeseries Insights API (Preview)

 

Large-scale time series forecasting and anomaly detection in real time.

 

  • Gathering insights in real time from time series datasets
  • Detecting anomalies while they are happening
  • Handling large scale datasets and running thousands of queries per second
Translation AI

 

Make your content and apps multilingual with fast, dynamic machine translation.

 

  • Delivering seamless user experience with real-time translation
  • Engaging your audience with compelling localization of your content
  • Reaching global markets through internationalization of your products
Video AI

 

Enable powerful content discovery and engaging video experiences.

 

  • Extracting rich metadata at the video, shot, or frame level
  • Creating your own custom entity labels with AutoML Video Intelligence
Vision AI

 

Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect objects, understand text, and more.

 

  • Using ML to understand images with industry-leading prediction accuracy
  • Training ML models to classify images by custom labels using AutoML Vision
AI Infrastructure
Deep Learning Containers

 

Preconfigured and optimized containers for deep learning environments.

 

  • Prototyping your AI applications in a portable and consistent environment
Deep Learning VM Image

 

Preconfigured VMs for deep learning applications.

 

  • Accelerating your model training and deployment
GPUs

 

High-performance GPUs on Google Cloud for machine learning, scientific computing, and 3D visualization.

 

  • Speeding up compute jobs like machine learning and HPC
  • Accelerating specific workloads on your VMs
TensorFlow Enterprise

 

Reliability and performance for AI applications with enterprise-grade support and managed services.

 

  • Boosting enterprise development with long-term support on specific distributions
  • Scaling resources across CPUs, GPUs, and Cloud TPUs
  • Developing and deploying TensorFlow across managed services
TPUs

 

Train and run machine learning models faster than ever before.

 

  • Running cutting-edge machine learning models with AI services on Google Cloud
  • Iterating quickly and frequently on machine learning solutions
  • Building your own ML-powered solutions for real-world use cases.

Scope of Data Science

One of the domains that data science influences directly is business intelligence. Having said that, there are specific functions for each of these roles. Data scientists primarily deal with huge chunks of data to analyze 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, a related field also uses data science, data analytics, and business intelligence applications- Business Analyst. A business analyst profile combines a little bit of both to help companies make data-driven decisions.

Data scientists analyze 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. The prescriptive analysis uses the inferences from the predictive model and helps businesses by suggesting the best ways to achieve those goals.

Data science uses many data-oriented technologies, including SQL, Python, R, Hadoop, etc. However, it also extensively uses 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, and 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 essential for businesses to set parameters in various data reports, and the way to do that is through machine learning. This is unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering.

Data Science Skills

Some Data Science skills include:

  • Programming: R, Python, SQL, SAS, MATLAB, STATA
  • Data Wrangling: Cleaning, Manipulating, and Exploring Data
  • Data Visualization: Creating graphs and charts to visualize data
  • Data Analysis: Conducting statistical analyses of data
  • Machine Learning: Building algorithms to learn from data

What is Artificial Intelligence?

AI, a rather hackneyed tech term 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 correct 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 bright commodities. When paired with conversational platforms, bots, and other intelligent machines, AI applications can improve technologies.
  • Progressive Learning: AI algorithms can train machines to perform any desired functions. The algorithms work as predictors and classifiers.
  • Analyzing Data: Since machines learn from the data we feed, analyzing and identifying the correct data set becomes very important. Neural networking makes it easier to train machines.

Artificial Intelligence Skills

Some artificial intelligence skills include:

  • Data analysis
  • Pattern recognition
  • Machine learning
  • Natural language processing
  • Robotics
  • Predictive modeling
  • Computer vision
  • Expert systems
  • Neural networks

What is data science used for?

Data science is used to study data in four main ways:

1. Descriptive analysis

Descriptive analysis examines data to gain insights into what happened or what is happening in the data environment. It is characterized by data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, a flight booking service may record data like the number of tickets booked each day. Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service.

2. Diagnostic analysis

Diagnostic analysis is a deep-dive or detailed data examination to understand why something happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.For example, the flight service might drill down on a particularly high-performing month to better understand the booking spike. This may lead to the discovery that many customers visit a particular city to attend a monthly sporting event.

3. Predictive analysis

Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.For example, the flight service team might use data science to predict flight booking patterns for the coming year at the start of each year. The computer program or algorithm may look at past data and predict booking spikes for certain destinations in May. Having anticipated their customer’s future travel requirements, the company could start targeted advertising for those cities from February.

4. Prescriptive analysis

Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning.

Back to the flight booking example, prescriptive analysis could look at historical marketing campaigns to maximize the advantage of the upcoming booking spike. A data scientist could project booking outcomes for different levels of marketing spend on various marketing channels. These data forecasts would give the flight booking company greater confidence in their marketing decisions.

What are the benefits of data science for business?

ata science is revolutionizing the way companies operate. Many businesses, regardless of size, need a robust data science strategy to drive growth and maintain a competitive edge. Some key benefits include:

Discover unknown transformative patterns

Data science allows businesses to uncover new patterns and relationships that have the potential to transform the organization. It can reveal low-cost changes to resource management for maximum impact on profit margins.For example, an e-commerce company uses data science to discover that too many customer queries are being generated after business hours. Investigations reveal that customers are more likely to purchase if they receive a prompt response instead of an answer the next business day. By implementing 24/7 customer service, the business grows its revenue by 30%.

Innovate new products and solutions

Data science can reveal gaps and problems that would otherwise go unnoticed. Greater insight about purchase decisions, customer feedback, and business processes can drive innovation in internal operations and external solutions.For example, an online payment solution uses data science to collate and analyze customer comments about the company on social media. Analysis reveals that customers forget passwords during peak purchase periods and are unhappy with the current password retrieval system. The company can innovate a better solution and see a significant increase in customer satisfaction.

Real-time optimization

It’s very challenging for businesses, especially large-scale enterprises, to respond to changing conditions in real-time. This can cause significant losses or disruptions in business activity. Data science can help companies predict change and react optimally to different circumstances.For example, a truck-based shipping company uses data science to reduce downtime when trucks break down. They identify the routes and shift patterns that lead to faster breakdowns and tweak truck schedules. They also set up an inventory of common spare parts that need frequent replacement so trucks can be repaired faster.

What is the data science process?

A business problem typically initiates the data science process. A data scientist will work with business stakeholders to understand what business needs. Once the problem has been defined, the data scientist may solve it using the OSEMN data science process:

O – Obtain data

Data can be pre-existing, newly acquired, or a data repository downloadable from the internet. Data scientists can extract data from internal or external databases, company CRM software, web server logs, social media or purchase it from trusted third-party sources.

S – Scrub data

Data scrubbing, or data cleaning, is the process of standardizing the data according to a predetermined format. It includes handling missing data, fixing data errors, and removing any data outliers. Some examples of data scrubbing are:·

  • Changing all date values to a common standard format.·
  • Fixing spelling mistakes or additional spaces.·
  • Fixing mathematical inaccuracies or removing commas from large numbers.

E – Explore data

Data exploration is preliminary data analysis that is used for planning further data modeling strategies. Data scientists gain an initial understanding of the data using descriptive statistics and data visualization tools. Then they explore the data to identify interesting patterns that can be studied or actioned.

M – Model data

Software and machine learning algorithms are used to gain deeper insights, predict outcomes, and prescribe the best course of action. Machine learning techniques like association, classification, and clustering are applied to the training data set. The model might be tested against predetermined test data to assess result accuracy. The data model can be fine-tuned many times to improve result outcomes.

N – Interpret results

Data scientists work together with analysts and businesses to convert data insights into action. They make diagrams, graphs, and charts to represent trends and predictions. Data summarization helps stakeholders understand and implement results effectively.

What are the data science techniques?

Data science professionals use computing systems to follow the data science process. The top techniques used by data scientists are:

Classification

Classification is the sorting of data into specific groups or categories. Computers are trained to identify and sort data. Known data sets are used to build decision algorithms in a computer that quickly processes and categorizes the data. For example:·

  • Sort products as popular or not popular·
  • Sort insurance applications as high risk or low risk·
  • Sort social media comments into positive, negative, or neutral.

Data science professionals use computing systems to follow the data science process.

Regression

Regression is the method of finding a relationship between two seemingly unrelated data points. The connection is usually modeled around a mathematical formula and represented as a graph or curves. When the value of one data point is known, regression is used to predict the other data point. For example:·

  • The rate of spread of air-borne diseases.·
  •  The relationship between customer satisfaction and the number of employees.·
  • The relationship between the number of fire stations and the number of injuries due to fire in a particular location.

Clustering

Clustering is the method of grouping closely related data together to look for patterns and anomalies. Clustering is different from sorting because the data cannot be accurately classified into fixed categories. Hence the data is grouped into most likely relationships. New patterns and relationships can be discovered with clustering. For example: ·

  • Group customers with similar purchase behavior for improved customer service.·
  • Group network traffic to identify daily usage patterns and identify a network attack faster.
  • Cluster articles into multiple different news categories and use this information to find fake news content.

The basic principle behind data science techniques

While the details vary, the underlying principles behind these techniques are:

  • Teach a machine how to sort data based on a known data set. For example, sample keywords are given to the computer with their sort value. “Happy” is positive, while “Hate” is negative.
  • Give unknown data to the machine and allow the device to sort the dataset independently.
  •  Allow for result inaccuracies and handle the probability factor of the result.

What are different data science technologies?

Data science practitioners work with complex technologies such as:

  1. Artificial intelligence: Machine learning models and related software are used for predictive and prescriptive analysis.
  2. Cloud computing: Cloud technologies have given data scientists the flexibility and processing power required for advanced data analytics.
  3. Internet of things: IoT refers to various devices that can automatically connect to the internet. These devices collect data for data science initiatives. They generate massive data which can be used for data mining and data extraction.
  4. Quantum computing: Quantum computers can perform complex calculations at high speed. Skilled data scientists use them for building complex quantitative algorithms.

Data science is an all-encompassing term for other data-related roles and fields. Let’s look at some of them here:

What is the difference between data science and data analytics?

While the terms may be used interchangeably, data analytics is a subset of data science. Data science is an umbrella term for all aspects of data processing—from the collection to modeling to insights. On the other hand, data analytics is mainly concerned with statistics, mathematics, and statistical analysis. It focuses on only data analysis, while data science is related to the bigger picture around organizational data.In most workplaces, data scientists and data analysts work together towards common business goals. A data analyst may spend more time on routine analysis, providing regular reports. A data scientist may design the way data is stored, manipulated, and analyzed. Simply put, a data analyst makes sense out of existing data, whereas a data scientist creates new methods and tools to process data for use by analysts.

What is the difference between data science and business analytics?

While there is an overlap between data science and business analytics, the key difference is the use of technology in each field. Data scientists work more closely with data technology than business analysts.Business analysts bridge the gap between business and IT. They define business cases, collect information from stakeholders, or validate solutions. Data scientists, on the other hand, use technology to work with business data. They may write programs, apply machine learning techniques to create models, and develop new algorithms. Data scientists not only understand the problem but can also build a tool that provides solutions to the problem.It’s not unusual to find business analysts and data scientists working on the same team. Business analysts take the output from data scientists and use it to tell a story that the broader business can understand.

What is the difference between data science and data engineering?

Data engineers build and maintain the systems that allow data scientists to access and interpret data. They work more closely with underlying technology than a data scientist. The role generally involves creating data models, building data pipelines, and overseeing extract, transform, load (ETL). Depending on organization setup and size, the data engineer may also manage related infrastructure like big-data storage, streaming, and processing platforms like Amazon S3.Data scientists use the data that data engineers have processed to build and train predictive models. Data scientists may then hand over the results to the analysts for further decision making.

What is the difference between data science and machine learning?

learning?Machine learning is the science of training machines to analyze and learn from data the way humans do. It is one of the methods used in data science projects to gain automated insights from data. Machine learning engineers specialize in computing, algorithms, and coding skills specific to machine learning methods. Data scientists might use machine learning methods as a tool or work closely with other machine learning engineers to process data.

What is the difference between data science and statistics?

Statistics is a mathematically-based field that seeks to collect and interpret quantitative data. In contrast, data science is a multidisciplinary field that uses scientific methods, processes, and systems to extract knowledge from data in various forms. Data scientists use methods from many disciplines, including statistics. However, the fields differ in their processes and the problems they study.

What is Machine Learning?

Machine Learning is a subsection of Artificial intelligence that devices mean by which systems can automatically learn and improve from experience. This particular wing of AI aims to equip machines with independent learning techniques so that they don’t have to be programmed. 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 behavior and formulate future forecasts. This learning algorithm analyzes any training data set to draw inferences that can be applied to output values. Supervised learning parameters are crucial in mapping the input-output pair.
  • Unsupervised machine learning: This ML algorithm does not use classified or labeled parameters and focuses on discovering hidden structures from unlabeled data to help systems infer a function correctly. Algorithms with unsupervised learning can use both generative learning models and a retrieval-based approach.
  • Semi-supervised machine learning: This model combines supervised and unsupervised learning elements, yet neither of them exists. It works by using both labeled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labeling data is 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, and 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 critical differences between Data Science vs. Machine Learning and AI vs. ML? Continue reading to learn more. You can also take a Python for Machine Learning course and enhance your knowledge of the concept.

Machine Learning Skills 

Some machine learning skills include:

  • Ability to identify patterns in data
  • Ability to build models to make predictions
  • Ability to tune model parameters to optimize performance
  • Ability to evaluate models for accuracy
  • Ability to work with large data sets

Difference between AI and Machine Learning

Artificial Intelligence Machine Learning
AI aims to make an intelligent computer system work 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 subset 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 robots, etc. Applications of ML are recommendation systems, search algorithms, Facebook auto friend tagging systems, etc.

Difference Between DS and ML

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
Preferred skillset:
– domain expertise
– strong SQL
– ETL and data profiling
– NoSQL systems, Standard reporting, Visualization
Preferred skillset:
– 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 Significant complexity is with the algorithms and mathematical concepts behind them.
Most of the input data is in a 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 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 that interrelates the points above 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 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. Still, it 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 is unique and is used for different purposes. Data Science is a broad term, and Machine Learning falls within it. Here’s the critical difference between the terms.

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
3. Keras
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.

Explore all the free courses at Great Learning Academy, get machine learning certificates for free, and learn in-demand skills.

Machine Learning vs. Data Science Salary

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 is to create programs that enable a machine to take specific actions without any explicit programming. Their primary responsibilities include data sets for analysis, personalizing web experiences, and identifying business requirements. Salaries of a Machine Learning Engineer and a Data Scientist can vary based on skills, experience, and company hiring.

Machine Learning Engineer Salary

Company Salary
Deloitte  ₹ 6,51,000 PA
Amazon ₹ 8,26,000 PA
Accenture ₹15,40,000 PA

Salary by Experience

Experience Level Salary
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 analyze vast data sets. Most business decisions today are based on insights drawn from data analysis, which is why a Data Scientist is crucial in today’s world. They work on modeling and processing structured and unstructured data and also work on interpreting the findings into actionable plans for stakeholders.

Data Scientist Salary

Company Salary
Microsoft ₹ 1,500,000 PA
Accenture ₹ 10,55,500 PA
Tata Consultancies ₹ 5,94,050 PA
Experience Level Salary 
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 significant differences between a Data Scientist and a Machine Learning Engineer.

Data Science, Artificial Intelligence, and Machine Learning Jobs

Data Science, Artificial Intelligence, and Machine Learning are lucrative career options. However, the truth is neither of the fields is mutually exclusive. There’s often overlap regarding the skillset required for jobs in these domains.

Data Science roles such as Data Analyst, Data Science Engineer, and Data Scientist have been trending for quite some time. These jobs offer excellent salaries and a lot of growth opportunities.

Some Requirements of Data Science-associated Roles.

 

  • Programming knowledge
  • Data visualization 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 is a bridging element between the technical and operational departments. They must have excellent interpersonal skills apart from 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 modeling and evaluation
  • Probability and statistics
  • Distributed computing
  • Machine Learning algorithms

As you can see, the skillset requirement of both domains overlap. In most cases, courses on data science and AIML include basic knowledge of both, apart from focusing on the respective specializations.

Even though data science vs. machine learning vs. artificial intelligence overlap, their specific functionalities differ and have respective application areas. The data science market has opened up several services and product industries, creating opportunities for experts in this domain.

What are the Applications of Artificial Intelligence?

Here is the list of the top 18 applications of AI (Artificial Intelligence):

1. AI Application in E-Commerce

Personalized Shopping

Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your customers. These recommendations are made in accordance with their browsing history, preference, and interests. It helps in improving your relationship with your customers and their loyalty towards your brand.

AI-Powered Assistants

Virtual shopping assistants and chatbots help improve the user experience while shopping online. Natural Language Processing is used to make the conversation sound as human and personal as possible. Moreover, these assistants can have real-time engagement with your customers. Did you know that on amazon.com, soon, customer service could be handled by chatbots?

Fraud Prevention

Credit card frauds and fake reviews are two of the most significant issues that E-Commerce companies deal with. By considering the usage patterns, AI can help reduce the possibility of credit card fraud taking place. Many customers prefer to buy a product or service based on customer reviews. AI can help identify and handle fake reviews.

2. Applications Of Artificial Intelligence in Education

Although the education sector is the one most influenced by humans, Artificial Intelligence has slowly begun to seep its roots into the education sector as well. Even in the education sector, this slow transition of Artificial Intelligence has helped increase productivity among faculties and helped them concentrate more on students than office or administration work.

Some of these applications in this sector include:

Administrative Tasks Automated to Aid Educators

Artificial Intelligence can help educators with non-educational tasks like task-related duties like facilitating and automating personalized messages to students, back-office tasks like grading paperwork, arranging and facilitating parent and guardian interactions, routine issue feedback facilitating, managing enrollment, courses, and HR-related topics.

Creating Smart Content

Digitization of content like video lectures, conferences, and textbook guides can be made using Artificial Intelligence. We can apply different interfaces like animations and learning content through customization for students from different grades.

Artificial Intelligence helps create a rich learning experience by generating and providing audio and video summaries and integral lesson plans.

Voice Assistants

Without even the direct involvement of the lecturer or the teacher, a student can access extra learning material or assistance through Voice Assistants. Through this, printing costs of temporary handbooks and also provide answers to very common questions easily.

Personalized Learning

Using top AI technologies, hyper-personalization techniques can be used to monitor students’ data thoroughly, and habits, lesson plans, reminders, study guides, flash notes, frequency or revision, etc., can be easily generated.

3. Applications of Artificial Intelligence in Lifestyle

Artificial Intelligence has a lot of influence on our lifestyle. Let us discuss a few of them.

Autonomous Vehicles

Automobile manufacturing companies like Toyota, Audi, Volvo, and Tesla use machine learning to train computers to think and evolve like humans when it comes to driving in any environment and object detection to avoid accidents.

Spam Filters

The email that we use in our day-to-day lives has AI that filters out spam emails sending them to spam or trash folders, letting us see the filtered content only. The popular email provider, Gmail, has managed to reach a filtration capacity of approximately 99.9%.

Facial Recognition

Our favorite devices like our phones, laptops, and PCs use facial recognition techniques by using face filters to detect and identify in order to provide secure access. Apart from personal usage, facial recognition is a widely used Artificial Intelligence application even in high security-related areas in several industries.

Recommendation System

Various platforms that we use in our daily lives like e-commerce, entertainment websites, social media, video sharing platforms, like youtube, etc., all use the recommendation system to get user data and provide customized recommendations to users to increase engagement. This is a very widely used Artificial Intelligence application in almost all industries.

4. Applications of Artificial Intelligence in Navigation

Based on research from MIT, GPS technology can provide users with accurate, timely, and detailed information to improve safety. The technology uses a combination of Convolutional Neural Networks and Graph Neural Networks, which makes lives easier for users by automatically detecting the number of lanes and road types behind obstructions on the roads. AI is heavily used by Uber and many logistics companies to improve operational efficiency, analyze road traffic, and optimize routes.

5. Applications of Artificial Intelligence in Robotics

Robotics is another field where Artificial Intelligence applications are commonly used. Robots powered by AI use real-time updates to sense obstacles in its path and pre-plan its journey instantly.

It can be used for:

  • Carrying goods in hospitals, factories, and warehouses
  • Cleaning offices and large equipment
  • Inventory management

6. Applications of Artificial Intelligence in Human Resource

Did you know that companies use intelligent software to ease the hiring process?

Artificial Intelligence helps with blind hiring. Using machine learning software, you can examine applications based on specific parameters. AI drive systems can scan job candidates’ profiles, and resumes to provide recruiters an understanding of the talent pool they must choose from.

7. Applications of Artificial Intelligence in Healthcare

Artificial Intelligence finds diverse applications in the healthcare sector. AI applications are used in healthcare to build sophisticated machines that can detect diseases and identify cancer cells. Artificial Intelligence can help analyze chronic conditions with lab and other medical data to ensure early diagnosis. AI uses the combination of historical data and medical intelligence for the discovery of new drugs.

Become an AI and ML Expert with Purdue & IBM!

Professional Certificate Program in AI and MLEXPLORE PROGRAM

8. Applications of Artificial Intelligence in Agriculture

Artificial Intelligence is used to identify defects and nutrient deficiencies in the soil. This is done using computer vision, robotics, and machine learning applications, AI can analyze where weeds are growing. AI bots can help to harvest crops at a higher volume and faster pace than human laborers.

9. Applications of Artificial Intelligence in Gaming

Another sector where Artificial Intelligence applications have found prominence is the gaming sector. AI can be used to create smart, human-like NPCs to interact with the players.

It can also be used to predict human behavior using which game design and testing can be improved. The Alien Isolation game released in 2014 uses AI to stalk the player throughout the game. The game uses two Artificial Intelligence systems – ‘Director AI’ that frequently knows your location and the ‘Alien AI,’ driven by sensors and behaviors that continuously hunt the player.

10. Applications of Artificial Intelligence in Automobiles

Artificial Intelligence is used to build self-driving vehicles. AI can be used along with the vehicle’s camera, radar, cloud services, GPS, and control signals to operate the vehicle. AI can improve the in-vehicle experience and provide additional systems like emergency braking, blind-spot monitoring, and driver-assist steering.

11. Applications of Artificial Intelligence in Social Media

Instagram

On Instagram, AI considers your likes and the accounts you follow to determine what posts you are shown on your explore tab.

Facebook

Artificial Intelligence is also used along with a tool called DeepText. With this tool, Facebook can understand conversations better. It can be used to translate posts from different languages automatically.

Twitter

AI is used by Twitter for fraud detection, for removing propaganda, and hateful content. Twitter also uses AI to recommend tweets that users might enjoy, based on what type of tweets they engage with.

12. Applications of Artificial Intelligence in Marketing

Artificial Intelligence (AI) applications are popular in the marketing domain as well.

  • Using AI, marketers can deliver highly targeted and personalized ads with the help of behavioral analysis, and pattern recognition in ML, etc. It also helps with retargeting audiences at the right time to ensure better results and reduced feelings of distrust and annoyance.
  • AI can help with content marketing in a way that matches the brand’s style and voice. It can be used to handle routine tasks like performance, campaign reports, and much more.
  • Chatbots powered by AI, Natural Language Processing, Natural Language Generation, and Natural Language Understanding can analyze the user’s language and respond in the ways humans do.
  • AI can provide users with real-time personalizations based on their behavior and can be used to edit and optimize marketing campaigns to fit a local market’s needs.

13. Applications of Artificial Intelligence in Chatbots

AI chatbots can comprehend natural language and respond to people online who use the “live chat” feature that many organizations provide for customer service. AI chatbots are effective with the use of machine learning and can be integrated in an array of websites and applications. AI chatbots can eventually build a database of answers, in addition to pulling information from an established selection of integrated answers. As AI continues to improve, these chatbots can effectively resolve customer issues, respond to simple inquiries, improve customer service, and provide 24/7 support. All in all, these AI chatbots can help to improve customer satisfaction.

14. Applications of Artificial Intelligence in Finance

It has been reported that 80% of banks recognize the benefits that AI can provide. Whether it’s personal finance, corporate finance, or consumer finance, the highly evolved technology that is offered through AI can help to significantly improve a wide range of financial services. For example, customers looking for help regarding wealth management solutions can easily get the information they need through SMS text messaging or online chat, all AI-powered. Artificial Intelligence can also detect changes in transaction patterns and other potential red flags that can signify fraud, which humans can easily miss, and thus saving businesses and individuals from significant loss. Aside from fraud detection and task automation, AI can also better predict and assess loan risks.

15. AI in Astronomy

If there’s one concept that has caught everyone by storm in this beautiful world of technology, it has to be – AI (Artificial Intelligence), without a question. AI or Artificial Intelligence has seen a wide range of applications throughout the years, including healthcare, robotics, eCommerce, and even finance.

Astronomy, on the other hand, is a largely unexplored topic that is just as intriguing and thrilling as the rest. When it comes to astronomy, one of the most difficult problems is analyzing the data. As a result, astronomers are turning to machine learning and Artificial Intelligence (AI) to create new tools. Having said that, consider how Artificial Intelligence has altered astronomy and is meeting the demands of astronomers.

  • Recently, a group of scientists used Artificial Intelligence in a galaxy merger investigation to establish that galaxy mergers were the primary force underlying starbursts. Given the size of the collection, the researchers created a deep learning system that trained itself to locate merging galaxies. According to one of the astronomers, the advantage of Artificial Intelligence is that it improves the study’s repeatability. The reason for this is that the algorithm’s definitions of a merger are consistent.
  • The changing sky has captured everyone’s attention as one of the most astounding projects of all time. This project seeks to survey the whole night sky every night, gathering over 80 terabytes of data in one go to study how stars and galaxies in the cosmos change over time.
  • One of the most important duties for an astronomer is to find a p. The theory is that whenever an exoplanet passes in front of its parent star, part of the light is blocked, which humans can see. Astronomers use this location to study an exoplanet’s orbit and develop a picture of the light dips. They then identify the planet’s many parameters, such as its mass, size, and distance from its star, to mention a few. However, AI proves to be more than a savior in this case. Using AI’s time-series analysis capabilities, it is feasible to analyze data as a sequential sequence and identify planetary signals with up to 96% accuracy.
  • Finding the signals of the universe’s most catastrophic events is critical for astronomers. When exoplanets collide with each other, they cause ripples in space-time. These can be identified further by monitoring feeble signals on Earth. Collaborations on gravitational-wave detectors – Ligo and Virgo have performed admirably in this regard. Both of them were effective in recognizing signals using machine learning. Astronomers now get notifications, allowing them to point their telescopes in the appropriate direction.

16. AI in Data Security

Many people believe that Artificial Intelligence (AI) is the present and future of the technology sector. Many industry leaders employ AI for a variety of purposes, including providing valued services and preparing their companies for the future.

Data security, which is one of the most important assets of any tech-oriented firm, is one of the most prevalent and critical applications of AI. With confidential data ranging from consumer data (such as credit card information) to organizational secrets kept online, data security is vital for any institution to satisfy both legal and operational duties. This work is now as difficult as it is vital, and many businesses deploy AI-based security solutions to keep their data out of the wrong hands.

Because the world is smarter and more connected than ever before, the function of Artificial Intelligence in business is critical today. According to several estimates, cyberattacks will get more tenacious over time, and security teams will need to rely on AI solutions to keep systems and data under control.

  • Identifies Unknown Threats

A human may not be able to recognize all of the hazards that a business confronts. Every year, hackers launch hundreds of millions of assaults for a variety of reasons. Unknown threats can cause severe network damage. Worse, they can have an impact before you recognize, identify, and prevent them.

As attackers test different tactics ranging from malware assaults to sophisticated malware assaults, contemporary solutions should be used to avoid them. Artificial Intelligence has shown to be one of the most effective security solutions for mapping and preventing unexpected threats from wreaking havoc on a corporation.

  • Flaw Identification

AI assists in detecting data overflow in a buffer. When programs consume more data than usual, this is referred to as buffer overflow. Aside from the fault caused by human triggers breaking crucial data. These blunders are also observable by AI, and they are detected in real-time, preventing future dangers.

AI can precisely discover cybersecurity weaknesses, faults, and other problems using Machine Learning. Machine Learning also assists AI in identifying questionable data provided by any application. Malware or virus used by hackers to gain access to systems as well as steal data is carried out via programming language flaws.

  • Threat Prevention

Artificial Intelligence technology is constantly being developed by cyber security vendors. In its advanced version, AI is designed to detect flaws in the system or even the update. It’d instantly exclude anybody attempting to exploit those issues. AI would be an outstanding tool for preventing any threat from occurring. It may install additional firewalls as well as rectify code faults that lead to dangers.

  • Responding to Threats

It’s something that happens after the threat has entered the system. As previously explained, AI is used to detect unusual behavior and create an outline of viruses or malware. AI is currently taking appropriate action against viruses or malware. The reaction consists mostly of removing the infection, repairing the fault, and administering the harm done. Finally, AI guarantees that such an incident does not happen again and takes proper preventative actions.

  • Recognize Uncharacterised Action

AI allows us to detect unusual behavior in a system. It is capable of detecting unusual or unusual behavior by continually scanning a system and gathering an appropriate amount of data. In addition, AI identifies illegal access. When unusual behavior is identified, Artificial Intelligence employs particular elements to determine whether it represents a genuine threat or a fabricated warning. Machine Learning is used to help AI determine what is and is not aberrant behavior. Machine Learning is also improving with time, which will allow Artificial Intelligence to detect even minor anomalies. As a result, AI would point to anything wrong with the system.

17. AI in Travel and Transport

Intelligent technology has become a part of our daily lives in recent years. And, as technology advances across society, new uses of AI, notably in transportation, are becoming mainstream. This has created a new market for firms and entrepreneurs to develop innovative solutions for making public transportation more comfortable, accessible, and safe.

Intelligent transportation systems have the potential to become one of the most effective methods to improve the quality of life for people all around the world. There are multiple instances of similar systems in use in various sectors.

  • Heavy Goods Transportation

Truck platooning, which networks HGV (heavy goods vehicles), for example, might be extremely valuable for vehicle transport businesses or for moving other large items.

The lead vehicle in a truck platoon is steered by a human driver, however, the human drivers in any other trucks drive passively, just taking the wheel in exceptionally dangerous or difficult situations.

Because all of the trucks in the platoon are linked via a network, they travel in formation and activate the actions done by the human driver in the lead vehicle at the same time. So, if the lead driver comes to a complete stop, all of the vehicles following him do as well.

  • Traffic Management

Clogged city streets are a key impediment to urban transportation all around the world. Cities throughout the world have enlarged highways, erected bridges, and established other modes of transportation such as train travel, yet the traffic problem persists. However, AI advancements in traffic management provide a genuine promise of changing the situation.

Intelligent traffic management may be used to enforce traffic regulations and promote road safety. For example, Alibaba’s City Brain initiative in China uses AI technologies such as predictive analysis, big data analysis, and a visual search engine in order to track road networks in real-time and reduce congestion.

Building a city requires an efficient transformation system, and AI-based traffic management technologies are powering next-generation smart cities.

  • Ride-Sharing

Platforms like Uber and OLA leverage AI to improve user experiences by connecting riders and drivers, improving user communication and messaging, and optimizing decision-making. For example, Uber has its own proprietary ML-as-a-service platform called Michelangelo that can anticipate supply and demand, identify trip abnormalities like wrecks, and estimate arrival timings.

  • Route Planning

AI-enabled route planning using predictive analytics may help both businesses and people. Ride-sharing services already achieve this by analyzing numerous real-world parameters to optimize route planning.

AI-enabled route planning is a terrific approach for businesses, particularly logistics and shipping industries, to construct a more efficient supply network by anticipating road conditions and optimizing vehicle routes. Predictive analytics in route planning is the intelligent evaluation by a machine of a number of road usage parameters such as congestion level, road restrictions, traffic patterns, consumer preferences, and so on.

Cargo logistics companies, such as vehicle transport services or other general logistics firms, may use this technology to reduce delivery costs, accelerate delivery times, and better manage assets and operations.

18. AI in Automotive Industry

A century ago, the idea of machines being able to comprehend, do complex computations, and devise efficient answers to pressing issues was more of a science fiction writer’s vision than a predictive reality. Still, as we enter the third decade of the twenty-first century, we can’t fathom our lives without stock trading and marketing bots, manufacturing robots, smart assistance, virtual travel agents, and other innovations made possible by advances in Artificial Intelligence and machine learning. The importance of Artificial Intelligence and machine learning in the automotive sector cannot be overstated.

With Artificial Intelligence driving more applications to the automotive sector, more businesses are deciding to implement Artificial Intelligence and machine learning models in production.

  • Manufacturing

Infusing AI into the production experience allows automakers to benefit from smarter factories, boosting productivity and lowering costs. AI may be utilized in automobile assembly, supply chain optimization, employing robots on the manufacturing floor, improving performance using sensors, designing cars, and in post-production activities.

  • Supply Chain

The automobile sector has been beset by supply chain interruptions and challenges in 2021 and 2022. AI can also assist in this regard. AI helps firms identify the hurdles they will face in the future by forecasting and replenishing supply chains as needed. AI may also assist with routing difficulties, volume forecasts, and other concerns.

  • Passenger and Driver Experience

We all wish to have a pleasant journey in our vehicles. Artificial Intelligence can also help with this. When driving, Artificial Intelligence (AI) may assist drivers in remaining focused by decreasing distractions, analyzing driving behaviors, and enhancing the entire customer experience. Passengers can benefit from customized accessibility as well as in-car delivery services thanks to AI.

  • Inspections

The procedure of inspecting an automobile by a rental agency, insurance provider, or even a garage is very subjective and manual. With AI, car inspection may go digital, with modern technology being able to analyze a vehicle, identify where the flaws are, and produce a thorough status report.

  • Quality Control

Everyone desires a premium vehicle and experience. Wouldn’t you prefer to know if something is wrong with your automobile before it breaks down? In this application, AI enables extremely accurate predictive monitoring, fracture detection, and other functions.

Is Artificial Intelligence Limiting Human Application?

Humans may appear to be swiftly overtaken in industries where AI is becoming more extensively incorporated. However, humans are still capable of doing a variety of complicated activities better than AI. For the time being, tasks that demand creativity are beyond the capabilities of AI computers.

For instance, a writer utilized AI to generate fresh screenplays for episodes of the TV sitcom ‘Friends’, all of which proved hard to read. While some authors are turning to AI writing tools for inspiration for their poetry and fiction, AI cannot handle all of the jobs for them.

Furthermore, while natural language processing has advanced significantly, AI is still not very adept at truly understanding the words that it reads. While language is frequently predictable enough that AI can participate in trustworthy communication in specific settings, unexpected phrases, irony, or subtlety might confound it. Similarly, AI cannot yet demonstrate empathy or critical reasoning. In comparison to AI, humans continue to excel in tasks that demand these talents.

AI has advanced swiftly and has become an essential component of our daily life. However, the day when AI will do everything for us and we will be able to spend our days resting or learning for pleasure is still a long way off. Artificial Intelligence still has to advance in areas such as language processing, creativity, problem-solving, and subtlety comprehension. If you’re concerned that robots will take your job, build these qualities in yourself to ensure that you’ll be employable in the future.

FAQs

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 worldwide. While Machine Learning focuses on enabling machines to self-learn and execute any task, Data science focuses on using data to help businesses analyze and understand trends. However, that’s not to say there isn’t any overlap between the two domains. Machine Learning and Data Science depend on each other for various applications as data is indispensable, and ML technologies are fast becoming integral to 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 study. It is like comparing science and arts. However, one cannot deny the apparent popularity of data science today. Almost all industries have recourse to data to make more robust business decisions. Data has become an integral part of businesses, whether for analyzing performance or device data-powered strategies or applications. On the other hand, Machine Learning is still an evolving branch that 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 in both these domains will be in equal demand 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 specialize in either of the two domains. 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 fundamental data analysis and data science concepts.

4. Who earns more, Data Scientist or Machine Learning Engineer?

Ans: Data Scientists and Machine Learning Engineers are 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. Many transitions have already happened worldwide where businesses seek more data-driven decisions, and more are to follow suit. Data science has rightly 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 a fulfilling career, fat pay cheques, and a lot of job security.

6. Can a Data Scientist become a Machine Learning Engineer?

Ans: Yes, Data Scientists can become Machine Learning. It will not be challenging for data scientists to transition to a Machine Learning career since they would have worked closely on Data Science technologies frequently used in Machine Learning. Machine Learning languages, libraries, and more are also often used in data science applications. So data science professionals do not need to put in a humongous amount of effort to make this transition. So yes, with the right upskilling course, data scientists can become machine learning engineers.