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Introduction  Data Science vs Machine Learning

AspectData Science Machine Learning DefinitionA multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.A subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that allow computer systems to learn and make predictions or decisions without being explicitly programmed.ScopeBroader scope, encompassing various stages of the data lifecycle, including data collection, cleaning, analysis, visualization, and interpretation.Narrower focus on developing algorithms and models that enable machines to learn from data and make predictions or decisions.GoalExtract insights, patterns, and knowledge from data to solve complex problems and make data-driven decisions.Develop models and algorithms that enable machines to learn from data and improve performance on specific tasks automatically.TechniquesIncorporates various techniques and tools, including statistics, data mining, data visualization, machine learning, and deep learning.Primarily focused on the application of machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning.ApplicationsData science is applied in various domains, such as healthcare, finance, marketing, social sciences, and more.Machine learning finds applications in recommendation systems, natural language processing, computer vision, fraud detection, autonomous vehicles, and many other areas.

What is Data Science? 

Source: DevOps School

What is Machine Learning? 

Computers can now learn without being explicitly programmed, thanks to the field of study known as machine learning. Machine learning uses algorithms to process data without human intervention and become trained to make predictions. The set of instructions, the data, or the observations are the inputs for machine learning. The use of machine learning is widespread among businesses like Facebook, Google, etc. 

Data Scientist vs Machine Learning Engineer

While data scientists focus on extracting insights from data to drive business decisions, machine learning engineers are responsible for developing the algorithms and programs that enable machines to learn and improve autonomously. Understanding the distinctions between these roles is crucial for anyone considering a career in the field.

Data ScientistMachine Learning EngineerExpertiseSpecializes in transforming raw data into valuable insightsFocuses on developing algorithms and programs for machine learningSkillsProficient in data mining, machine learning, and statisticsProficient in algorithmic codingApplicationsUsed in various sectors such as e-commerce, healthcare, and moreDevelops systems like self-driving cars and personalized newsfeedsFocusAnalyzing data and deriving business insightsEnabling machines to exhibit independent behaviorRoleTransforms data into actionable intelligenceDevelops algorithms for machines to learn and improve

What are the Similarities Between Data Science and Machine Learning?

When we talk about Data Science vs Machine Learning, Data Science and Machine Learning are closely related fields with several similarities. Here are some key similarities between Data Science and Machine Learning:

1. Data-driven approach: Data Science and Machine Learning are centered around using data to gain insights and make informed decisions. They rely on analyzing and interpreting large volumes of data to extract meaningful patterns and knowledge.

2. Common goal: The ultimate goal of both Data Science and Machine Learning is to derive valuable insights and predictions from data. They aim to solve complex problems, make accurate predictions, and uncover hidden patterns or relationships in data.

3. Statistical foundation: Both fields rely on statistical techniques and methods to analyze and model data. Probability theory, hypothesis testing, regression analysis, and other statistical tools are commonly used in Data Science and Machine Learning.

4. Feature engineering: In both Data Science and Machine Learning, feature engineering plays a crucial role. It involves selecting, transforming, and creating relevant features from the raw data to improve the performance and accuracy of models. Data scientists and machine learning practitioners often spend significant time on this step.

5. Data preprocessing: Data preprocessing is essential in both Data Science and Machine Learning. It involves cleaning and transforming raw data, handling missing values, dealing with outliers, and standardizing or normalizing data. Proper data preprocessing helps to improve the quality and reliability of models.

Where is Machine Learning Used in Data Science?

In Data Science vs Machine Learning, the skills required for ML Engineer vs Data Scientist are quite similar. 

Skills Required to Become Data Scientist

Exceptional Python, R, SAS, or Scala programming skills

SQL database coding expertise

Familiarity with machine learning algorithms

Knowledge of statistics at a deep level

Skills in data cleaning, mining, and visualization

Knowledge of how to use big data tools like Hadoop.

Skills Needed for the Machine Learning Engineer

Working knowledge of machine learning algorithms

Processing natural language

Python or R programming skills are required

Understanding of probability and statistics

Understanding of data interpretation and modeling.

Source: AltexSoft

Data Science vs Machine Learning – Career Options

There are many career options available for Data Science vs Machine Learning.

Careers in Data Science

Data scientists: They create better judgments for businesses by using data to comprehend and explain the phenomena surrounding them.

Data analysts: Data analysts collect, purge, and analyze data sets to assist in resolving business issues.

Data Architect: Build systems that gather, handle, and transform unstructured data into knowledge for data scientists and business analysts.

Business intelligence analyst: To build databases and execute solutions to store and manage data, a data architect reviews and analyzes an organization’s data infrastructure.

Source: ZaranTech

Careers in Machine Learning

Machine learning engineer: Engineers specializing in machine learning conduct research, develop, and design the AI that powers machine learning and maintains or enhances AI systems.

AI engineer: Building the infrastructure for the development and implementation of AI.

Cloud engineer: Builds and maintains cloud infrastructure as a cloud engineer.

Computational linguist: Develop and design computers that address how human language functions as a computational linguist.

Human-centered AI systems designer: Design, create, and implement AI systems that can learn from and adapt to humans to enhance systems and society.

Source: LinkedIn

Conclusion

Data Science and Machine Learning are closely related yet distinct fields. While they share common skills and concepts, understanding the nuances between them is vital for individuals pursuing careers in these domains and organizations aiming to leverage their benefits effectively. To delve deeper into the comparison of Data Science vs Machine Learning and enhance your understanding, consider joining Analytics Vidhya’s Blackbelt Plus Program.

The program offers valuable resources such as weekly mentorship calls, enabling students to engage with experienced mentors who provide guidance on their data science journey. Moreover, participants get the opportunity to work on industry projects under the guidance of experts. The program takes a personalized approach by offering tailored recommendations based on each student’s unique needs and goals. Sign-up today to know more.

Frequently Asked Questions

Q1. What is the main difference between Data Science and Machine Learning?

A. The main difference lies in their scope and focus. Data Science is a broader field that encompasses various techniques for extracting insights from data, including but not limited to Machine Learning. On the other hand, Machine Learning is a specific subset of Data Science that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions.

Q2. Are the skills required for Data Science and Machine Learning the same?

A. While there is some overlap in the skills required, there are also distinct differences. Data Scientists need strong statistical knowledge, programming skills, data manipulation skills, and domain expertise. In addition to these skills, Machine Learning Engineers require expertise in implementing and optimizing machine learning algorithms and models.

Q3. What is the role of a Data Scientist?

A. The role of a Data Scientist involves collecting and analyzing data, extracting insights, building statistical models, developing data-driven strategies, and communicating findings to stakeholders. They use various tools and techniques, including Machine Learning, to uncover patterns and make data-driven decisions.

Q4. What is the role of a Machine Learning Engineer?

A. Machine Learning Engineers focus on developing and implementing machine learning algorithms and models. They work on tasks such as data preprocessing, feature engineering, model selection, training and tuning models, and deploying them in production systems. They collaborate with Data Scientists and Software Engineers to integrate machine learning solutions into applications.

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List Of Machine Learning Certifications And Best Data Science Bootcamps

Introduction

Every one has a different style of learning. Hence, there are multiple ways to become a data scientist. You can learn from tutorials, blogs, books, hackathons, videos and what not! I personally like self paced learning aided by help from a community – it works best for me. What works best for you?

If the answer to above question was class room / instructor led certifications, you should check out machine learning certifications and data science bootcamps. They offer a great way to learn and prepare you for the role and expectations from a data scientist.

More: 11 things you should know as a Data Scientist

How can this article benefit you?

Global Machine Learning Certifications – This list highlights the widely recognized & renowned certifications in machine learning which can add significant weight to your candidature, thereby increasing your chances to grab a data scientist job.

Data Science Bootcamps – You can think of bootcamps as online / offline classroom training which are held periodically. The motive of these bootcamps is to empower aspiring data scientists with necessary skills & knowledge highly sought by potential employers, in a short duration of time. These are like concentrated shots of learning consumed along with a bunch of fellow (aspiring) data scientists.

Free Resources for Machine Learning – This list highlights the free course material available on machine learning & related concepts. Interesting part is, I have included some resources from the top universities of the world which are not so commonly mentioned, but can turn out to be great if you follow them seriously.

Please note that this is simply a list of best certifications / bootcamps / resources. You should look at them as the best options available and choose what fits you the best. They are not ranked.

Let’s get started!

Global Machine Learning Certifications

This course is provided by University of Washington. It is available in dual (online / offline) format. This course provides hands-on experience of machine learning using open source tools such as R-Studio, scikit-learn, Weka etc. By the end of this course, you’re expected to gain the necessary knowledge required to fulfill business needs from a data scientist.

This course is provided by Stanford Center for Professional Development. It is a graduation certification course which is to be completed in maximum of 3 years. This course is highly suited for candidates having a prior programming experience in C / C++. This course covers the essential modules of AI including logic, knowledge representation, probabilistic models & machine learning.

This certification course is provided by Data Science Institute (Columbia University). This certification offers multiple courses such as algorithms for data science, probability and statistics, machine learning for data science, exploratory data analysis. This course is best suited for candidates having prior knowledge in programming, statistics, linear algebra, probability & calculus.

This certification course is provided by Harvard Extension School. The methodology used in this course is via live web conference using blackboard collaboration. Generally, these classes are arranged on Fridays. This course will begin from 4th September 2024. This is a 15 week long course which covers every essential aspect of machine learning algorithms and precisely explains the logic underlying them.

Udacity offers a comprehensive certification course on machine learning wherein the concepts are aptly explained using interactive practice videos. They have a unique style of explaining things, which might just work for you. The course duration is 4 months. This course closely covers the aspect of supervised, unsupervised & reinforcement learning using real life examples and problems.

Other Machine Learning Courses

You might also be interested to check out the best machine learning PhD, Graduation programs in the world (mostly in US) right now:

11 Best Data Science Boot Camps & Fellowship

The principal motive of these boot camps is ensuring the structured acquisition of data science concepts & knowledge, thereby empowering the participants with necessary skills required by the recruiters. This concept of teaching has rapidly evolved in many countries. The primary reason being, the inability of people to stay focused on self-paced courses and follow every step as instructed. People now look for external support (teacher, mentor, instructor) to monitor their growth and development.

Here I have highlighted the best of all boot camps being organized in the world. I’ve chosen these bootcamps on the basis of enrollment status, placement support, mentors / instructors, curriculum.

P.S. The list is in alphabetical order

This program provides dual ways of enrolling participants i.e. Data Science Cohort & Big Data and Hadoop cohort. The program aims to address the shortage of big data & data science talent in the industry. It provides job placement assistance within a salary range of $75 – $150k. The curriculum of both the courses is designed to focus on the essential aspects of data science & big data with a special focus on statistics and mathematics.

Location: New York

Duration: 4 weeks / 6 weeks

Pre-requisites: Background in SQL, Mathematics, Programming skills

This program offers dual career track such that the candidates enrolling this program have the option of choosing to become a data scientist or a data engineer. This program relishes an amazing support of industry stalwarts. The class size happens to be relatively small which allows the instructor to pay attention to every candidate.

Location: Berlin, Germany

Duration: 3 months

Pre-requisites: Experience in Programming, Databases

This program claims to train data scientists to tackle problems that really matter. This program is provided by University of Chicago. It teaches aspiring data science candidates to learn data mining, machine learning, big data and data science projects and work with non-profits, federal agencies and local governments and make a social impact.

Location: Chicago

Duration: 12 weeks

Pre-requisites: Graduates & Under Graduates

This program teaches you core skills which includes using math & programming skills to make sense out of large data, analyzing and manipulating data using python, fundamental modeling techniques to mention a few. The ultimate aim of this course is to empower students with appropriate knowledge required to make informed decision making at the workplace.

Location: San Francisco / New York

Duration: 11 weeks

Pre-requisites: Good hold on Probability, Statistics, Python, R

This fellowship program intends to bridge the gap between academia and data science being practiced in the industry. This program receives the wide support of industry mentor and follow a pedagogy of project based learning. This course is FREE (you need to take placements through them – what else could you ask for!).

Location: Silicon Valley/ New York, NY

Duration: 7 weeks

Pre-requisites: PhD Degree / Post Doc

The demand of data engineers has increased by 400% in the past 3 years. This fellowship program is designed to match the desired industry skills with skills acquired by candidates in academia. This course is FREE to enroll.

Location: Silicon Valley (CA)

Duration: 6 weeks

Pre-requisites: Knowledge in mathematics, science and software engineering

The key features of this program includes in-person instructions from expert data scientists, career coaching & employment support. By the end of this project, candidates are expected to comfortably design, implement and communicate the results of data science projects creatively.

Location: New York, NY

Duration: 12 weeks

Pre-requisites: Prior knowledge of statistics and programming

This bootcamp provides the much needed acceleration to reach the next level in your data science career path. It teaches real world, practical skills to become a data scientist / data engineer. In addition, the participants also get job search support. This program claims to have a 360 degree view of data science industry needs, and accordingly design the curriculum so that participants can be the best fit for industry needs.

Location: Manhattan, NY

Duration: 12 weeks

Pre-requisites: Experience in Programming, Quantitative discipline

This fellowship is highly applicable for people keen to start their career with startups. This program presumes that data science is more of a skill than just acquiring knowledge which needs to honed by continuous practice. Hence, the candidates attending this program will learn to build real machine learning applications and established data science teams.

Location: San Francisco, CA

Duration: 4 months

Pre-requisites: Software Engineering, Quantitative Analysis, Advanced quantitative degrees

The fellowship program enables you to jumpstart your career in data science. This program is widely supported by industry leaders such as foursquare, the new york times, capital one, microsoft, ebay etc. This program is focused on providing training that links your analytical skills to job opportunities.

Location: New York, NY

Duration: 7 weeks

Pre-requisites: PhD / PostDoc

In this bootcamp, you’ll undergo a structured curriculum which covers the essential aspects of data science. Participants are given real industry problems for practicing data science techniques. The statistics at Zipfian website claims to have 93% placement, $115 average salary in less than 6 months. They also run a 6 week data fellowship.

Location: San Franciso, California

Duration: 12 weeks

Pre-requisites: Quantitative background, familiarity with programming and statistics

Free Resources for Machine Learning

Here you’ll also find resources from the top universities teaching machine learning including cornell, MIT, harvard, carnegie universities. These are self-paced tutorials which includes slides, videos, blogs and what not! These resources are in no order.

1. Machine Learning course by Yaser Abu Mostafa – This is one of the highly recommended course on Machine Learning. Usually, this course is provided on edX, but it has been closed now. It is expected to run again in 2023. You can still check out the course content and learn from them.

2. Machine Learning (Andrew Ng) on Coursera – This course requires no further introduction. If you are in data science, chances are you already know of this course. One of the best course on machine learning for beginners by Andrew Ng. It starts by covering linear regression and progresses towards higher level algorithms. This course is available for FREE!

3. Probabilistic Graphical Models – This course is provided by Stanford University on Coursera. The course instructor is Daphne Koller (co-founder of Coursera). This course teaches you the basics of PGM representation, methods of construction using machine learning techniques.

4. Neural Networks for Machine Learning – This course is provided by University of Toronto on Coursera. The course instructor is Geoffrey Hinton. This course will make you familiar with the applications of machine learning such as artificial intelligence, image recognition, speech recognition, human motion and how they are being used. In this course, Geoff has beautifully explained the basic algorithms & practical tricks to get machine learning working.

5. Scalable Machine Learning –  This course is provided by University of California on edX. This course allows you to learn underlying statistical and algorithmic principles required to develop machine learning pipelines, implementation of scalable algorithms for fundamental statistical models, hands-on experience on Apache Spark.

6. Machine Learning Tutorials – Carnegie Mellon University – Carnegie Mellon University is widely known for its machine learning department. This resource provides tutorial videos & slides from the class of 2011. It consist of Andrew Moore’s tutorials as well. This tutorial focuses on explaining the concepts of supervised, unsupervised and reinforcement learning by building models.

7. Machine Learning Quick Tutorials – Cornell University – Here’s the course material of Fall 2014 in Cornell University. This tutorial attempts to teach machine learning from the scratch using some interesting presentations. This course covers almost all the modules of machine learning. If you think you can’t watch videos to learn these concepts, checking out these presentations should do good for you!

8. MIT Open Course on Machine Learning – This course is provided by Massachusetts Institute of Technology.  If I am not wrong, this course has been archived but you can still access the course material. This tutorial aims to cover the underlying machine learning algorithms, starting from Regression, Classification till higher level concepts such as bayesian networks, collaborative filtering etc. It is available for download in PDF version.

9. Machine Learning Algorithms Tutorial by Andrew Moore – Andrew Moore is the Dean of the School of Computer Science at Carnegie Mellon University. Here are the set of tutorials which covers many aspects of statistical data mining, classical machine learning, foundation of probability to mention a few. These tutorials are available to download in PDF version. I’d highly recommended beginners to follow this tutorial.

10. CSCI E-181 Machine Learning: This course is provided by Harvard Extension School. It consists of video lectures which are focused on machine learning algorithm. Since, not everyone is fortunate enough to get into Harvard, you surely shouldn’t miss the erudite discussions and knowledge being disseminated by Harvard professors in these tutorials. I really admired the pedagogy used by professors in these tutorials.

11. CSCI E-109 Data Science: This course is also provided by Harvard Extension School. I believe these are one of the best video tutorial available on learning data science in Python. The course instructor has beautifully explained such strenuous concepts using interesting examples and viewpoints. I’d recommend beginners to take this course as it covers every underlying aspect of data science and machine learning.

End Notes

In this article, I have strived to provide you the best possible information on machine learning certifications and data science bootcamps. While creating this article, I realized there are more than 20 bootcamps being organized across the world, but later I decided to highlight the best ones in this article. If you’ve attended any bootcamp and got benefited, please share your review below.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

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What Is The Difference Between Cloning And Molecular Biology?

Introduction

Cloning is the process of producing similar populations of genetically identical individuals that occur in nature when organisms such as bacteria, fungi, insects, or plants reproduce asexually. Examples of such organisms are various trees such as hazel trees, blueberry plants, and the American sweetgum. Cloning in biotechnology refers to processes used to create copies of DNA fragments (molecular cloning), cells (cell cloning), or organisms.

The term also refers to the production of multiple copies of a product such as digital media or software. Cloning can be natural or artificial. Examples of cloning that occur naturally are vegetative reproduction in plants, e.g., water hyacinth producing multiple copies of genetically identical plants through apomixis, binary fission in bacteria, and parthenogenesis in certain animals. Clones can also be produced through artificial means.

Making multiple copies by manipulation procedures or biotechnology is artificial cloning such as molecular cloning, where copies of specific gene fragments are produced. Cellular cloning, where single-celled organisms with the exact genetic content of the original cell are produced in cell cultures. Organism cloning, or reproductive cloning, is where a multicellular clone is created generally through somatic cell nuclear transfer.

Molecular biology cloning generally uses DNA sequences from two different organisms. First is the species that is the source of the DNA to be cloned. Second, is the species that will serve as the living host for the replication of the recombinant DNA. Molecular cloning technology is central to many contemporary areas of modern biology and medicine. Long before attempts were made to clone an entire organism, researchers learned how to reproduce desired regions or fragments of the genome, a process that is referred to as molecular cloning.

Plasmids have been repurposed and engineered as vectors for molecular cloning and the large-scale production of important reagents such as insulin and human growth hormone. Molecular cloning has progressed from the cloning of a single DNA fragment to the assembly of multiple DNA components into a single contiguous stretch of DNA.

Molecular Biology Cloning

Molecular cloning is one method in molecular biology that is commonly used to amplify a genetic sequence of interest. This is accomplished by inserting recombinant DNA into a vector which can then carry DNA fragments in host organisms to be amplified

This process of amplification is based on molecular biology standard, first is to recombine the target gene into the vector DNA molecules in vitro. Then transfer the recombinant DNA to host cells. After transferring there is a screening of cells which have expressed the recombinant DNA, after purification and amplification.

Molecular Biology Cloning Technology Process

This is the process by which copies of biomolecules, such as DNAs, are produced. It is used to amplify a particular DNA fragment containing target genes. Apart from the genes (coding sequences), it is also used in making multiple copies of promoters, non-coding sequences, and randomly fragmented DNA. The general steps in molecular cloning are fragmentation, ligation, transfection, screening, or selection.

Isolate the target gene and vector.

Direct separation is suitable for the extraction and separation of bacterial chromosomes, plasmids, and virus DNA whose genetic background are of interest to be studied.

Gene synthesis is used to generate short DNA fragments whose sequence is known clearly.

cDNA can be synthesized by reverse transcription from mRNA.

Screening the gene of from the genomic library for molecular cloning.

The target gene and vector are cleaved with a restriction enzyme: This allows the fragments to be more easily connected later.

The target gene and vector are then ligated with DNA ligase: This seals the connection between target gene and vector.

Transfer the ligated recombinant vector into host cells: Bacteria: E. coli, fungi: yeast, insect cells or mammalian cells.

Conduct screening at different levels using different methods to test for quality: For example: vector size, enzyme digestion results, screening markers and so on.

Applications

Molecular cloning provides scientists with an essentially unlimited quantity of any individual DNA segments derived from any genome. This material can be used for a wide range of purposes, including those in both basic and applied biological science. A few of the more important applications are summarized here.

Genome organization and gene expression

Production of recombinant proteins

Transgenic organisms

Gene therapy

Conclusion

Molecular cloning has progressed from arduously isolating and piecing together two pieces of DNA, followed by intensive screening of potential clones, to seamlessly assembling up to 10 DNA fragments with remarkable efficiency in just a few hours, or designing DNA molecules in silico and synthesizing them in vitro.

Together, all these technologies give molecular biologists an astonishingly powerful toolbox for exploring, manipulating, and harnessing DNA, that will further broaden the horizons of science. Among the possibilities are the development of safer recombinant proteins for the treatment of diseases, enhancement of gene therapy, and quicker production, validation, and release of new vaccines.

Difference Between Information And Data

What is Data?

Data is a raw and unorganized fact that required to be processed to make it meaningful. Data can be simple at the same time unorganized unless it is organized. Generally, data comprises facts, observations, perceptions numbers, characters, symbols, image, etc.

Data is always interpreted, by a human or machine, to derive meaning. So, data is meaningless. Data contains numbers, statements, and characters in a raw form.

What is Information?

Information is a set of data which is processed in a meaningful way according to the given requirement. Information is processed, structured, or presented in a given context to make it meaningful and useful.

It is processed data which includes data that possess context, relevance, and purpose. It also involves manipulation of raw data.

Information assigns meaning and improves the reliability of the data. It helps to ensure undesirability and reduces uncertainty. So, when the data is transformed into information, it never has any useless details.

KEY DIFFERENCE

Data is a raw and unorganized fact that is required to be processed to make it meaningful whereas Information is a set of data that is processed in a meaningful way according to the given requirement.

Data does not have any specific purpose whereas Information carries a meaning that has been assigned by interpreting data.

Data alone has no significance while Information is significant by itself.

Data never depends on Information while Information is dependent on Data.

Data measured in bits and bytes, on the other hand, Information is measured in meaningful units like time, quantity, etc.

Data can be structured, tabular data, graph, data tree whereas Information is language, ideas, and thoughts based on the given data.

Data Vs. Information

Parameters Data Information

Description Qualitative Or QuantitativeVariables which helps to develop ideas or conclusions. It is a group of data which carries news and meaning.

Etymology Data comes from a Latin word, datum, which means “To give something.” Over a time “data” has become the plural of datum. Information word has old French and middle English origins. It has referred to the “act of informing.”. It is mostly used for education or other known communication.

Format Data is in the form of numbers, letters, or a set of characters. Ideas and inferences

Represented in It can be structured, tabular data, graph, data tree, etc. Language, ideas, andthoughts based on the given data.

Meaning Data does not have any specific purpose. It carries meaning that has been assigned by interpreting data.

Interrelation Information that is collected Information that is processed.

Feature Data is a single unit and is raw. It alone doesn’t have any meaning. Information is the product and group of data which jointly carry a logical meaning.

Dependence It never depends on Information It depended on Data.

Measuring unit Measured in bits and bytes. Measured in meaningful units like time, quantity, etc.

Support for Decision making It can’t be used for decision making It is widely used for decision making.

Contains Unprocessed raw factors Processed in a meaningful way

Knowledge level It is low-level knowledge. It is the second level of knowledge.

Characteristic Data is the property of an organization and is not available for sale to the public. Information is available for sale to the public.

Dependency Data depends upon the sources for collecting data. Information depends upon data.

Example Ticket sales on a band on tour. Sales report by region and venue. It gives information which venue is profitable for that business.

Significance Data alone has no signifiance. Information is significant by itself.

Meaning Data is based on records and observations and, which are stored in computers or remembered by a person. Information is considered more reliable than data. It helps the researcher to conduct a proper analysis.

Usefulness The data collected by the researcher, may or may not be useful. Information is useful and valuable as it is readily available to the researcher for use.

Dependency Data is never designed to the specific need of the user. Information is always specific to the requirements and expectations because all the irrelevant facts and figures are removed, during the transformation process.

DIKW (Data Information Knowledge Wisdom)

DIKW is the model used for discussion of data, information, knowledge, wisdom and their interrelationships. It represents structural or functional relationships between data, information, knowledge, and wisdom.

Example:

Difference Between Machine To Machine Vs. Internet Of Things

Although the phrases “Internet of Things” (IoT) & “Machine to Machine” (M2M) are sometimes used indiscriminately, they have different meanings. IoT refers to communication between internet-connected devices, whereas M2M pertains to connection between machines. IoT refers to a system of devices that can gather and exchange data. Thermostats, lightbulbs, automobiles, and heart monitors are just a few of these gadgets. These devices may be remotely controlled, and their data can be utilized to make forecasts, increase efficiency, and boost performance.

On the contrary hand, M2M refers to a system of machines that can interact among themselves outside the internet without the assistance of humans. This communication is frequently used in industrial environments where equipment must communicate data to operate effectively. So, what’s the difference? The following sections below give a quick rundown −

What is the Difference Between IoT and M2M?

Machine to Machine (M2M) and the Internet of Things (IoT) are two phrases that are sometimes used synonymously, although there is a significant distinction between them.

The Internet of Things (IoT) is a collection of tangible things equipped with sensors and other connected devices to gather and share data. On the contrary side, a system of machines defined as M2M communicates with one another to share information and complete tasks.

IoT gadgets often cater to consumers, whereas M2M machines are more industrial. As an illustration, an IoT device may be a smart household appliance like a thermostat or security camera. Still, an M2M machine would be a manufacturing robot or an irrigation system for farming.

The primary distinguishing feature between the two is that IoT devices are designed and intended for human control, but M2M machines are autonomous and often don’t require human interaction.

What are Some Examples of IoT and M2M?

The Internet of Things (IoT) is the interconnection of physical things and gadgets with sensors and electrical components, allowing them to gather and share data. In addition to monitoring and controlling the equipment, this data may also be processed further to give insight into how to increase productivity or accomplish other goals.

M2M, on the other hand, often refers to machine-to-machine (M2M) communication that does not require human interaction. This can involve anything from operating a household appliance from a distance to monitoring machinery in a factory.

Some examples of IoT devices include −

Smart thermostats

Connected cars

Wearables

Smart home devices

Medical devices

Some examples of M2M applications include −

Factory automation

Remote monitoring and control of equipment

Telemetry

Automatic meter reading

Benefits of IoT and M2M

IoT may be used to track and regulate energy usage, saving money for both individuals and enterprises. Additionally, monitoring stock movements and inventory levels in real-time is possible, nullifying the requirement for operator intervention.

M2M technology is frequently employed in commercial settings, including process control and surveillance. We can automate jobs and processes by tying machines and gadgets together, which requires less human involvement. As a result, productivity benefits and enhanced efficiency may result.

Safety can also be increased by using M2M and IoT technology. For instance, we can get real-time notifications in the event of a fire by linking smoke detectors to the world wide web. Thus, evacuation processes can make better use of this information.

The Challenges of IoT and M2M

For businesses, emerging new technology can be the impetus for significant change; it could mean a new revenue stream. But with any new technology, there are always challenges to overcome and risks to mitigate to succeed. There is a distinction between the phrases “machine to machine” (M2M) and “Internet of Things,” even though the two terms are sometimes used interchangeably. The Internet of Things (IoT) is the term used to describe how linked everything is to everything else. Contrarily, M2M refers to machine-to-machine communication, which is conducted without the involvement of a human.

As many devices get online, the Internet of Things (IoT) has experienced phenomenal development in recent years. Due to the increased attack surface that must now be managed and secured, organizations face new issues. Industrial applications where human intervention may be challenging or impossible frequently employ M2M communication. As a result, maintaining upgrades and patching schedules and security and dependability issues may become problematic.

IoT vs. M2M

Basis

IoT

M2M

Type of Connection

Network connection

Point to Point (P2P) connection

Communication Protocol

HTTp, FTP

Traditional

System involved

Internet is mandatory

Internet is not required

API Support

Available

Not Available

Applications

B2B, B2C, Cloud computing

Specialised devices

Examples

Smart Wearables, Big Data

Sensors, Transducers

Conclusion

The Next Domain That Machine Learning Aims To Conquer Is Hard Science

Hard sciences are also being revolutionized by machine learning

From email to the Internet, particle physicists, the hard sciences’ experts, have historically been early users of technology, if not its creators. Therefore, it is not unexpected that researchers began training computer models to tag particles in the chaotic jets produced by collisions as early as 1997. Since then, these models have plodded along, becoming increasingly more capable—although not everyone has been pleased with this development. Particle physicists have taught algorithms to solve previously unsolvable issues and take on entirely new challenges over the past ten years, concurrently with the broader deep-learning revolution.

According to Jesse Thaler, a theoretical particle physicist at the Massachusetts Institute of Technology, “I felt really scared by machine learning.” He claims that at first, he believed that it imperiled his ability to characterize particle jets using human judgment. Thaler, however, has now come to accept it and has used machine learning to solve a number of issues in particle physics. He claims that machine learning is a partner.

To begin with, the data utilized in particle physics differs greatly from the conventional data used in machine learning. Though convolutional neural networks (CNNs) have excelled at categorizing photos of commonplace items like trees, kittens, and food, they’re less good at handling particle collisions. Javier Duarte, a particle physicist at the University of California, San Diego, claims that the issue is that collision data from sources like the Large Hadron Collider isn’t by nature an image. Flashy representations of LHC collisions may deceitfully fill the entire detector. In reality, a white screen with a few black pixels represents the millions of inputs that aren’t actually registering a signal. Although this weakly supplied data produces a subpar image, it can perform well in a newer architecture called graph neural networks (GNNs).

Innovation is needed to overcome additional particle physics problems. According to Daniel Whiteson, a particle physicist at the University of California, Irvine, “We’re not merely importing hammers to smash our nails.” We need to create new hammers because there are strange new types of nails. The enormous volume of data generated at the LHC—roughly one petabyte every second—is one peculiar nail. Only a limited amount of high-quality data is saved from this large volume. Researchers seek to teach a sharp-eyed algorithm to sort better than one that is hard coded in order to develop a better trigger system, which saves as much good data as possible while getting rid of low-quality data. The intention is not to connect the device or the experiment to the network and have it publish the articles without keeping them informed, according to Whiteson. He and his colleagues are attempting to have the algorithms deliver feedback in terms of what people can comprehend, but it’s possible that other individuals have communication duties as well.

However, according to Duarte, such an algorithm would need to execute in just a few microseconds in order to be efficient. Particle physicists are pushing the boundaries of machine techniques like pruning and quantization to accelerate their algorithms in order to solve these issues. Researchers are looking for ways to compress the data because the LHC needs to store 600 petabytes during the next five years of data collecting (equal to about 660,000 movies at 4K resolution or the data equivalent of 30 Libraries of Congresses).

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