Keerthi Ravichandran: What's new?

Using Data Science to Combat Climate Change

Given the new risks and challenges the world is facing due to climate change, developments and advancements in climate science have enormously important economic, business, and societal implications. Emerging initiatives focused on decarbonization, reforming agriculture, and emission reduction have taken priority on urgent sustainability agendas for businesses, governments, and other organizations.

Climate science uses the power of data science to unlock new insights that can aid in evaluating and addressing pressing issues brought on by both naturally occurring climate variables and human-caused climate change, just like every other entity that depends on data to spark transformative momentum. The ultimate objective is to create solutions to prevent, control, and minimize them.

Evolution of Climate Science

The field of climatology, also known as climate science, developed out of weather forecasting in the 1940s and has advanced to include the highly complex earth system models used today. The main objective of climatologists is to understand and respond to climate change and global warming scenarios. They focus on long-term trends and patterns. Researchers from U.C. Berkeley recently presented a study at NASA that attests to the reliability of climate models used over the past 50 years to forecast global warming.

AI has advanced climate modeling, allowing scientists to predict environmental responses to factors like air carbon concentrations that may negatively impact some populations. Australian climate scientist Steven Sherwood is one of the international authors of a widely publicized research report on climate change produced by the World Climate Research Program that addresses the trajectory of global warming via climate sensitivity. This study used the very foundation of data science, advanced statistical techniques, and temperature change data.

New technologies open up new possibilities

A roadmap for how machine learning (ML) can help address risks from global warming in areas like climate prediction and forecasting energy demand is being contributed to climate science by eminent AI researchers. Their report lists a number of technologies that could be used as interventions, including AI, computer vision, NLP, machine learning, and reinforcement learning. This is merely one specific illustration of how the use of intelligent technologies can facilitate advancements in this field. For detailed information on NLP, and other ML techniques, refer to the machine learning course in Pune.

In order to analyze environmental data for vital global climate change initiatives, Microsoft's AI for Earth programme and IBM's Green Horizon Project both use AI and ML. The two tech behemoths are just a few of the many businesses attempting to set standards for how digital technologies can aid in the fight against climate change.

Machine Learning: Machine learning algorithms can be used to track and forecast potentially disastrous weather events.

IoT: Through variables like bee activity, geographic erosion, and water levels, it is possible to identify and quantify the indirect effects of climate change.

Neural networks: Network networks are used to distinguish between human-caused forced changes in precipitation and temperature from natural climate variations.

The Data Disparity

In the field of climate science, data science has a hero role to play because it can lay the foundation for future research, solution development, and education by spotting novel patterns in data. Climate scientists can create visualizations about climate change using data from IoT systems, for instance, to communicate their research better and advance understanding.

The use of AI technologies in climate modeling will become more and more crucial as the time for insights increases. This is based on the fundamental ideas of data science, which collects, interprets, and analyses data using AI technologies to find patterns, make predictions and gain new insights. It's interesting to note that there is now a field called climate informatics that bridges the gap between climate scientists and AI researchers to pursue the promise of technology to answer important questions about climate change.

Conclusion

Climate science influences how we perceive and understand how climate change will impact society, the economy, geography, and business. With the development of technology comes the chance for data scientists to impact our world and tackle some of the most pressing global problems of our time. As a result, data scientists play a key role in the world of climate change. If you’re considering a career change to data science, Head to the top data science course in Pune and become a certified data scientist on top MNCs.

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Data Science and Social Media: An Intersection

In recent years, social media usage has increased dramatically, but never more so than since the COVID-19 outbreak, when lockdowns and other restrictions changed user behavior and habits to become more dependent on technology. Social media usage increased by 10.4% in July 2022 compared to the previous year, and in March 2020, TikTok attracted 12 million unique U.S. visitors. In addition, 46% of women and 42% of men say the pandemic has caused them to spend more time on social media.

While social media and data science have undoubtedly interacted in significant ways, the pandemic has permanently changed the role and significance of analytics in this field. Social media platforms are now the main focus of contemporary business marketing strategies and a playground for real-time trend analysis.

In the upcoming years, social media's trajectory in business development will grow, resulting in related career roles. Web analysts, AI engineers, machine learning engineers, digital marketing experts, social media analysts, and advanced mobile marketers are just a few of the jobs in this field that are already in high demand, so this is the best time for both working professionals and those looking to break into the field to study how data science and social media interactions. You can visit the top data science certification course in Pune, designed in collaboration with IBM.

Increase in Data Sources

A growing body of structured and unstructured data in various formats, including images, videos, sounds, text, and geolocations, has accumulated due to the remarkable increase in social media usage.

As time has passed, social media has developed into a vital tool for gathering and disseminating information in various fields, including journalism, business, politics, and science. This expansion creates new opportunities for pattern discovery and analysis that can shed light on important issues, influences, and market and social changes.

The specific application domain, data source and format, techniques, and objectives often determine the complexity of data discovery, collection, and preparation for analytics and predictive modeling. This involves extensive data science techniques like big data analytics, data mining, machine learning, and AI.

Expanding the use cases

Numerous use cases have repeatedly shown the relationship between data science and social media, influencing business strategies, scientific and medical research, product development, and marketing optimization.

Making Better Decisions
Big data analysis is used by businesses to identify trends in real-time, use these competitive insights to guide decision-making, reduce business risks, and link social data to their bottom line.

Scientific and medical research
Social media is a crucial tool in the healthcare industry for reducing misinformation, providing the public with real-time updates, and increasing awareness of health-related issues. Data science is used by public healthcare organizations, academic researchers, and scientists to benefit from the accessibility and availability of data for learning, collaboration, and research recruitment.

Product Creation
Businesses use ML-driven social listening techniques for intelligent analysis of videos, photos, and content in natural language. This enables them to capture emerging trends or rapidly shift preferences to create the most suitable products for the market.

Enhanced Marketing Strategy
In order to support marketing strategies, social media analytics and big data analytics are used. These tools are used to create contextualized, personalized ads and content based on customer sentiment, track the effectiveness of campaigns, and measure marketing performance KPIs.

Observing the Effects of COVID-19
In order to better inform the public about the facts and dangers of COVID-19, data science techniques are currently being used to track the pandemic's global impact and momentum by examining social media.

Conclusion

Social media has fundamentally changed how traditional forms of communication are used, opening up new opportunities for data science professionals to direct and support everything from scholarly research and health education to digital marketing and service development. Check out the top Data science course in Pune which supports both current and potential career paths for those who want to broaden their skill horizons in data science and social media.

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Why Python Is Crucial for Data Science and Analysis

Introduction
According to its creators, Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Rapid Application Development, as well as a scripting or glue language to connect existing components, find its high-level built-in data structures, dynamic typing, and dynamic binding very appealing.

Python can be used to create both desktop and web applications because it is a general-purpose programming language. Additionally, it aids in the creation of sophisticated mathematical and scientific applications. With this level of adaptability, it should be no surprise that Python is one of the world's programming languages with the fastest growth rate.

Analyses of Data Overview

What exactly does a data analyst do?

It might be simpler to respond to the question of why Python is a good fit if you have a brief review of the duties of a data analyst. You can choose the right tools for the job more effectively the more you comprehend the task at hand.

Data analysts decipher data, apply statistical methods to the results, and produce ongoing reports. They create and implement statistically efficient and high-quality data analyses, data collection systems, and other techniques. Additionally, they are in charge of maintaining databases and collecting data from primary or secondary sources. In addition, they locate, examine, and interpret patterns or trends in large, complex data sets. Data analysts examine computer reports, printouts, and performance indicators to identify and fix code issues. They can clean and filter data by doing this.

Data analysts carry out comprehensive life cycle analyses that cover requirements, tasks, and design and creative capabilities for analysis and reporting. In order to find improvements, they also track performance and quality control plans. They use the outcomes of the tasks and duties described above to better collaborate with management and create priorities for business and information needs.

This list of data-intensive tasks is all necessary to see why having a tool that can handle large amounts of data quickly and easily is essential. Managing vast amounts of data, cleaning it up, and processing it for use is crucial given the spread of big data (which is still increasing). Python is a good fit because it is simple to use and makes it simple to complete repetitive tasks, reducing the time needed to understand how the tool operates. To become a data analyst, explore the trending data analytics course in Pune, and master the tools.

Data Science Vs Data Analysis

It's important first to define the relationship between data analysis and data science because the latter often greatly benefits from the programming language before delving too deeply into why Python is so crucial to data analysis. In other words, many of the factors that make Python effective for data science also make it appropriate for data analysis.

Both fields are on the right and have a lot of overlap while also being quite different. The key distinction between a data analyst and a data scientist is that the former seeks out significant insights from existing data, whereas the latter focuses more on hypothetical scenarios. While data scientists attempt to predict the future and frame those predictions in new questions, data analysts deal with the day-to-day, using data to address the questions presented to them. Or, to put it another way, data scientists extrapolate what might be while data analysts concentrate on the present.

The distinctions between the two fields of study can sometimes become hazy, which is why Python's benefits for data science may also be advantageous for data analysis. For instance, both professions call for proficiency in communication, basic math skills, and an understanding of algorithms, in addition to knowledge of software engineering. Additionally, both professions demand proficiency in programming languages like Python, SQL, and R.

While a data analyst doesn't need to worry about developing that particular skill, a data scientist should ideally have strong business acumen. Instead, data analysts should be adept at using spreadsheet programmes like Excel. The average annual salary for an entry-level data analyst is $60,000, while the median pay for data scientists in the US and Canada is $122,000. The average salary for data science managers is $176,000 per year.

Why is Python Essential for Data Analysis?

Modularity
Python is ideal for you if you want to try something creative that has never been done before. It's perfect for programmers who want to script websites and applications.

Simple To Learn
Because Python prioritizes readability and simplicity, it has a gradual learning curve that is relatively low. Python is a great tool for beginning programmers because it is simple to learn. Python allows programmers to accomplish tasks with fewer lines of code than they would need to when using more traditional languages. In other words, instead of dealing with code, you spend more time playing with it.

Open Source
Python uses a community-based development model and is free because it is open-source. Python can be used in Linux and Windows environments. Additionally, porting it to different platforms is simple. Data manipulation, data visualization, statistics, mathematics, machine learning, and natural language processing are just a few examples of the many open-source Python libraries available (though see below for more about this).

Strong Support
Anything that can go wrong will go wrong, and getting assistance when using something you weren't required to pay for can be very difficult. Fortunately, Python has a sizable user base and is widely used in academic and professional settings, so many practical analytics libraries are available. Stack Overflow, mailing lists, user-contributed code, and documentation are always available to Python users who need assistance. Additionally, as Python grows in popularity, more users will share information about their experiences using the programme, increasing the number of available free support materials. As a result, an increasing number of data analysts and data scientists have begun to accept it. Python's growing popularity is understandable.

Conclusion

Python is a useful tool for data analysts and scientists because it is designed specifically for repetitive tasks and data manipulation. Anyone who has worked with much data knows how frequently repetition occurs. Data analysts can focus on their jobs in more interesting and satisfying aspects because they have a tool to handle tedious tasks. Data analysts also should be aware of the many different Python libraries that are out there. Once you have a firm grasp of the fundamentals of Python, you should consider these libraries, such as NumPy, Pandas, and Matplotlib, which assist the data analyst in carrying out their duties. Interested in making a career in data science and AI? Check out the top notch data science course in Pune, which was co-created with IBM. This certification course covers the most in-demand data science tools through a combination of self-paced learning, instructor-led virtual classes, and hands-on industry projects.

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7 Steps To Building A Career As A Data Scientist Without A Degree

Data science is the most in demand field in today’s decade. There is a very low likelihood that candidates will have an actual degree in data science because it is a relatively new field. Below are 7 essential steps that will enable you to become a data scientist without a degree to make up for that.

Gain Necessary Prerequisite Knowledge
The vast field of data science is derived from several branches of mathematics, computer science, and statistics. There are many books available that can help you learn more about these topics.
Additionally, you can learn about topics like calculus, linear algebra, probability, discrete mathematics, etc. You can learn Python and R, the two most widely used programming languages in the field of data science, to gain knowledge of the fundamentals of computer science.

Learn Data Science Tools and Techniques
Learning data science is the next critical step toward becoming a data scientist. What does Data Science really consist of? Data science is made up of many different elements, including data extraction, data transformation, data cleaning, visualization, and prediction.
Each of these elements needs to be mastered separately. Storytelling is a crucial component of data science. You need to be proficient with a variety of tools in order to learn these skills. For instance, you need to be familiar with tools like ggplot2, seaborn, and matplotlib for visualization.

Get Certified
Although obtaining a certificate is optional for this step, it will only increase your chances of becoming a data scientist. An official certification will demonstrate the data science abilities you have used. Microsoft, Cloudera, SAS, and other organizations provide certifications in data science. If you’re looking for a complete data science bootcamp, an IBM-accredited data science certification course in Pune is the ultimate destination for your career.

Explore real-time Case Studies
Once you have a solid understanding of data science and the various tools used in the process, you should read and research various case studies of how large corporations are using data science to enhance their operations and increase their profits.
More case study research can help you discover challenges to solve and the best way to handle them.

Work on live projects
In the more practical field of data science, you must work on real projects to solve real problems to gain true knowledge. Your data science skills will advance as a result of getting practical experience solving real-world issues. Finding a job in data science as a new graduate can be difficult, so make sure you work on worthwhile live projects and develop your skills.

Build a Portfolio
Your work in the area of data science is displayed in your portfolio. Several Data Science projects can help you improve your portfolio. You can attract the attention of numerous job recruiters by increasing your presence on websites like Github, Linkedin, Kaggle, Tableau Public, etc. You can tailor your portfolio to a particular job. For instance, if your job requires machine learning, you will need to have projects in your portfolio that use machine learning algorithms.
The data analysis portfolio is another type of portfolio that allows you to show off data transformation, cleaning, visualization, etc. The storytelling portfolio, which is a comprehensive project that converts a business issue into data science, is the third category of the portfolio.

Participate in Hackathons
Practicing data science is the best way to learn it. Kaggle is just one of many online venues that enable active participation in data science competitions. You can build your portfolio and add experience to your resume by participating in these competitions. You can thoroughly understand how to apply data science in real-life scenarios by engaging in extensive data cleaning, transformation, analysis, and visualization.

Summary
We underwent a number of steps, including acquiring prerequisite knowledge, learning essential data science skills, earning certificates, constructing a portfolio, and participating in competitions. Without a degree, one can still become a data scientist by following these steps. Do you want to work as a data scientist in MNCs? You can develop your expertise by gaining experience through the best data science course in Pune, which involves learners engaged in real-world projects created by industry experts.

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Data Science Ethics And Its 5 Principles For Businesses

Overview

Making decisions based on data can have a significant impact on businesses. Nevertheless, there are some disadvantages to this beneficial resource. What ethical means can businesses collect, store, and use data? What rights are necessary to safeguard? Personnel in business who handle data must adhere to certain ethical standards. There must be a proper way to use the data and maintain privacy because data is someone's personal information.

Ethics

The Greek word ethos, which means "habit" or "custom," is where the word "ethics" originates. Ethics teaches us the difference between right and wrong. Philosophers have debated this important issue for a very long time, and they have much to say about it. Most people link morality, or a sense of what is "good," to ethics. Humans live in societies, and societies have laws and rules. We must be able to differentiate between right and wrong. Ethics is concerned with the emotions, rules, and social norms that distinguish right and wrong. Our way of life must be reasonable and in line with social norms.

Why Is Ethics in Data Science Important?

Data science is now impacting how industries like medical sciences, smart cities, and transportation conduct business. The risks of data science without ethical considerations are as evident as ever, whether it's the protection of personally identifiable information, implicit bias in automated decision-making, the appearance of free will in psychographics, the social effects of automation, or the apparent separation of truth and trust in virtual communication. Because data science practices threaten our conception of what it means to be human, the need for a focus on data science ethics goes beyond a summary of these potential issues.

When used properly, algorithms have a great deal of potential to improve the world. The advantages could be enormous when we use them to carry out tasks that previously needed a person: cost savings, scalability, speed, accuracy, and consistency, to name a few. Additionally, the results are more balanced and less likely to exhibit social prejudice because the system is more accurate and dependable than humans.

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Data Science Ethics

Data science ethics must concern analysts, data scientists, and IT professionals. Anyone who works with data must be familiar with the basics. Anyone working with any data type must report any instance of data theft, unethical data collection, storage, use, etc.

In the past, protected data has been posted online and made public, harming the people whose information was exposed. Data leaks can result from improperly configured databases, spyware, theft, or publication on a public forum. To address computer and data security, individuals and organizations must adopt safe computing practices, conduct regular system audits, and adopt policies. In order to prevent the leakage of data and information, businesses must implement the necessary cybersecurity measures. This is crucial for banks and other financial institutions that deal with customers' money. Policies require that protections be kept in place even when equipment is transferred or discarded.

5 Key Principles of Data Science Ethics

Making Decisions
Even if the decision is in the project's best interest, data scientists should never make decisions without first consulting the client. Both data scientists and clients need to be aware of the project's goals and objectives.

Let's imagine that a data scientist wants to act for a client on a specific ongoing project. Even if the decision is in the client's and the project's best interests, it must be made with their knowledge and consent. Data scientists make decisions when it is specifically stated in the contract or within the scope of their authority.

Privacy and Confidentiality of Data:
Data scientists are constantly involved in information creation, development, and acquisition. This category frequently includes information about client affiliates, customers, employees, or other parties with whom the clients have a confidentiality agreement. The data scientist must then take care to protect any sensitive information, regardless of the type. This type of information should only be disclosed or discussed when the customer gives consent for it to be done so. Data about clients or customers must be kept completely private.

Even if a customer gives permission for your business to gather, store, and analyze their personally identifiable information (PII), it doesn't necessarily follow that they want it to be made public.

Data Ownership:
The idea that each person has ownership over their data is one of the key ethical principles in data science. It is illegal and immoral to collect someone's personal information without their consent. As a result, permission is needed in order to obtain someone's data.

The typical methods for obtaining consent include:
Signed written agreements.
Digital privacy policies ask users to agree to a company's terms and conditions.
Pop-up windows with checkboxes that permit websites to track users' online activity using cookies.
Never assume a customer agrees to you collecting their data; always ask permission to avoid ethical and legal problems.

Good intentions with data:
Data collection and analysis must be done with good intentions. Data experts must be transparent about the purposes for which they use the data. The goal is good if a team gathers information on users' spending patterns to create an app for budgeting.

Transparency:
In addition to having a right to own their personal information, data subjects have a right to know how they intend to collect, store, and use it. When gathering data, transparency should be used. In addition to developing a policy outlining how cookies are used to track user activity and how the data collected is stored in a secure database, you should also develop an algorithm that provides a customized online experience. Users have a right to access this data to decide whether to accept cookies from your website.

Conclusion

Data science ethics is a crucial topic for debate in the modern world. Companies and organizations using data must adhere to certain ethical standards when working with it. Data can assist you in making better decisions and changing the world if used ethically. Wondering how to learn data science and its tools? You can master your skills with the Best data science course in Pune and participate in real-world projects created by professionals in the field. Become job-ready with its exclusive placement support and 15+ real-time projects.


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Real-Life Applications of Data Science in Education

Introduction

The Data Science sector is booming but still very young and has a lot of potential. By 2026, this industry is projected to grow by 26%, but most of us are still unaware of the field of data science. Let me give you an example when you go somewhere new for the first time. Our GPRS services are currently the most popular source. Therefore, when we turn on the GPRS for our road trip, it uses various data science techniques to find the shortest route, calculate the arrival time, and find places to eat and refuel nearby.

The data science software operating in the background supports all of these operations. Everywhere, the data science team assists organizations in handling, managing, and analyzing their data. To put it simply, data science is a discipline that aids in structuring data through data mining techniques. Influential data scientists can spot the most recent trends in their field, but they also assist organizations in structuring their data and determining how it relates to the deluge of information that is readily available. We need to comprehend why Data Science is the newest global trend in education.

We observed the majority of the students constantly stumbling around the house with their laptops as the COVID-19 pandemic spread throughout the world. . I talked to a few cousins who looked apprehensive about this big shift in their everyday lives, and it took a long time for them to adjust to this "new normal." I recall one of my cousins complaining that she doesn't like learning and misses taking notes in her notebook.

Advantages of Data Science in the Education

Data visualization, data reduction and description, and prediction tasks will all be emphasized by educational data science. Data reduction will make it easier for the students to grade their assignments and submit them.

Students will benefit from learning complex data in a simpler form and being taught in a narrative format through the data visualization process.

Understanding the contextualization of education—what works for whom, when, and where—is essential for schools and other online programme providers. This will keep the program's instructor inspired to develop new teaching strategies.

Applications of Data Science in Education


Socio-emotional Skills
An important area that requires educational development is social-emotional skills. Through this, a child learns to develop the ability to comprehend, consider, express, and manage emotions. He gains knowledge about how to build relationships with other people.

An important responsibility of educational institutions is to support the development of social and emotional skills. This illustrates a non-academic skill that significantly influences how well students can learn. Additionally, data scientists can use various predictive analytical techniques with the extracted data to help teachers understand why their students are motivated to take the course.

Monitoring Student Requirements
Educational institutions make use of a variety of evaluation and assessment techniques. However, these conventional approaches are frequently needed to capture and encapsulate all the significant trends and patterns in student services. Additionally, the majority of the assessment methods needed to be more real-time. Thanks to developments in big data analytics, teachers can now carefully examine their students' needs based on their performance and evaluations. Explore various analytics techniques through a top data analytics course in Pune.

Teachers can respond appropriately and even alter their teaching strategies to meet students' expectations by keeping an eye on their needs. Teachers frequently harbor unintentional prejudices against particular students. A data platform will treat its users impartially, eliminating bias from student performance evaluations. Thanks to this, all students will have an equal opportunity to participate and hone their skills.

Innovating the Curriculum
In order to offer their students the right courses, different universities must stay current with market demands. The universities also struggle to keep up with the expansion of industries. Universities are using Data Science systems to analyze escalating market trends to account for this.

Data science can help analyze industrial patterns and help the course creators to imbibe useful topics by using a variety of statistical measures and monitoring techniques. Universities can also use predictive analytics to identify the need for new skill sets and design courses to meet those needs.

Measuring Instructor Performance
The teachers are responsible for the student's performance. Although numerous assessment methods have been employed, they have typically been manual.

For instance, student evaluations of teachers' performance have long been the go-to resource for measuring instructional strategies. However, all these methods could be more effective and typically require some time to evaluate. In addition, coming up with an analogy and reading student reviews are both taxing tasks.

The development in data science has made it possible to monitor teacher performance. This holds true for both real-time and recorded data. As a result, thorough data collection and analysis are made possible with the real-time observation of teachers. We can also store and manage unstructured data on a big data platform, such as student reviews. Additionally, a thorough analysis of teacher performance can be produced using Natural Language Processing to analyze the reviews' sentiments.

Conclusion
In conclusion, data science techniques are applied to all fields. The use of data science techniques in education benefits students greatly. Every student's academic performance improves as a result. Applying data science in education has opened up new possibilities for improving students' overall performance.

Future generations will benefit greatly from data science in education because it improves student performance while reducing teacher workload. This is a development for our country as well. It increases the proportion of intelligent and productive students in the nation. Are you curious to learn more about data science techniques? Check out the trending data science course in Pune trained by experienced industry experts along with multiple domain-focused data science projects.

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Blockchain & Data Science — The Future Of Technology

Data science and blockchain technology are two of the most innovative and disruptive technologies in use today. Data science examines and explains unprocessed data to comprehend how a system functions. Blockchain technology is a cutting-edge way to track transactions and store financial data. These two ideas have been combined to create amazing innovations in various fields, including finance and software development.

Data Science and Blockchain Technology

Data Science is one of the fields of technology that is expanding quickly. The many scientific subfields that are constantly changing include descriptive analytics, diagnostic analytics, and predictive analytics. The objective is to obtain insights from already collected data, whether structured or not.

A decentralized digital ledger that can store any data is called a blockchain. With the help of blockchain technology, many users can share an encrypted database without a third party controlling it. This makes it possible to store information about transactions between parties in a way that is impervious to tampering.

Integration of Data Science and Blockchain Technology

The basis of blockchain technology is data. In order to address several major industry pain points, data is also essential. For instance, we must examine patterns and trends in previous user behavior and correlate them with current activities to increase transparency and reduce fraud.

Both have had a profound impact on the modern world. For years, data scientists have been researching the use of the blockchain for data storage. The best-known illustration of this is Factom, which most recently collaborated with Microsoft on the Cocoa Framework project. As a result, businesses can store sensitive data on the blockchain at the enterprise level.

Data science in blockchain technology makes sure that transactions are secure and unchangeable. It supports keeping blockchain transactions secure and authentic. Additionally, it can be used to guarantee prompt transaction execution.

By using data science, any suspicious activity on the blockchain network can be found. It can also classify different transactions according to their characteristics, making data collection and analysis easier. This would make it simpler for businesses to locate criminals who use blockchain networks for nefarious activities like money laundering or financing terrorism.

Businesses utilizing blockchain technology's decentralized features for authentication or record-keeping can reap many advantages. When it comes to analyzing the data kept on a blockchain network also poses some difficulties. Because blockchains are distributed, there aren't any centralized servers where users can run queries or run statistical analyses on the data they store. Researchers have created novel methods for performing analytics on blockchains by integrating ideas from disciplines like artificial intelligence (AI), machine learning (ML), and deep learning in order to get around these restrictions (DL). Checkout Learnbay's Machine learning course in Pune and master the ML techniques.

Blockchain Uses Cases in Data Science

Data Integrity:
The accuracy of the stored data guarantees its reliability because it underwent a thorough verification process. The blockchain network also provides transparency because it allows tracking activities and transactions. The majority of the time, data integrity is protected by automatically storing and verifying a data block's transactions and origin on the blockchain.

Ensures high-quality data and accuracy:
Both private and public data are present in every element of the digital ledger used by the blockchain. Before the data is incorporated into various blocks at the entry point, it is cross-checked and analyzed. There is no simpler way to verify data than this.

Allows Data Traceability:
The blockchain makes it simpler for individuals to establish partnerships with one another. If a published account, for instance, does not adequately describe any technique, any peer can examine the entire procedure and determine how the results were obtained.

Real-time analysis:
Analyzing real-time data is very difficult. Keeping an eye out for changes in real-time is the best method for spotting con artists. Due to the distributed nature of blockchain, businesses can identify any discrepancies in their databases right away.

Businesses that require extensive real-time data analysis can benefit from a blockchain-enabled solution. Blockchain technology allows banks and other organizations to quickly detect data changes and take prompt action, such as blocking a suspicious transaction or keeping an eye on abnormal behavior.

Making predictions (Predictive analysis)
Predictive analytics is one of the simplest methods. Blockchain data can be analyzed, just like other types of data, to gain important insights into behaviors and patterns and to foretell future events. Blockchain also provides organized data that has been gathered from people or devices.

Data scientists use predictive analysis to precisely predict social events, such as customer lifetime value, organizational churn rates, and consumer preferences. Because of this, almost any occurrence, including social attitudes and investment signals, can be predicted with the right data analysis.

Conclusion

Despite being relatively young, both industries are expanding quickly hand in hand.
Together, these technologies can help many businesses examine blockchain networks for security, learn more about their users, and start making better decisions about the technology they develop. In conclusion, data science has many potential uses in this brave new world of blockchain technology, and we eagerly anticipate what the future will bring!

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Data Science and AI in Pharmacy — 6 Ways to Drive Innovation & Value

Introduction
Leading pharmaceutical companies, start-ups, and researchers are incorporating Data Science, machine learning (ML) and artificial intelligence (AI) into research and development to analyze large amounts of data, find patterns, and create algorithms to explain them. With less human involvement, AI is getting better at problem-solving, predictive analytics, and innovation. Hence, AI has the potential to bring R&D scale processes to the industrial scale for biopharmaceuticals while also ensuring patient safety.

Finding patterns in data and creating new datasets after using the various techniques as information is directly related to machine learning (ML). The information will be taken into consideration for improvements in the pharmaceutical industry's production of drugs and vaccines, as well as being taken into consideration as a helpful input by scientists for the development, testing, and safety aspect of novel vaccines and drug molecules.

Data Science and AI in Pharmaceutical Companies:
Accelerate drug discovery and development
According to a 2013 Forbes analysis, the cost of bringing a new drug to market is approaching $5 billion, and many blockbuster drug patents have expired or are about to expire. As a result, anything that can speed up the drug discovery and development process will be extremely beneficial. The ability to intelligently search huge data sets of patents, academic articles, and clinical trial data should speed up the discovery of new medicines by allowing researchers to look at the outcomes of earlier tests. They should be able to focus on the important information and gain insight into the potential directions that will produce the best results by applying predictive analytics to the search parameters.

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Optimize and enhance the efficacy of clinical trials
Pharmaceutical companies want to ensure they have the right mix of patients for a given trial because clinical trials are expensive and time-consuming. By analyzing demographic and historical data, big data can help with patient selection for clinical trials, remote patient monitoring, analysis of previous clinical trial outcomes, and even the early detection of potential side effects. According to global management consulting firm McKinsey, big patient data could also assist pharmaceutical firms in considering additional factors, such as genetic data, in helping firms identify niche patient populations to speed up and lower the costs of trials.

Target specific patient populations more effectively
Pharmaceutical companies are better equipped than ever to investigate the underlying causes of particular pathologies and understand that one treatment approach does not, in fact, work for all patients thanks to information from genomic sequencing, medical sensor data (devices that, for example, can be worn and track physical changes in an individual during treatment), and electronic medical records. For a variety of reasons, patients with the same disease or condition will react to treatments differently from one another. Drug companies can develop more specialized treatments for patients who share certain characteristics by sifting through the data from these various sources and identifying trends and patterns.

Better understanding of patient behavior to enhance medicine administration and efficacy
Pharmaceutical manufacturers can now gain much more insight into current patient behavior thanks to increased data that businesses can access, including data from remote sensor devices, combined with sophisticated analytical models. In order to increase treatment effectiveness, the business can use the data to create services catered to various demographics or patient populations who are at risk.

Improve safety and risk management
Pharmaceutical firms have been considering how to use this type of unstructured data more efficiently as signals from various sources, such as social media and Google searches, can act as an early warning signal about issues with product safety.

Gain improved insight into marketing and sales performance
Due to increased competition from generics, Big Pharma is becoming more tricky about analyzing and enhancing effectiveness in its sales and marketing operations. Analyzing data from social media, demographics, electronic medical records, and other data sources can help identify new, niche, and underserved markets. Additionally, pharmaceutical companies can gain an advantage over rivals by analyzing the success of their sales efforts, gathering customer feedback from visits, and effectively utilizing it.

Conclusion
Today's fastest-growing and most in-demand industries are biopharmaceutical and data science, which are driven by need, problem-solving, and knowledge gaps. As a result, the information in this article will assist in tagging the thrush areas of biopharmaceuticals and inspire aspiring researchers and scientists. Those with knowledge of data science tools are employed in any department of the biopharmaceutical industry. Interested in making a career transition to data science and AI?Gain profound knowledge on AI techniques with the best data science course in Pune, where students can engage in real-world projects designed by tech leaders.


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Top 5 Technologies That Enable IIoT and Data Science

Introduction
For many years now, data science has been a hot topic. We all encounter examples of data science's application to the Internet of Things (IoT) daily in our personal and professional lives. The Industrial Internet of Things, or IIoT, is the application of IoT technologies to manufacturing and industrial settings. Data Science has a unique place in this field.

What is IoT?
The network of physical objects known as the Internet of things (IoT) is equipped with electronics, software, sensors, and network connectivity, allowing them to gather, share, and communicate data.

Consumer goods such as connected cars, home automation, wearable tech, connected health devices, and appliances with monitoring capabilities make up a growing portion of IoT devices. The information gathered from these IoT sectors and their activities are essential for businesses and society. For instance, this technology will be used by the medical and healthcare sectors to track and assess patient health. Additionally, transportation technology will require the integration of communication, control, and information processing across the systems.

What is IIOT?
The connectivity between sensors, instruments, and other devices used in manufacturing and energy management is referred to as the Industrial Internet of Things (IIOT). In other words, these gadgets assist with data analysis, operational technology, places, and people by being connected to industrial equipment. As a result, it includes the manufacturing, agricultural, and maritime sectors.

This technology aims to analyze, monitor, and automate the streamlining process to improve it and cut production costs. Cyber-physical systems, Cloud computing, Edge computing, big data, artificial intelligence, and machine learning are some of the technologies that make IIoT possible. We now reach the point where data science is applied.

How is Data Science applied here?
The industrial Internet of things' system configuration is divided into three categories: first, the cloud, where all data is stored, transformed, and analyzed. The second is the network, which is where the devices communicate with one another, and the third is the edge, which manages every machine.

Data science is applied cyclically to address problems. It calls for taking action, comprehending the business need, visualizing the data for any issues, and putting a machine learning model into place.

Cyber-physical
A computer system in which the mechanism is managed or controlled by a computer-based algorithm is referred to as a cyber-physical or intelligent system. In these systems, hardware and software are tightly integrated, enabling communication and cloud data sharing.

Data science is a tremendous asset for finding anomalies, performance problems, or cost-saving detections in the algorithm and the machine. For instance, when keeping track of a cyber-physical process, the algorithm will log data from the hardware and modify its behavior as necessary. However, the hardware or software occasionally veers off course and causes issues throughout the supply chain.

Artificial intelligence
In short, artificial intelligence, also known as AI, is the study of intelligent programming machines to behave like people. A key component of AI called machine learning, or ML, seeks to predict outcomes accurately without explicit programming. IIOT uses AI and machine learning to forecast machinery's performance thanks to big data. Check out Learnbay’s trending Artificial Intelligence Course in Pune.

Cloud Computing
Cloud computing is known as the on-demand availability of computer system resources for storage and processing power without the need to own the hardware. For complex analytics, big data mining, cutting-edge visualization, and long-term data storage, cloud computing is helpful in the IIOT. Furthermore, its data centralization is one of its key benefits. For instance, gathering all the data on a single server in a windmill field would be simpler than downloading it individually from each turbine.

Traditional data storage involves pushing data to a server and having a client pull it back; cloud computing is not appropriate for real-time data where timing is crucial in a supply chain. The Cloud Computing system, on the other hand, is ideal for more complicated analyses that need a lot of computing power. Predictive maintenance, for instance, uses cloud computing to determine when a machine needs to be repaired by the business.

Edge Computing
A distributed computing paradigm known as "edge computing" brings computer data storage closer to the point of need. As opposed to cloud computing, edge computing refers to decentralized data processing at the network's edge.

Real-time data analysis and machinery control are the edge's main benefits. It permits constant data flow, pre-processing/filtering, device-to-device communication, and basic data visualization and analytics. Accessing an edge computing device can quickly diagnose the machinery and ascertain its functionality and efficiency. For instance, a cloud-based solution can more effectively deliver the crucial data required to diagnose the turbine in a windmill field without depending on erratic cellular communications.

Big Data
Big data is analyzing a large amount of data that requires sufficient computing power, whereas cloud computing deals with computing power and storage. Big data is currently used to describe advanced analysis, predictive analysis, or behavior analysis that uses knowledge gleaned from big data.

Because of all the devices connected to the machinery in the industrial environment, big data computing is essential (visual sensors, heat sensors, communication protocols, WIFI, Bluetooth, etc.). It became simpler to forecast the evolution of the machinery (machine failure, maintenance analysis) and its efficiency by analyzing the data provided by the captor (productivity, expected productivity).

Conclusion
Data is produced across various industries daily, particularly in the IoT and industrial settings. Therefore, better tools and technologies are required for businesses and professionals to find, gather, and analyze every insight and anomaly of each process. Thanks to technologies like data science and machine learning, we can now make the most of the data collected by industrial devices.

We examine the ideas behind IoT and IIoT in this article and how data from these concepts can be used to make predictions and identify problems more accurately. Check out India’s top data science course in Pune, crafted in partnership with IBM for working professionals of domains.

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