by Anika D | Sep 27, 2022 | Data Science
Big data is a major component in the tech industry, thanks to the actionable results businesses obtain. However, such data sets require understanding and proper tools to uncover the right information. To better understand big data, data science, and data analytics, both fields have gone from large academia rather than becoming an integral business intelligence element.
However, it isn’t easy to differentiate between data analytics and data science. Though they are interconnected, they give different results and follow different approaches. If you want to study your business data, it is important to grasp what they bring to the table and how both are unique. To optimize your big data analytics, examine the main differences between data science and data analytics.
What is Data Science?
Data science is a versatile field focused on determining actionable insights from large raw and structured data sets. This field primarily directs on unearthing answers to what you don’t know. Data science professionals use different techniques to get answers, incorporating computer science, statistics, predictive analytics, and machine learning to determine through massive datasets.
The main goals of data scientists are to ask queries and find potential avenues of study with minimum concern about specific concerns. Experts accomplish this by forecasting possible trends and exploring disconnected data sources.
What is the Meaning of Data Analytics?
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Data analytics concentrates on performing statistical analysis on existing datasets. Analysts focus on making processes to store, process, and organize data to uncover actionable results for current problems. More simply, the data analytics field directs toward solving problems to which we don’t know the answers. More importantly, it produces results that can lead to immediate improvements.
Difference Between Data Science and Data Analytics
The following are some main differences between data science and data analytics:
The basic distinctions in both add to their fundamental objectives. Analytics is inclined towards understanding the business-oriented data to obtain insights that you can turn into action. Whereas data science is tuned toward finding answers to suitable business queries that were not addressed before.
Data analysis focuses on answering questions for improved decision-making in businesses. It uses available data for unearthing actionable results. On the other hand, data science focuses on finding new questions that have come up till now and their answers too.
Data analytics focuses on putting historical data into context, whereas data science emphasizes machine learning and predictive modeling. It is a multidisciplinary process covering algorithms alike. Both data analytics and science need data tapping through diverse methods. Data scientists usually deal with raw data and use a fusion of mathematical, statistical, and machine-learning methods for processing and interpreting data to get results.
They tailor advanced methods for data modeling with models, ML-based algorithms, prototypes, and custom analysis. Data analysts study datasets for picking trends and conclusions and collect large volumes of data for organizing and analyzing the same. They recognize relevant patterns and present findings through suitable visualization, like graphs and charts. Data Science uses C++, Python, Java, and Perl as common languages, but data analytics needs knowledge of R and Python.
Another difference between data science and data analytics is their skill requirements. Here are some skills needed for data analytics:
- Excel, SQL database
- Intermediate statistics
- BI tools such as Power BI for reporting purposes
- A Hadoop-based analysis is used to get conclusions from unstructured data
- Skills in databases, statistics, modeling, and predictive analytics are beneficial for aspiring data analysts
Skills needed for data science include:
- Mathematics
- Advanced Statistics
- Predictive modeling
- Machine learning
- In-depth knowledge of programming
- Data Visualization with QlikView, D3.js, Tableau, and other tools
- SQL, NoSQL databases such as MongoDB and Cassandra
- Programming languages like R, Scala, and Python
Differences Between Data Science and Data Analytics Using an Example
Data analytics focuses mainly on intermediate statistics and excellence in problem-solving skills. Here are some the examples of using data analytics:
- Improving the medical care quality through electronic health record systems and digitized healthcare system
- Stopping cybercriminals in their tracks by strengthening the detection of threats
- Developing reasonable products warranties
Data science mainly focuses on machine learning, advanced statistics, mathematics, programming, and predictive modeling. Here are some examples of channeling data science in business expansion:
- In the manufacturing field, by forecasting the product demand
- To optimize the supply chain in the field of logistics
Which Career is Right for you?
Data analysts and data scientists both job titles are deceptively similar, given the differences in responsibilities, educational requirements, or career trajectory. However, once you consider factors like your personal interests, background, and expected salary, you can decide the right career for you and move on to your path to success.
by Anika D | Sep 24, 2022 | Artificial Intelligence
Computers are becoming more efficient in making fake faces using a technology called generative adversarial network (GAN). This technology enables propagandists to hide behind computer-generated personas without any baggage. GAN is good at imitating people, but some features still trouble them. As a result, there are many artificially generated pictures with signature glitches. Here are some things to look for when recognizing an image produced by a GAN and identify fake AI – generated faces:
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Straight Hair Appears Like Paint
Usually, long hair takes a hyper-straight look where a small patch seems good. But long strands look like someone smudged the acrylic with a huge brush or a palette knife.
Asymmetrical Jewelry or Facial Features
Suppose a big weird blob is floating off the top of the person’s head in a picture. Such kind of aberration or artifact is common in artificial intelligence generated images. You can also check asymmetry in a photo by looking around the ear of the person. You will notice fuzzy hair, and sometimes also there are missing earrings.
Indecipherable Text
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AI-generated faces have difficulty capturing things in the background with some structure. Also, GANs have seen original and mirrored versions of the data, meaning they have trouble modeling writing. Finally, it is due to the fact that it only appears in one orientation.
Teeth
Algorithms don’t know the orientation or number of teeth. So instead, it draws on examples of teeth from different angles. Sometimes, the algorithm messes with that and leaves its creations with mangled and odd teeth. So it might be a little more difficult to spot than a disfigured or missing earring. But, if you look at the image closely, you can find some weirdness, especially around the ears.
Surreal Background
GAN-created faces look believable because all the training data has been centered. It means there is less variability for the GAN to the model while placing and rendering eyes and ears. On the other hand, the background can contain anything. It is enough for the GAN to model; thus, it replicates general background-like textures, not the “real” scenes.
Non-Stereotypical Gender Presentation
GAN has a collection of 200k images of above 10k celebrity faces. In this dataset, GAN regularly mixes different features from stereotypical gender presentations. More generally, it is because GANs don’t learn the same categories that humans socially reinforce. Therefore, it is vital to be clear that if asymmetry and non-stereotypical gender presentation is not inherently an indicator, an image will not be “real.”
Strange Backgrounds or Clothing
Sometimes, patterns are a mystery for face-generating algorithms, leading to strange structures in the image background or weird clothing on the subject. Therefore, watch out for text in the background of the image since it’s always malformed. Though the image’s subject will look stunning, it will appear in some geometric jade prison.
To Sum Up
Hopefully, the tips mentioned above will help you identify fake AI – generated faces make you start to question things you see in different ways. In addition, it will force you to corroborate evidence even when you see an image that looks human.
by Anika D | Sep 20, 2022 | Information Technology
In recent years, technological advancements have changed the medical field. The days of manually filing the records are decreasing. With emerging technologies in electronic recordkeeping and telehealth, patients can easily access their data. They also improve efficiency and effective communication in the nursing field. Let us know more about how emerging technologies are impacting nursing practices.
Benefits of Nursing Technology
The advancements in technology and latest devices have enhanced the quality of life for healthcare professionals and patients in the following ways:
Better Accessibility
Electronic Health Records (EHRs) have changed the healthcare space entirely. EHR is a digital version of patients’ medical history, including progress notes, problems, providers, lab data, medications, etc. EHRs can improve patient care by enhancing the accuracy of medical records and making easy data accessible to doctors and patients.
In addition, advancements in telehealth have played a large part in better accessibility. Telecommunication systems make it easy for patients separated geographically to get nursing care through live video conferencing, remote patient monitoring, and mobile health apps. So, you can easily access quality care even in remote geographic areas these days.
Decreased Human Error
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Emerging technologies can lessen the chances of human errors. For example, nurses working long hours are at higher risk of committing mistakes. In addition, with the latest medical technologies, routine procedures have become simple. For instance, smart beds can help nurses to check weight and track movement and vitals. Or automated IV pumps can measure medication dosage for patients. Moreover, EHRs can reduce bedside mistakes as data is readily available.
Positive Impact on Nursing Shortage
Nurse burnout is an important factor in the shortage of nurses. Prolonged physical and mental exhaustion makes nurses feel stretched thin, leading them to leave their practice setting. Emerging technologies in telehealth can lighten the nurse’s burden since it takes fewer nurses to offer adequate care.
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In addition, telehealth allows nurses to reach geographical areas with a shortage of health professionals. It includes locations in rural regions that lack healthcare professionals. Telehealth helps address these issues by remotely providing effective care to patients.
Potential Drawbacks of Nursing Technology
The rapid advancement of technology in healthcare has identified potential obstacles that many hospital systems face. Some of them are as follows:
Threat to Humans
Recent technology can threaten to replace direct interaction between patient and nurse. Nurses play the role of making a relationship with patients and their families. They are also responsible for explaining medications and taking vitals. In some hospitals, nurses record the patient’s information. Though typing information on a computer seems more trustworthy to healthcare professionals and patients, it will inevitably create less person-to-person interaction.
Data and Security Threats
EHRs kept in the form of big data or the cloud is susceptible to being hacked. Hospital cyber crimes are common as patient details can be sold on the black market. Also, the entire EHR system can be at risk if an employee accidentally taps on malware. Hospitals with a data breach can pay hefty fines based on the severity.
Final Words
The emerging technology in nursing is a future wave in the healthcare industry. Advanced technologies in AI, EHRs, software, and app development are becoming popular as more hospitals integrate them into the health system. Though there are some drawbacks, it is clear that information technology has great potential to improve the quality of life.
by Anika D | Sep 14, 2022 | Blockchain
Are you a smart contract developer? If yes, then your work can be tedious. It is because blockchain technology is not only about cryptocurrency. You can use it to design everything, starting from the voting system to medical record keeping. However, only a few applications of blockchain technology have jumped from theory to a functional prototype till now. But first of all, let us learn about smart contracts and their benefits:
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What are Smart Contracts?
Smart contracts are software development programs executed on the basis of specific logic and agreements. These programs run on localized networks whose records cannot be tampered with or changed after registering transactions. So, they are a reliable way to perform transactions requiring transparency, trust, and anonymity.
Benefits of Smart Contracts
The key benefits of smart contracts are as follows:
- Autonomous: Smart contracts offer automation which means no third party can make changes in decisions or agreements.
- Secured: Smart contracts enable processes to complete securely. Also, encryption makes them work as intended.
- Interruption Free: If smart contracts start execution, they cannot be interrupted or stopped.
- Trustless: There is no need to trust other parties to carry out a transaction. It does not require trust as an integral part.
- Cost-Effective: Smart contracts make trades cost-effective with the removal of intermediaries.
- Fast Performance: Smart contracts get executed faster than the old-fashioned traditional method. All the parameters are pre-defined within the smart contracts, so it only needs to match them before execution.
- Accurate: Smart contracts are error-free. The only thing is that you need to code them correctly.
10 Practical Examples of Smart Contracts on Blockchain Technology
Here are some of the examples of smart contracts development:
Smart contracts can develop a strong B2C relationship in real time. For instance, suppose there is a shoe brand in partnership with a streaming music service. It gives a complimentary subscription time if the buyer creates a playlist for listening while they jog.
A smart contract also sends customers a discount on new shoes and suggests similar tempo songs to add to the playlist. In addition, it can enhance customer expectations by supporting runners who like tracking fitness and listening to music.
Another useful smart contract practical example is security. You can simplify or improve the capitalization table management with smart contracts. It means that there is a channel between the parties, including security custody chains. You can also use it for automatic payments, dividends, stock splits, and liability management. In addition, contracts can allow you to make workflows digitized and reduce operational risk.
Nearly all industries worldwide can use smart contracts to improve the speed and security of record-keeping. One of these industries that stands to benefit a lot is the healthcare industry. The global healthcare computer systems currently hold trillions of patients’ medical records. Though these healthcare organizations have invested a significant amount in security, current storage methods are far more vulnerable to cyber crimes than their blockchain-based equivalents.
You can securely encrypt or keep the databases of personal health records using blockchain technology. In addition, this technology facilitates using a private key, meaning only certain individuals can access the data. Some other blockchain smart contracts examples include storing receipts, issuing prescriptions, storing test results, general stock management, etc.
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Fill the Gap in Entertainment Consumption
Blockchain technology can improve consumers’ interaction with their preferred entertainment choices. For instance, non-fungible tokens (NFTs) authenticate ownership of digital assets. A smart contract can simplify the selling, buying, and trading of NFTs. Moreover, there is interest in using smart contracts to pay independent creators like filmmakers, authors, and musicians. This automation eliminates the need for intermediaries for processing royalty payments.
You can revolutionize trade finance with the help of smart contracts. Undoubtedly, it can allow trade payment initiations and international goods transfer by using a Letter of Credit. In addition, smart contracts can also improve the liquidity and efficiencies of financial assets.
It is important to find the industry standards and implement them accordingly to make smart contracts work in trade finance. It happens especially in international trade and cross-border payments. You can solve the legal complications and provide a better way to solve problems among parties with proper integration.
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Maximize Productivity for Human Resources
You can automate an HR manager’s workflow via distributed ledger technology. For instance, an HR employee can perform reference checks and confirm employment history. A smart contract can ease the onboarding of employees by simplifying verification tasks. In addition, blockchain technology can automate responsibilities like enforcing employee contract terms, penalties and paycheck processing.
Smart contracts have two significant examples when it comes to the property market. Firstly, you can use them to record property ownership. The use of smart contracts is more cost-efficient and faster, so it makes them a better alternative to existing systems. It also means you can use them to record the ownership of all properties from land, buildings, phones, and watches.
Within the housing sector, smart contracts can eliminate the need for expensive services like those provided by housing brokers and lawyers. This technology also means that sellers can handle the transactions by themselves for the first time ever.
Some areas of supply chain management can benefit from enterprise blockchain. Smart contracts can increase the product’s traceability and materials. For example, blockchain software can track the origin of items as it moves between international supply chains by calculating tariffs. Some organizations are also exploring smart contracts on blockchain technology. In such cases, blockchain has the potential to minimize errors and increase efficiency.
Another example of smart contracts is keeping a record of the product development stages. Two parties will sign the contract to activate it. For example, you can record the stages, and other relevant information to the smart contract with the development of a project. If the parties agree to such things as split payments, the agreement will initiate their release when reaching the milestones.
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Efficient Utility Distribution
The opportunity to use blockchain technology is growing in the energy industry. For instance, it can automate electricity delivery from an energy firm to a consumer. Executing smart contracts can simplify energy trading by connecting small energy producers. Also, a smart contract can certify renewable energy sources. Blockchain can process and record transactions, making implementation promising.
The Bottom Line
Are you interested in adopting smart contracts of blockchain technology for your businesses? If yes, it is better to look for the potential of smart contracts. The only limitation is your imagination. Smart contracts offer a unique way of solving all business problems. So, it is best to explore them asap.
by Anika D | Sep 9, 2022 | Artificial Intelligence
When you think about Artificial Intelligence, the first thing that comes to mind is robots that can service your every whim. So, the future of AI will revolutionize the way of working. AI is already present in the workplace. For example, you can use AI while using Google Maps to find the way for an off-site meeting. You can also use it to spell-check the prepared project reports. However, AI’s current state and future go far beyond simple mundane tasks. so, let’s discover if can robots are the new employees and a lot more!
AI and Robots
The answer to whether can robots be the new employees lies in their work. A robot is a device made to perform specific tasks automatically and better than humans. Though robots are not exactly human-like talking figures, you can get them designed to act like humans. AI-enabled robots use the principle of reinforcement learning. It means they can make their optimal decisions based on past experiences.
AI robots can do repetitive work and determine ways to improve it. They learn everything themselves and decide their actions based on complex calculations. For example, robots can enhance the productivity of repetitive work in the manufacturing field.
Robotic Process Automation (RPA) means robots can integrate human interactions and perform specific processes like humans. Such robots can learn business processes and interpret human communications.
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Why Does AI Replace Some Jobs?
Many organizations implement AI technology to perform different tasks. AI not only has a limit to assembly lines. But also, AI job disruption takes place in many sectors like sales and customer support. AI is replacing certain jobs due to the following factors:
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AI Robots Show more Accurate Results than Humans
Humans can make errors, but robots don’t. Computers can perform exact processes for which they are programmed and execute them perfectly. Thus AI-enabled robots can perfectly do the tasks like copying, typing, transcribing, or pasting.
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Robots Do Not Require Breaks
Robots can deliver quality work efficiently if they have a battery. In contrast, humans perform their jobs differently all the time. Moreover, lack of sleep, hunger, or boredom can affect human productivity.
Many industries are dangerous for workers; for example, mining, construction, etc. They involve a lot of risks and circumstances that can affect workers. In such conditions, the AI robot can work effectively and reduce the risks. It can withstand toxic fumes and intense heat easily.
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Robots Offer Cost Savings
Though investing in AI technology is not cheap, it can be much more affordable in the long run. Once you get the machine, it just needs power and maintenance. Moreover, you don’t have to pay monthly wages and invest in training. As a result, businesses save a lot of money in the long run.
Which Sectors Will AI Change?
AI has changed almost every sector, but its future promises to revolutionize more businesses. Let us know some of them.
- Health Care: Using Artificial Intelligence, doctors can better diagnose illnesses and make lifesaving treatment more likely. They might use AI to find new ways to treat problems like dementia and cancer.
- Service Industry: In the future, AI machines can replace cashiers, customer service representatives, and even cooks.
- Law Enforcement: AI facial recognition in security cameras will help police officers to catch potential criminals. Intelligent bots can also replace police officers.
- Online Security: AI can more intelligently detect online fraud and provide cyber security. The AI will better determine spending changes and consumer credit activity than the alerts you currently receive.
- Transportation: In the future, vehicles will be able to drive themselves.
Final Thoughts
This was a great discussion on how can robots be the new employees. Though AI has some drawbacks, nothing is stopping it from moving ahead. However, as per various experts, as AI evolves, humans need to monitor and regulate its progress more carefully.
