Trending February 2024 # Dataman Next Tracks Your Cellular Data Usage In A Very Elegant Way # Suggested March 2024 # Top 3 Popular

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I didn’t care much about cellular data tracking apps until I gave up my unlimited data plan, a couple months ago. Then all the sudden it became important to me to figure out how much data I was using and make sure I didn’t go over my allocated amount. My wife and I share 4GB, and although we’ve historically never been close to using all of that, I like to keep an eye on my usage, just to feel safe.

So I downloaded the DataMan app, which has served its purpose wonderfully ever since as it provided me with real time data usage. One thing always bothered me about this app though: it just looked bad.

But this morning, I received an email from Johnny Ixe, the developer of DataMan, letting me know about his new app called DataMan Next. It serves the exact same purpose as DataMan, except it looks much better. I’d actually go as fasr as saying it looks beautiful, but you might want to see for yourself…

Upon launching the app for the first time, you are asked to enter some basic information about your data plan. After entering your billing date, your monthly data plan cap, and your existing usage so far, DataMan Next is ready to go.

With this new app, emphasis is made on the percentage of data used so far and the Smart Forecast of your data usage until the end of your billing cycle.

Smart Forecast predicts if you’ll stay within your data cap. Its intelligent algorithm uses multiple inputs including your current usage and the remaining days in your bill cycle to compute real-time usage forecasts. This helps you plan ahead and take precautions.

The app is designed in a way that you don’t really have to worry about the numbers. Just look at the background color. If it’s green, you’re all good and you probably won’t hit your data cap. If it’s yellow, you might want to go easy on your data usage for a while. If you’re in the red, all indicates that you will go over your data cap, but of course it is still time to take corrective action, which depending on your case might mean no more Netflix streaming until the end of your billing cycle.

You don’t even have to launch the app to monitor your data usage. You can simply set usage alerts that will notify you when you hit a given threshold. By default, DataMan will alert you once you reach 50% of your allocated data. A second alert will pop up at 70%, a third at 90%, and finally, you’ll be given a last alert once you reach 100%.

One may argue that this app doesn’t do anything the old DataMan app didn’t already do, and that would be correct, except that DataMan Next does it in a beautiful way, and for people who like to pay attention to details, this is a world of difference.

DataMan Next is available in the App Store for the special price of $0.99. I believe it will go up to $1.99 shortly. As for the old DataMan app, it will soon be retired.

Do you keep tab of your data usage? If so, how do you do it?

You're reading Dataman Next Tracks Your Cellular Data Usage In A Very Elegant Way

An Ocean Of Data: Looking For Sunken Treasure In A New Way

Early last October, Brendan Foley found himself on a small, inflatable boat making rings in the middle of the Aegean Sea. The 43-year-old maritime archaeologist was waiting on three divers, who were searching for ancient shipwrecks 100 feet below. Rather than drop anchor, the boat’s skipper, a potbellied Greek man named Giorgos, held the wheel hard to port, spinning the boat around and around. Whether inured to the repetitive course or just oblivious to it, Giorgos didn’t appear to mind making circles. But the repetition was making Foley antsy. He fidgeted with the zipper on his wetsuit. He rearranged the dive gear, still dripping wet from his own survey earlier that day. Then he sat down next to me and made an unusual confession for someone whose livelihood is tied to the sea. “I hate small boats,” he said. “Not too fond of big ones either.”

What Foley likes is finding shipwrecks, which is why he and his Greek colleagues chose the day’s dive site, Dia, a small, rocky island about eight miles north of Heraklion, the capital of Crete. The city has been an active port for about 6,000 years. In that time, it is likely that many Heraklion-bound ships wrecked on the cliffs of Dia. Jacques Cousteau found several wrecks on the island’s south shore in 1976 while searching for Atlantis. Foley and his team were the first archaeologists to search the north shore.

As much as Foley likes discovering shipwrecks—he’s found or helped find 26 in the past 14 years—he doesn’t much like spending time looking for them, at least not in the conventional ways. Rather than sending dive teams down to survey 1,000-foot transects one fin kick at a time, Foley prefers to use autonomous underwater vehicles (AUVs) to survey huge tracts of seafloor. Where the robots don’t work well, Foley sends down divers armed with closed-circuit rebreathers and thrusters, allowing them to cover more ground. He wants to go faster, he says, because he needs a lot more information. Maritime archaeologists can spend years on just a few sites, but for Foley’s purposes, a solitary wreck is statistically weak—nothing more than a few words from a greater conversation. To understand the entire conversation, maritime archaeologists must study many wrecks and identify patterns between them. Foley’s model is not the soft science of digging and interpretation, but the hard science of high-throughput screening deployed by gene and drug researchers, who gather data at an industrial rate and analyze that data with powerful computers able to detect subtle patterns beyond the reach of ordinary analysis.

Foley

Archaeologist Brendan Foley uses rebreathers to extend dive times to as long as three hours.

If Foley can determine where hundreds or even thousands of ancient ships were headed, when they were headed there, and what they were carrying, he could use computer analysis to trace the origin of the world’s earliest cultures, and in so doing he could test his central hypothesis: that it was seaborne trade that enabled the spread of civilization in the Mediterranean Basin. But to do all of that on a computer, he first wants to “map, in exquisite detail, the entire seafloor of the Mediterranean,” a sea that covers nearly a million square miles and may contain as many as 300,000 wrecks.

Today is proving to be especially difficult. Foley’s AUVs wouldn’t work near Dia; its steep undersea cliffs interfere with the robots’ sensors. Someone also left a critical part of the thrusters back at the dock in Heraklion. Instead of finding wrecks fast, Foley’s team was left to search out wrecks the old-fashioned way. He and his dive partner had made one dive earlier and had come up with nothing.

After a few more minutes spent motoring in circles, Foley took action. Earlier in the morning, Giorgos mentioned how much he’d enjoy diving in the area, so Foley, in his typical collegial manner, suggested that it was a good time for him to do just that, and added: “Mind if I drive?” Taking the helm, Foley backed the engine down to an idle. The boat slowed, bobbing over mild swells bearing south toward Dia’s yellow cliffs. The temperature was about 80 degrees and the visibility underwater about 100 feet. Now at least in control of something, Foley looked comfortable, happy even.

Given a choice, though, Foley would not be on a small boat on a perfect day. He wouldn’t even be on a large boat. Instead he would be seated on his tiled patio in Heraklion poring over the latest data collected by his robots.

Robot Wranglers

Brendan Foley brought along a team of three engineers and had additional help from three Greek colleagues to manage the AUV operations off the coast of Crete.

Until recently, maritime archaeology was an unlikely candidate for high-throughput techniques. Automated systems appropriate for the field did not exist, and the notion of gathering large data sets runs counter to standard archaeological practice for shipwrecks. Rather than taking cursory surveys of many sites, most archaeologists prefer to linger at one or a handful of sites for years. Because archaeology so often relies more on interpretation than quantifiable data, it is often lumped in with other soft sciences, such as history and cultural anthropology.

Foley is trying to make maritime archaeology a hard science, more in league with biology or physics. He says he doesn’t care about painstakingly examining every last shipwreck. Rather, he plans to automate the discovery of hundreds or thousands of shipwrecks, transform them into a large data set, and probe that data set—not the wrecks themselves—for answers to his questions.

Most ancient wrecks in the Mediterranean are little more than piles of amphorae, two-handled urns that were used as shipping containers. But scientists have developed methods to draw information from them. By examining the size of the pile (once the boat, which has long since rotted away) and the shape of the amphorae, archaeologists can often place the wreck’s origin and its era. The wreck’s location gives hints to its destination.

More often than not, archaeologists date wrecks in the Mediterranean to the Roman or Byzantine era, when sea trade was already well-established. Although these are useful data points to Foley, he is particularly focused on finding Bronze Age wrecks, which date from 3500 to 1200 B.C. They were the first seafaring ships in the region, so their locations and contents could indicate which cultures were in contact with each other at the time. But they are exceedingly rare.

“Foley is trying to make maritime archaeology a hard science, more in league with biology or physics.”Foley’s data-driven approach to maritime archaeology is not entirely new. For example, in 1992 archaeologist A.J. Parker cataloged all the 1,259 known shipwrecks in the Mediterranean. But that data is rudimentary, Foley says, and the inconsistencies in how it was collected make it difficult to compare one ship with the next (some wrecks are well-known excavations; others were found by chance by sponge divers and, unlike wrecks excavated by specialists, may not have been properly categorized by age or origin). Foley’s robots will collect uniform data so that archaeologists can directly compare one wreck with another.

When describing his work, Foley can make high-throughput archaeology sound simple, as if it’s just a matter of time before all the wrecks in the Mediterranean are charted, digitized, and stored. It’s not. Foley’s approach is unproven, technically challenging and expensive. His month-long expedition to Crete cost about $500,000—his entire annual budget—a sum most archaeologists would spread across half a decade. To pay for his robots and the staff necessary to run them, Foley, like most ocean scientists, applies for grants from the National Science Foundation and the National Oceanic and Atmospheric Administration. But the bulk of his funds come from other sources, mostly private donors. When not hunting wrecks, Foley is courting potential benefactors. He attended dozens of fund-raising events last year alone.

Foley’s emphasis on private fundraising is unusual for most scientists, but he goes about it unabashedly. In fact, he is pressing to increase his annual budget fivefold, to $2.5 million. “Why is it that physicists can have a multibillion-dollar facility like CERN and archaeologists don’t?” he says when I ask him about the high price tag. “Do you really care what a muon is? Does anybody really care what a muon is, besides physicists? I argue that it’s just as important, maybe even more important, to understand what it is to be human.”

Into The Deep

Remus AUVs were originally developed to find naval mines.

A few days after the excursion to Dia, Greg Packard gave me an impromptu demonstration in robot handling in the waters off Heraklion. Packard, a wiry engineering technician from the Woods Hole Oceanographic Institution in Massachusetts, was balanced at the stern of the research ship Alkyon and jabbing a pole at a five-foot-long yellow torpedo floating just out of reach. The torpedo was actually a Remus 100, a $375,000 autonomous robot on loan from Woods Hole, equipped with a video camera. A third of the way into its 80-minute survey, the robot had sprung a leak. It automatically aborted the mission and returned to the boat. With the pole, Packard was trying to hook a loop on top of the robot so he could pull it toward the Alkyon’s winch and bring it on board.

After much jabbing and winching and some help from another technician, Packard got the 80-pound craft onto the Alkyon’s deck and into a scarred expedition case. Then he and some of his Greek colleagues launched a second Remus, this one outfitted with sonar, off the stern. The robot swam on the surface for a few moments while Packard tested its tracking system from a ruggedized laptop, and then it dove out of sight and headed to an unexplored section of seafloor.

Foley’s data-gathering system is built around these two Remus robots. The sonar-equipped “acoustic” Remus scans the seafloor first. It travels at up to 328 feet below the water’s surface in a grid pattern while its transducers send a sonar beam across the seafloor. Those signals reflect off solid objects, including large fish, rocks and shipwrecks, producing highlights and shadows in the resultant images. On this trip, the acoustic Remus was complemented by a multibeam sonar built onto the Alkyon’s hull. With it, the team scanned large swaths of the seafloor, albeit at a lower resolution than the acoustic Remus provides.

If Packard finds a shadow in the sonar data that might indicate a shipwreck, he sends out the video-camera-equipped Remus. Sonar images can be difficult to read, so potential wrecks must be examined with video. Once a wreck is confirmed, a dive team descends to the site and snaps hundreds of photos. Later, a graduate student will digitally stitch these photos together and tag the “photomosaic” with location and depth data.

Onshore, having robots gather and computers analyze your data sounds like a great idea, but in the field the challenges are many. First, the Mediterranean is huge. At the rate he’s going right now, Foley would need 2,658 years to map the entire seafloor. Second, many areas of the Mediterranean seafloor are dynamic, with shifting sands that cover anything that may be down there. Third, the robots’ sensors can’t operate very close to islands and coasts that have steep slopes near the seafloor, just the place where many wrecks happen. These regions require dive teams, slowing the rate of discovery. The gear that speeds up the diving is expensive. Each thruster costs $3,500 and each rebreather $15,000; Foley brought four thrusters and six rebreathers on his trip.

If Foley manages to overcome those challenges and collect his data, he will then face yet another obstacle: how to analyze it. He hasn’t yet defined his approach. When I asked him, he said data analysis is a problem he chooses to “kick down the road.” He did say that one possibility would be to create image-recognition software that could identify boat size and amphora shape, categorizing the wrecks by era and origin, and correlate that with the location data and potential destinations. In this way he could, for example, identify all Bronze Age wrecks in the southern Aegean. If the set was large enough, patterns in the data might suggest questions that Foley hadn’t even thought to ask.

As Packard and I sat on the Alkyon waiting for the acoustic Remus to finish its survey, a fishing trawler appeared a few miles off the bow. Packard emerged from the cabin where he’d been monitoring the Remus from his laptop and eyed the trawler. He frowned and stepped back into the cabin, where he typed out a few commands to reprogram the Remus to safer waters so the fishermen wouldn’t net it along with their quarry.

For a field to shift from interpretive to data-driven is not unprecedented, says Theodore Porter, a historian at the University of California at Los Angeles who specializes in the quantification of science. Geography, Porter says, is an example of a field that has become strongly quantitative and data-driven. In the past five decades, drafting techniques and static cartography have merged with geographic information systems, which pull data from numerous sources to create digital, interactive maps. Economics has also morphed from an interpretive science into one driven by mathematics (though how successful this transition was depends on who you ask).

Archaeologists have been moving toward more quantitative approaches for decades, says Michael McCormick, a Harvard University archaeologist and historian. They already use techniques such as radio-carbon dating and DNA analysis to transform physical objects into data. Foley’s method is a next step.

After that step is taken, it would create a self-perpetuating data feedback loop. With more data comes more options. Archaeologists won’t need to physically visit each wreck to determine if it’s worth exploring. The ability to target only significant wrecks will yield more-productive excavations, which in turn will yield more data to analyze and cross-reference and from which patterns may emerge to further describe the ancient world.

High-throughput archaeology will not supplant older techniques, such as excavation. Rather, McCormick says, the methods are complementary. “One shipwreck that is exquisitely excavated and published is a fantastic time capsule from the time and place where it went down, the places it was sailing to and from,” he says. “But 100 shipwrecks, even if we know only a little bit about each of them, give us a whole different type of evidence that we can compare with the exquisite, unique and rare fully excavated shipwrecks. And they will illuminate each other.”

Divers

In the warm waters of the Aegean Sea, timbers of ancient ships rot away, leaving only amphorae behind.

Two weeks after the Greek expedition, I called Foley to see how things had gone following my departure. He was in the middle of readying a presentation for the trustees of the American School of Classical Studies at Athens. “We had a pretty good showing,” he said. In a month, the team found eight wrecks. Granted, three of those were modern, and of the five ancient wrecks, one of them, a Roman ship, was previously known from Cousteau’s work on Dia. Neither divers nor robots had found a Bronze Age wreck.

Nonetheless, Foley said he was not discouraged. Four new ancient wrecks plus the Cousteau wreck were five more data points to add to his database. He said that his donors knew that finding a Bronze Age ship was a long shot. And he said he’s encouraged that the acoustic Remus found a ship—proof, he said, that his system will work.

Even before leaving Crete, Foley had a few expeditions in mind for the following year. This spring, he may survey the waters off Algeria for the first time. His Algerian colleagues will use ship-based sonar to look for underwater seismic faults; Foley is counting on that sonar to find a few shipwrecks in the process. He’s also working to establish new contacts in Egypt and Libya, because the ones he negotiated with last year were ousted in the Arab Spring.

Foley said he also plans to expand his robot fleet, adding larger AUVs like the Remus 6000, which can dive to 19,685 feet. The additional robots will allow him to explore a lot of new terrain, possibly even the deepest zones of the Mediterranean.

“100 shipwrecks, even if we know only a little bit about them, give us a whole different type of evidence.”After his expedition to Crete, Foley had 34 ancient wrecks, some of them found by conventional means, while his divers and AUVs found others in his database. Though that is a very long way from 300,000, it’s not a reason to dismiss his work. As much as he would like to, Foley does not actually need to catalog all 300,000 wrecks. He’ll need only a few hundred or maybe a few thousand to achieve statistical significance across a range of queries. With more robots and more funds, he could do it. And even if he doesn’t—even if Foley just goes along adding a handful of wrecks a year to his database—he plans to continue his work for the next 25 years or so. “I don’t go poking my head into other people’s labs to ask if what they’re doing is worth doing,” he says. “If you can generate funding and go out and do your projects and publish your papers, what more validation do you need?”

Shipwrecks

FINDING SHIPWRECKS FASTER

1. The acoustic Remus 100 AUV scans, for example, a one-square-mile patch of seafloor with sonar, a process that requires about 4.5 hours. The sonar bounces off large objects to create highlights and shadows that reveal the rough outlines of a wreck.

2. If the sonar images indicate a shipwreck, the video Remus goes in for a targeted scan of the area. The video Remus cannot take high-quality images, so any shipwrecks confirmed by the visual data are later photographed by a team of divers.

3. Underwater cliffs interfere with sonar signals, so in areas where the seafloor is near one, Foley’s team searches for wrecks by diving. Divers equipped with rebreathers can stay down about three times as long as one with scuba gear. Thrusters allow them to cover twice the ground that divers swimming under their own power can survey.

Underline

Sonar transducers on the sides of the Remus 100 cannot “see” directly beneath the AUV [dark area, above]. On the right are two ancient shipwrecks.

Portrait of a Shipwreck

Chios Shipwreck Survey 2005

Ancient wrecks, such as this one from the 4th century B.C., are mostly just debris fields of amphorae, but they can still contain a great deal of data. Once a wreck is identified, divers take hundreds of photos of the site with Nikon D100 and D300 cameras. Those photos are stitched together into a photomosaic [above], which is then tagged with information on the location and depth of the wreck.

Brooke Borel is a contributing editor at Popular Science. She lives in Brooklyn.

Card Crawl Review: Battle Your Way Through A Dangerous Deck

In Card Crawl, developer Arnold Rauers has created an eclectic mix of games that don’t immediately sound like they would fit well together. With the bones of a simple game of solitaire and some heavy inspiration from Dungeons and Dragons, Card Crawl is just strange enough to warrant a closer look.

Concept

The idea behind Card Crawl is actually an extremely clever framing device. Players take control of a seemingly bored tavern patron who gets swept up into a game of chance presided over by another customer. The actual card game takes place on a single table within the tavern, with the protagonist trying to navigate a deck of dangerous enemies and mountains of treasure.

Design

From the second you boot up Card Crawl, its amazing art and charming attitude are on full display. The attention to detail is stunning, with tons of great little touches, from a d20 in one corner to a bug crawling along the ceiling. The cards all have amazingly detailed art as well, and it’s worth mentioning that your character card art will change to reflect how close to death you are. All of these things create the feeling of a living, breathing world.

That feeling is carried into every action you take in the hub world. For instance, you have to physically drag keys into locks to unlock cards, and, in order to organize your constructed deck, you have to drag the cards into position. These realistic movements tie in with the music, sound design, and art of Card Crawl to create a surprisingly immersive experience.

Gameplay

Card Crawl is a deceptively simple concept with a surprising amount of depth. The goal of the game is to safely traverse a Dungeon Deck of 54 cards while collecting as much gold as possible. The game takes place on an eight square grid, with four zones on top and four on bottom. The player begins with thirteen health, represented by a character card that resides at the bottom of the screen. Accompanying the character card are two equipment slots and a backpack for storage.

The four squares above the character are always filled by four of the remaining cards in the Dungeon Deck. All of these cards have numbers associated with them ranging from two to ten. Each card will only preform its function up to the indicated number. Enemies will lower your life, swords will damage enemies, shields will absorb damage, and potions will heal your character. To further complicate matters, the game has skills that the player can use to bend the rules. In order to use any card, it must be equipped in one of the hands. New cards are dealt from the deck when only one card remains in the top boxes. It really isn’t as complicated as it sounds, and the short five minute tutorial does a great job of guiding you through the basics.

The controls themselves are very stiff, and the cards are extremely particular about where they can be placed. In order to move a card, you must hold it exactly over the desired location. Anything less than that, and the card will fly back to its original position. This can lead to a few frustrating attempts to get the game to do what you want, but it was obviously done to prevent accidental placement. In a game where one false move can end an otherwise promising run, I think this is a great solution. I would much rather have to repeat an action every once in awhile than have my character suffer a “cheap” death. In my entire time with the game I never once had a card move to the wrong spot.

The good

A large component to Card Crawl is luck due to the random nature of being dealt cards. While this could be a drawback, it is actually the point of Card Crawl. Overcoming luck because of a sound strategy is great fun, and the reason I keep coming back to this game even after beating it.

The bad

You get absolutely nothing for a failed run. If your character card’s life dips below zero you won’t see one single gold. When coupled with the game’s reliance on the luck of what you are dealt, this can end up being a little frustrating. It does, however, add a great deal to the tension of the game. It creates a very real sense of risk and reward since you instinctively want to sell items for gold, but will reap no benefit at all if your hubris eventually causes you to fall in battle.

Value

It will take you about seven hours to unlock all of the abilities Card Crawl has to offer. After that, there is a little replay value to be found in some well thought out and challenging achievements. Add to that the global and local leaderboards, and you have a tremendous value for the asking price of $1.99.

Conclusion

Card Crawl is a perfect game for the mobile market. Individual runs are short enough to be perfect time killers, while the depth of the game’s strategy can provide hours of solid entertainment. The graphics, sound design, and music all make Card Crawl a joy to play, and the overall attitude is lighthearted and fun. I highly recommend picking this game up if you have even a passing interest in the puzzle or strategy genres. Download Card Crawl in the App Store.

Related apps

Dell Data Manager High Cpu, Memory, Disk, Power Usage

Some users have noticed that when they start the computer and look in the Task Manager, Dell Data Manager shows high CPU, Memory, Disk, or Power usage. The issue is common and mostly caused by corrupted program files, software conflicts, or malware.

What is Dell Data Manager?

Dell Data Manager is a process of Dell Support Assist that looks after your data. It ensures regular backup of data to stay away from inconsistency in data. You can not access Dell Data Manager in the Control Panel or Settings but can be seen in the Task Manager.

Fix Dell Data Manager high CPU, Memory, Disk, or Power usage

Misconfiguration in the Dell Support Assist Manager can also cause this issue. If Dell Data Manager shows high CPU, Memory, Disk, or Power usage in the Task Manager of your Windows computer, follow these suggestions to resolve the issue.

Disable the System Repair option in the Dell Support Assist

Update all drivers

Troubleshoot in Clean Boot State

Scan your computer for Malware

Uninstall Dell Data Manager

Let’s get started

1] Disable the System Repair option in the Dell Support Assist

System Repair is a feature in the Dell Support Assist app that does what its name suggests. However, for the time being, you should disable System Repair from Dell Support Assist’s settings as that increases the CPU surge. To do the same, follow the steps below.

Open Control Panel from the Start Menu.

Go to System and Security.

Open SupportAssist OS Recovery.

Go to the Settings tab and then disable System Repair.

After disabling the feature, reboot your computer and then check if the issue is resolved.

2] Update all drivers

Dell Update utility can also help you download or update Dell Drivers. Dell automates the whole process. It will detect the model and make of your device and components and see which drivers are required for it.

3] Troubleshoot in Clean Boot State

Dell Data Manager resource utilization is caused by third-party application processes related to Dell. So this problem can be solved by stopping these applications. To do this we have to start the computer in a clean boot in which the computer will start with a minimal set of drivers and startup programs. Follow the solutions mentioned below to do the same.

Press the Windows key and type ‘system configuration’ and hit Enter.

Check the box of Hide all Microsoft Services.

Now, go to the Startup tab and hit the Open Task Manager.

Search Dell-related services and disable them.

Finally, close Task Manager and restart the computer.

Once your computer restarts, it will start without many unnecessary services. You then have to manually enable services to determine which app is causing the issue. Once you stumble upon the culprit, remove it from your system or keep its service disabled.

4] Scan your Computer for Viruses and Malware

Make sure that the issue is a result of a virus or malware that’s infected your computer. In that case, you need to scan for the malicious files and then remove them from your computer. To do the same, you can make use of any third-party antivirus that you have or use Windows Defender Antivirus.

Hopefully, you will be able to resolve the issue using the solutions mentioned in this article.

How do I disable Dell Data Manager?

Dell Data Manager is a part of Dell Support Assist, in that case, you need to disable the Assist app. One can disable the app from the System Configuration app, go to Services and then disable all related services.

Why is my CPU always at 100%?

If your CPU is always at 100%, then it is very likely that some heavy applications are configured to launch at startup and then continue chugging your resources. Not just that, if your system files are corrupted, some systems will keep hogging the CPU, GPU, memory, or some other component of your computer.

Data Science Plus Blockchain Will Be The Next Big Frontier In Tech

The future of data science is blockchain, which is quickly gaining popularity. What is blockchain and data science technology?

One of the areas of technology that is now expanding quickly is data science. Science has various subfields that are always developing, some of which include descriptive analytics, diagnostic analytics, and predictive analytics. From current data, whether organized or unstructured, insights should be extracted. As an illustration, Netflix Recommendations – Netflix can offer suggestions based on a user’s past video viewing behavior and ratings. As a consequence, based on their choices, users can receive recommendations for new movies and TV shows that are related to their interests. Maintaining user engagement on such websites can increase the company’s income.

Blockchain is a decentralized digital record that may be used to store any kind of data. Blockchain technology might allow multiple users to share a secure database without a centralized control system. It is therefore possible to safely save information about a party’s financial behavior. To illustrate, let’s utilize bitcoins. Blockchain technology is used by cryptocurrencies, a type of digital currency, to secure and record each transaction. You may buy anything from foodstuffs to cars using Bitcoin as a form of virtual currency, for instance.

How are they related?

If there is a relationship between blockchain and data science, it hasn’t received much attention. Or to put it another way, the core of each of these technologies is data. Blockchain validates and saves data, but data science focuses on obtaining pertinent insights from the data for problem-solving. Each of these technologies uses algorithms to control interactions with different data segments. In a nutshell, the blockchain validates data while data science is used to anticipate.

How is blockchain important for data science? Enables data tracing

Blockchain facilitates peer-to-peer relationships. Any peer can examine the complete procedure and identify how the findings were arrived at, for instance, if a published description falls short of appropriately describing any technique. Thanks to the ledger’s open channels, anybody can understand if data is correct to use, how to keep it, how to update it, where it originated from, and how to properly utilize it. In conclusion, consumers will be able to follow data from beginning to end thanks to blockchain technology.

The capability of real-time analysis Ensures the data’s accuracy

Numerous nodes, both public and private, store the data in the blockchain’s digital log. Before being added to new blocks, the data is cross-checked and reviewed at the entrance point. This process serves as a way to verify data on its own.

Lessens the difficulty of data exchange

When data flows swiftly and readily, there are various benefits for enterprises. It is quite difficult to do with paper records. When the data it contains is required somewhere else, this issue is made worse. True, the data will reach the other division, but it could take a while, and there’s a chance they could get lost.

Builds trust

As you are well aware, biases are typical when there is a central body. Over relying on a single individual might be risky. Many firms decline to provide other parties access to their data because of problems with trust. As a result, information sharing becomes nearly impossible. Information interaction with the blockchain is not hampered by the trust issue. Businesses may connect effectively by exchanging the knowledge they have.

Improving data integrity

In the past ten years, businesses have placed a major emphasis on expanding data storage. By the end of 2023, that has been rectified. The new challenge for the majority of organizations is safeguarding and authenticating the data. The main cause of this is that different sources of data are gathered by organizations. Even information acquired from governmental organizations or produced locally may have errors. Additionally, information from other sources, including social networks, could be inaccurate.

The implication of data science and blockchain

Data is the foundation of blockchain technology. Data is also necessary to address several significant industry pain points. For instance, to promote transparency and decrease fraud, we must detect trends and patterns in prior user behavior and connect them to present behavior. Both have had a big influence on the current world. Data scientists have been investigating the potential for storing data on the blockchain for a while. The company Factom, which most recently worked with Microsoft on the Cocoa Framework project, is the best-known example of this. Businesses will therefore be able to keep private data on the Blockchain.

Things To Keep In Mind Before Applying For Next Data Science Job

Data Science job interviews are beyond checking for technical acumen.

It is now a well-established fact that

Reading the Job Profile

The hiring managers generally want to know if the candidate is actively interested in what the company does and that the person has already begun thinking about how she could bring value to the company in that role. They want to see if the applicant’s skill set matches the job’s requirements. Hence, it is important to read the Job Profile, especially for skills, tools and techniques. If the job description is not self-explanatory or in detail, then it is helpful to research on the company. One must be clear as to what type of a data scientist position she is applying for. Review the different nuances expected in various job openings and prepare accordingly.  

Build a Digital Presence

While it is true that one will find numerous available resources online to help prepare for the interview, he should also have a social media or relevant digital presence. Recruiters will admit that they often check a candidate’s LinkedIn profile before calling them for an interview. So, one should get started by having: • LinkedIn Account that is tailored (updated) according to job skills mentioned in the job listing. It won’t be helpful if the job applied is for a data scientist position while the profile portrays you as a wildlife photographer. • GitHub Account. Nothing is convincing like a well-documented work. Therefore, applicants can put their coding works and projects to GitHub so that the recruiter can see their work first-hand, before calling for interview or tests. • Answering data science-related questions on Quora, blog writing to showcase one’s understanding of the subject matter. Even registering and participating in data science community events can display the applicant’s keen interest in this niche. It is important that while building a LinkedIn account, treat it like a digital resume. One must be thorough while explaining about work experience, data science projects. For instance, ‘Helped with XYZ project’ won’t be as impactful as ‘Helped with XYZ project by improving accuracy by 23% and thus generating 45% increase in revenue’  

Studying and Reviewing

It always pays to practice. One must have a careful reviewing of one’s data science projects, work on puzzles to improve problem-solving skills, study about use cases of data science job roles, stay alert on current data science trends, have a clear understanding of the difference between confusing terms or concepts (e.g. Precision and Recall, False Positive Rate and True Negative Rate, etc.)   Brush up on fundamental subjects topics like • Probability – Random variables, Bayes Theorem, the Probability distribution • Statistical Models – Algorithms, Linear Regression, Non- Parametric Models, Time Series • Machine Learning, Neural Networks   One should also practice how she can use the STAR (situation, task, action, and result) method to answer these questions, which will basically be used to evaluate 1. Teamwork/ culture fit, 2. Communication skills,   • Problem-solving, 1. Presenting convincing actionable insights 2. Versatility Apart from that, pay attention to body language.

It is now a well-established fact that data science jobs are on an exponential rise. With companies trying to analyze data to gain valuable insights, understand trends and more, data science roles , like data scientists, data engineers, data analysts, analytics specialists, consultants, insights analysts, and more are in high demand than ever. No wonder that Harvard Business Review has named it as the sexiest job of the 21st Century in October 2012. However, preparing for a data science job position can be intimidating. While it is often suggested that the key to crack such an interview is having technical preparation about technology and possessing technological aptitude. However, Ted Kwartler , VP of Trusted AI at DataRobot and Harvard Adjunct Professor, shares from his real-life experiences that “anticipating audience needs is the most important factor at each interview stage. In reality, data science candidates often over-index on technical acumen, and neglect the fact that every evaluator is reviewing different attributes. This implies that one doesn’t need to go overboard on technical acumen rather than analyze that every evaluator reviews different attributes. Here are a few pointers that can help interested applicants keep in mind before the data science chúng tôi hiring managers generally want to know if the candidate is actively interested in what the company does and that the person has already begun thinking about how she could bring value to the company in that role. They want to see if the applicant’s skill set matches the job’s requirements. Hence, it is important to read the Job Profile, especially for skills, tools and techniques. If the job description is not self-explanatory or in detail, then it is helpful to research on the company. One must be clear as to what type of a data scientist position she is applying for. Review the different nuances expected in various job openings and prepare accordingly.While it is true that one will find numerous available resources online to help prepare for the interview, he should also have a social media or relevant digital presence. Recruiters will admit that they often check a candidate’s LinkedIn profile before calling them for an interview. So, one should get started by having: • LinkedIn Account that is tailored (updated) according to job skills mentioned in the job listing. It won’t be helpful if the job applied is for a data scientist position while the profile portrays you as a wildlife photographer. • GitHub Account. Nothing is convincing like a well-documented work. Therefore, applicants can put their coding works and projects to GitHub so that the recruiter can see their work first-hand, before calling for interview or tests. • Answering data science-related questions on Quora, blog writing to showcase one’s understanding of the subject matter. Even registering and participating in data science community events can display the applicant’s keen interest in this niche. It is important that while building a LinkedIn account, treat it like a digital resume. One must be thorough while explaining about work experience, data science projects. For instance, ‘Helped with XYZ project’ won’t be as impactful as ‘Helped with XYZ project by improving accuracy by 23% and thus generating 45% increase in revenue’It always pays to practice. One must have a careful reviewing of one’s data science projects, work on puzzles to improve problem-solving skills, study about use cases of data science job roles, stay alert on current data science trends, have a clear understanding of the difference between confusing terms or concepts (e.g. Precision and Recall, False Positive Rate and True Negative Rate, etc.) Brush up on fundamental subjects topics like • Probability – Random variables, Bayes Theorem, the Probability distribution • Statistical Models – Algorithms, Linear Regression, Non- Parametric Models, Time Series • Machine Learning, Neural Networks One should also practice how she can use the STAR (situation, task, action, and result) method to answer these questions, which will basically be used to evaluate 1. Teamwork/ culture fit, 2. Communication skills, • Problem-solving, 1. Presenting convincing actionable insights 2. Versatility Apart from that, pay attention to body language. It is also beneficial if the applicants can study about the engineering/DS materials from applied companies. Further, learn how to whiteboard to make sure of not being caught unaware when asked to pen ideas.

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