You are reading the article If A Girl Isn’t Interested In Science, It’s Not Because She’s A Girl updated in December 2023 on the website Bellydancehcm.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 If A Girl Isn’t Interested In Science, It’s Not Because She’s A Girl
I think a lot about the reasons I became a scientist. There are so many aspects of science that I adore. I love the feeling of having new data to pour over. I love analysis and statistics and creating mathematical models to explain my findings. I love tinkering with equipment in the laboratory. I love soldering and wiring and getting my hands dirty. I love generating new hypotheses and testing them, and I love to harass my fellow scientists about whether their experiments contain the proper controls. There is no doubt that I find joy and fulfillment in the technical aspects of my job.
In her recent article “If a girl isn’t interested in science, don’t force her to be,” Telegraph columnist Mary Kenny claims that women are inherently less interested in science. Science, she argues, is based in fact and the “laboratory testing of elements,” career features that interest men alone. Women, meanwhile, are interested in careers where the story or narrative is important and the job is centered around people; “biography, psychology and language” are a few of the career examples she gives. This fundamental difference between men and women deters women from science careers, she concludes.
What Kenny misunderstands is that science is narrative. If she believes that women’s sole interest in narrative is what keeps them out of science, then her article only highlights how out of touch she is with modern science. In fact, I would expect that if a love for narrative were the critical factor determining women’s success in science, women should be excelling. Yet they are leaving science disproportionately. Women don’t leave science en masse as girls. They leave after they’ve received all of their technical training and have put in years of commitment to their fields. They leave when they reach the narrative part of their career.
As a new professor and group leader, my primary job is the narrative. Although I’ve had more than a decade of technical training from biologists, engineers, and surgeons, the majority of my time is now spent mentoring students and helping them find the story in their data so that we can communicate their findings to others. I believe it is critical to teach these younger scientists to find their narrative and to tell their story flawlessly. Importantly, because research dollars are becoming increasingly more difficult to obtain, the success of my research program depends on my ability to craft a convincing narrative. If I want to keep my research afloat, my narrative skills are critical in convincing funding agencies to support our work.
However, my use of the narrative is not born purely of necessity. I love telling people about our work, especially non-scientists. In one of my favorite experiments, which we recently published in the New England Journal of Medicine, we studied a group of adults that were born as preemies in the late 1990’s. These folks looked and acted completely normal when we met them—but when we stressed them by giving them low oxygen to breathe, they responded completely abnormally. Unlike our subjects that had been born full-term, they didn’t increase their breathing to compensate for the low oxygen. This novel finding certainly has consequences for future problems they may develop as they age. Not only did we make sure to communicate this to our physician and scientist colleagues by publishing our work, but I also spent a lot of time talking to members of the media about our findings and explaining why our future work is so important.
University of Wisconsin, Pediatric Critical Care Medicine and The John Rankin Laboratory of Pulmonary Medicine
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A Master’s In Data Science And A Six
A Master’s in Data Science and a six-figure salary are mutually exclusive in the present generation
While a bachelor’s degree in math, statistics, computer science, or a similar field of study is normally required for data scientists, some companies prefer applicants with a master’s in data science. These employers will pay more for this qualification in addition to preferring it. That is how a Master’s in Data Science and a Six- figure Salary go hand in hand.
Graduates of data science master’s programs can earn six- figure salary, beginning with base salaries, and incentives are not included in this figure. A large part of many of the best master’s programs in data science emphasizes experiential learning. In many decision-making processes, students have practical experience transforming complex data into visual representations. Students gain collaboration skills through working on practical data science projects with academic and commercial partners.
Most data science master’s programs have only been around for five to seven years, making them a relatively recent phenomenon. Before the availability of complete data science programs, many data scientists sought regular master’s degrees in statistics or computer science. After graduation, you may have a variety of possibilities if you’re considering how to find a career in data science and what a master’s in data science pay might look like. There are many more high-paying jobs, but these are just a few to think about:
Data Scientist – Many people join the profession as generalists in data science, typically at the IC-II level. Building models, preprocessing data, and validating data are the responsibilities of data scientists.
Data Architect – The creation of data infrastructure is occasionally delegated to data architects. Typically, architects and data engineers work closely together.
Machine Learning Engineer – Machine learning, deep learning, and AI specializations are available in several master’s degrees in data science. Graduates with these degrees are more prepared for specialized ML positions like engineers, scientists, and AI specialists.
Data Science Manager – A master’s degree is sometimes a requirement for the position, even though master’s programs aren’t explicitly created to prepare you for management. You might graduate and be hired as a manager if you have prior managerial experience. Some of the highest salaries in the industry are paid to data science managers.
Data Scientists and Mathematical Science Occupations: $100,000 or more (IN USA) – Data scientists and those in related fields make an average salary of $103,930 per year, according to a BLS report from May 2023. You can also have the opportunity to make extra money depending on the sector.
Market Research Analysts: $66,000 (IN USA).
Market research might be a difficult but rewarding place to start if you’re considering how to break into the data science field. According to BLS data from 2023, market research analysts made a median yearly compensation of $65,810.
Computer and Information Research Scientists: $127,000 (IN USA).
The median master’s in data science income for computer and information research scientists with a degree in computer science or a related field was $126,830 per year in 2023, according to the BLS.
A master’s degree in data science will help you progress your career and possibly earn higher pay while making you a competitive applicant for these roles. The completion of a master’s program in data science demonstrates to potential employers not only your commitment to your studies but also your expertise in the industry.
You can access and analyze information in ways that others can’t by obtaining essential skills in statistics, analytical techniques, programming, and business through a master’s in data science. When figuring out how to get a job in data science, these abilities might make you stand out from other jobs.
Conclusion:6 Quick Tips To Fix Your Laptop Camera If It’s Not Working
6 Quick Tips to Fix Your Laptop Camera if it’s not Working Reinstalling the device driver is usually a fast fix to try
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If the
integrated camera is not working
, the most likely trigger is driver-related issues.
In some rare cases, you may have faulty hardware that will require a replacement.
Also, if the
built-in camera is not working,
you must ensure that any webcam protection software is properly configured.
X
INSTALL BY CLICKING THE DOWNLOAD FILE
To fix Windows PC system issues, you will need a dedicated tool
Fortect is a tool that does not simply cleans up your PC, but has a repository with several millions of Windows System files stored in their initial version. When your PC encounters a problem, Fortect will fix it for you, by replacing bad files with fresh versions. To fix your current PC issue, here are the steps you need to take:
Download Fortect and install it on your PC.
Start the tool’s scanning process to look for corrupt files that are the source of your problem
Fortect has been downloaded by
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There are many uses for the laptop camera, but most people use it to take photos or hold video chats like Skype or Google Hangouts, among other uses.
But the laptop camera may not always launch or work as it is ordinarily intended. As one would expect, videoconference-focused apps are prone to be more impacted than others. Thus, issues with laptop cameras not working on Teams or on Zoom are frequent.
Why is my camera not working on my laptop?On your Windows 10 devices, there are a few reasons why the camera may not work. The top triggers for this problem include:
Driver-related issues – This could be missing drivers after an update or old and corrupted device drivers.
Compatibility with the antivirus program – In some cases, users have found out that the antivirus program blocks the camera. Hence you should use only the most compatible Windows 10 antivirus software.
Problems with the system settings – In some cases, the apps do not have the authority to use the camera; when that is the case, it will not turn on.
Hardware issues – You may simply be dealing with a bad camera and will need to replace your webcam.
Now, moving on with our guide. Here are solutions to help you fix your laptop camera when it just won’t work, so read on!
How to fix my camera on my laptop? 1. Run the Hardware troubleshooterFollow the on-screen instructions to run the Hardware and Devices troubleshooter. The troubleshooter will begin detecting any issues.
If you’re experiencing problems with your PC’s recently installed device or hardware, then run the Hardware and Devices troubleshooter to resolve the issue.
This checks for commonly occurring issues and ensures any new device or hardware is correctly installed on your computer.
2. Update the laptop camera driverYou can repair the laptop’s camera just by updating your web camera driver. Plus, it’s possible to perform this action from Device Manager as presented down below.
4. When prompted to choose How do you want to search driver, select Search automatically for updated driver software.
Finding the right driver can be a difficult task. Therefore, we recommend you use an automatic driver tool to scan your PC for out-of-date or missing drivers and automatically update them.
Dedicated driver updaters will match your hardware with its corresponding driver, eliminating additional problems that can occur when using a mismatched driver.
A dedicated tool that helps you find the precise and latest updates for all your drivers.
Free trial Visit website
3. Reinstall the laptop camera 4. Roll back driver 5. Check your antivirus softwareIf your laptop camera doesn’t work or open, or you get an error saying that the laptop camera cannot be found or cannot start, it may be caused by antivirus software that is blocking it or an outdated webcam driver.
6. Check the camera privacy settingsThe solutions above will work if the laptop camera is not working and showing a black screen. They will also come in handy if the laptop camera is not working but the light is on, or if it is not working with services like Google Meet, Teams, or on Zoom.
This article will also help you fix the following camera errors:
Error code 0xa00f4271 – It manifests itself when you are trying to use your webcam
0xa00f4244 No cameras are attached error – This happens when the Camera app can’t detect your webcam properly
Error 0xa00f4292 – Corrupted or missing drivers are the root of this issue, but our guide will help you fix it as well
Laptop camera not working in Windows 10, 11, 7, 8 – The solutions above are applicable to all OS iterations.
HP, Dell, Lenovo, Asus, Acer, MSI laptop camera not working – Update the laptop camera drivers on all laptop brands in order to fix this.
Laptop camera not working on Zoom, Teams, Google Meet, Discord – Check if the apps have the right to access your camera.
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Opinion: Apple Pay Is Easier Than Swiping A Card … Until It’s Not
Apple unveiled its mobile payment service Apple Pay last September alongside the iPhone 6 and Apple Watch later rolling it out to new iPhone users in October through the free iOS 8.1 software update. Dozens of banks and credit unions have flipped the switch on Apple Pay since then as more merchants have announced support or plans to accept the new payment method.
Apple Pay, which allows users to securely pay in stores using the latest models of the iPhone simply by placing the smartphone near a special terminal, uses your existing credit or debit card without revealing personal information like your name or card number to merchants.
In practice, Apple Pay is a real delight to use as a payment method as it feels a bit like you’re skipping the payment process altogether; I imagine moving from cash and checks to debit and credit cards years ago felt similar. There’s still a social oddity about paying with your phone in many parts of the United States in 2023, though, which I’m not sure happened with the transition to using cards.
This sort of social awareness experience doesn’t stretch overseas in parts of Europe and Asia where mobile-based payments have existed for years, and using Apple Pay in San Francisco or New York City probably only felt novel for a few days before becoming completely normal.
Paying for a cab by tapping my iPhone on a terminal from the backseat in NYC last year felt more futuristic than summoning an Uber from an app and paying with my thumbprint, almost like the cab was more tech-savvy than me.
In other parts of the country, though, the short-but-growing list of Apple Pay merchants and the feature being limited to one model of iPhone sometimes makes paying with your iPhone an awkward experience.
My best case scenario happened over the weekend when I picked up chocolates and an orchid from a local Winn-Dixie with self-checkout; almost like using Apple Pay in a social vacuum.
My worst case scenario followed the next day at Walgreens: terminal is picky at picking up the iPhone, still requests PIN input with my debit card, asks for an optional donation to an organization, presents cash back options, asks to confirm total. Four or so screens to get through all while a line builds up behind me. Had I paid by swiping my card I wouldn’t have noticed, but it crossed my mind that the process started by me waving my phone at the terminal.
What’s worse is the whole CurrentC episode that played out late last year causing problems even in big cities. Apple Pay worked at some non-official partners as expected, but later some of those merchants disabled support breaking expectations for shoppers.
Other merchants like CVS Pharmacy and Best Buy have terminals that display the contactless payment logo but manually disable support to block Apple Pay and other mobile payment solutions in favor of the upcoming CurrentC service.
Unless you follow technology news closely and know the backstory with Mobile Customer Exchange, you may attempt to use Apple Pay at one of these locations without success and be turned off by the experience. Hopefully this issue is resolved in the future.
Even some Apple Pay partners aren’t 100% prepared for accepting mobile payment services which makes using the service tricky.
Apple Pay worked flawlessly for me at Walgreens and McDonalds on the day of its launch, but the payment method did not work at my neighborhood Subway (an Apple Pay partner with terminals that support contactless payments) when I tried it a few weeks later. I also tried using Apple Pay at a local drug store with terminals displaying the contactless payment logo without luck.
In both instances the iPhone 6 knew something with Apple Pay was happening as it activated Passbook and displayed a message saying “Hold Near Reader to Pay”, but hiccups somewhere along the way (not distance!) couldn’t close the deal.
This is really where the way it feels to use Apple Pay comes in.
Swiping a card is largely the norm; paying with cash is acceptable; paying with a check is inconvenient, last decade, and increasingly not accepted by merchants, but not completely foreign. Paying with other methods are different. Despite contactless payments and mobile payment services existing years ahead of Apple Pay, the whole concept hasn’t become the new norm yet in the United States.
Apple also hasn’t actively marketed Apple Pay with the iPhone 6 using TV ad spots like it has other features like the Health app on iOS 8, sending voice messages, and using the camera. Not found in Apple’s recent TV spots for the iPhone: anything about Apple Pay.
There’s certainly no shortage of Apple Pay compatible iPhones out in the wild with over 74 million iPhones (a mix of old and new models) sold around the world last quarter.
Once the Apple Watch hits the market in April, even more people will be able to use Apple Pay as pairing the Watch with the older iPhone 5, iPhone 5c, and iPhone 5s models lets you use the mobile payment service in stores, according to Apple.
As ready for the Apple Watch as I am, though, I imagine paying with Apple Pay and the Apple Watch will have the social side effects of feeling like an early technology adopter even at official partner merchants like Walgreens and Whole Foods.
This is not to say that Apple Pay isn’t a winner, but that whole experience is more nuanced than that. In general, I use Apple Pay at least once a week around town. I’d love to use it everywhere for the security benefits alone, and I’m confident we’ll see more merchants accept the payment type over time.
My current Apple Pay use is at the same few locations each time, and I’m reluctant to try Apple Pay again at places where it hasn’t worked in the past. There are even a few mom-and-pop shops that recently added terminals with the contactless payment logo, but I’m about as comfortable asking if they accept checks as I am trying to hover my iPhone over that reader at those places. Explaining the abstract concept of mobile payments to a cashier after a failed attempt isn’t a great experience, and trying again time and again inconveniences both the cashier and the person checking out.
Even without a marketing campaign in people’s living rooms led by Apple, mobile payment services like Apple Pay will become more common in the US—even helping services like Google Wallet—but I would love to see a bigger push from Apple to make Apple Pay feel as normal as swiping a credit card.
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It’s Not About The Data…
It’s all about the story…
It’s a jungle out there, dog eat dog, and unstructured data is proliferating at an exponential rate. As a marketer you have to be careful, you don’t want to get sucked into this digital Wild West, even the quickest out there could get stung.
Yet, there are opportunities to be had, little nuggets of 24-carat gold that can provide your business with a cutting edge worth millions. Sounds like an opportunity, right?
However, just as the prospectors found it tough trying to make a buck during the 19th century Alaskan gold rush so businesses are finding it increasingly difficult to make sense of how to make big data pay. So even at this early stage of its evolution businesses are asking, what does the future of big data look like?
There isn’t an easy answer but as Shawn O’Neal, VP of Global Marketing Data and Analytics at Unilever, put it at the HAVAS chúng tôi data festival in London recently: ‘The number one lesson I’ve learned in 20 years of working in data and analytics and trying to convince people that the data is saying something is that it’s not about the data. It’s about how you tell the story and translate’.
‘No matter how good the data is, if you can’t portray it in a human, connected way to business decision makers, it never has an impact. It’s about story-telling. The data could be 1% but it is the nugget that stimulates everything.’
How will data tell an engaging story?In today’s social and mobile world, businesses need to move faster and share knowledge more broadly than ever before. So they need to move quickly but also translate huge amounts of data, which can take time.
Nobel Prize winner and behavioural psychologist Daniel Kahneman explored the notion that we have two ways of processing data in his book Thinking, Fast and Slow. He theorised that we have two basic systems of thought, ‘Thinking Fast’ is unconscious, intuitive and effort-free. ‘Thinking Slow’ is conscious, uses deductive reasoning and is high-effort.
‘Slow’ likes to think it is in charge, but it’s really the irrepressible ‘fast’ that runs the show, making thousands of decisions and judgements every second.
The problem with analytics is that it’s all about ‘thinking slow’, it requires focus and laborious analysis.
The challenge is to move data analytics from ‘slow’ to ‘fast’, intuitive thinking. Going straight for the gold but armed with the knowledge of 100 geography professors.
In thinking about ‘it’s not about the data’ Adoreboard aims to do is to make it possible for the most sophisticated thinking system in the world to make a decision based on information presented by the best man-made computers in the world.
Copernicus changed the world when he came up with On the Revolutions of Celestial Orbis, yet he only had a limited amount of data available to him in 1543, it was intuitive insight that allowed him to make the imaginative leap and place the sun at the centre of our solar system.
And, we agree with O’Neal, that’s the future of data analytics, presenting the data in such an innately human way that it allows decision makers to make that all important imaginative leap and turn insight into opportunity.
But what does this look like? One of the biggest challenges for digital marketers in the next five years will be to create a bridge for people to understand what the data is saying.
Decision makers need the ability to digest and understand data quickly, often on the move, often through a mobile and definitely within a very short time window.
What if the prospectors of 19th century Alaska had the geographical knowledge that we have now and they could just point a mobile phone at a mountain and the phone translated the data and presented a picture of where the gold was? Maybe that is the future of data analytics.
Let’s look to the futureIf the future is not about the data, how can we switch data analysis from thinking slow to thinking fast? Here at Adoreboard we’ve been experimenting with different ways of analysing data to tell stories. In doing so we’ve collaborated with world renowned innovators from Havas helia to Ministry of Sound, here are 3 examples…
Three campaign examples of experiencing and interpreting data
Data visual experience
We analysed how the media and public viewed the highs and lows of golfer Rory McIlroy’s eventful year and presented our findings in a full-blown brand sponsorship report (download it for free here) from which we produced an audio-visual data interpretation. From the break-up of his relationship with tennis ace Caroline Wozniacki through to a Ryder Cup victory in September and beyond, we charted and expressed publicly expressed emotion through a 2 minute music and data visualisation.
Data music experience:
What if your brand had a beat, how would it sound and what would you change? Adoreboard teamed up with Ministry of Sound and Havas helia to create a house music interpretation of what Twitter users think about certain individuals and brand names. We used mathematical algorithms for 20 emotions expressed in tweets that turn them into melodies and rhythms. So feelings such as love, hate, anger, surprise, annoyance and trust each create their own individual sounds.
Data avatar experience:
Through collaboration with Cantoche, a French base specialist in avatars, we created a virtual media assistant who could process the emotions expressed online and convert this into a facial expression. Facial expressions are something we, as people, have a natural ability to instantly interpret – it’s amazing how quickly we can understand something without communicating through words.
It’s not all about the data
As a wise man once said, it’s not all about the data. If you can’t portray it in a human, connected way to business decision makers it never has an impact.
What are your thoughts? How can we move data understanding from thinking slow to thinking fast, so as to speed up the process of data understanding? How can data be portrayed in a more meaningful, humanistic way, appealing to the visual, auditory and kinesthetic communicators?
Golang Program To Check If A String Contains A Substring
A substring is a small string in a string and string in Golang is a collection of characters. Since strings in Go are immutable, they cannot be modified after they have been produced. Concatenating or adding to an existing string, however, enables the creation of new strings. A built-in type in Go, the string type can be used in a variety of ways much like any other data type.
Syntax strings.Contains(str,substring string)To determine whether a string contains a particular substring, use the Contains(s, substr string) bool function. If the substring is found in the supplied string, a boolean value indicating its presence is returned.
strings.Index(str, substring string)The int function index(s, str string) is used to determine the index of the first instance of a specified substring within a given string. It returns either -1 if the substring is missing or the index of the substring within the string.
strings.Index(str, substring string)The int function index(s, str string) is used to determine the index of the first instance of a specified substring within a given string. It returns either -1 if the substring is missing or the index of the substring within the string.
Algorithm
Step 1 − Create a package main and declare fmt(format package) and strings package
Step 2 − Create a function main and in that function create a string mystr
Step 3 − Using the string function, check whether the string contains the substring or not
Step 4 − Print the output
Example 1In this example we will see how to check if a string contains a substring using a built-in function strings.Contains(). The output will be a Boolean value printed on the console. Let’s see through the code and algorithm to get the concept easily.
package main import ( "fmt" "strings" ) func main() { mystr := "Hello,alexa!" fmt.Println("The string created here is:", mystr) substring := "alexa" fmt.Println("The substring from the string is:", substring) fmt.Println("Whether the substring is present in string or not?") fmt.Println(strings.Contains(mystr, substring)) } Output The string created here is: Hello,alexa! The substring from the string is: alexa Whether the substring is present in string or not? true Example 2In this example, we will see how to check if a string contains a substring or not using strings.Index() function.
package main import ( "fmt" "strings" ) func main() { mystr := "Hello, alexa!" fmt.Println("The string created here is:", mystr) substring := "alexa" fmt.Println("The substring from the string is:", substring) fmt.Println("Whether the string contains the substring or not?") fmt.Println("The string contains the substring.") } else { fmt.Println("The string does not contain the substring.") } } Output The string created here is: Hello, alexa! The substring from the string is: alexa Whether the string contains the substring or not? The string contains the substring. Example 3In this example we will see how to find if a string contains a substring using for loop in strings.Index() function −
package main import ( "fmt" "strings" ) func main() { mystr := "Hello, alexa!" fmt.Println("The string created here is:", mystr) substring := "alexa" fmt.Println("The substring present here is:", substring) fmt.Println("Whether the substring is present in string or not?") found := false for i := 0; i < len(mystr); i++ { if strings.Index(mystr[i:], substring) == 0 { found = true break } } if found { fmt.Println("The string has substring in it.") } else { fmt.Println("The string does not have substring in it.") } } Output The string created here is: Hello, alexa! The substring present here is: alexa Whether the substring is present in string or not? The string has substring in it. ConclusionWe executed the program of checking if a string contains a substring or not using three examples. In the first example we used strings.Contains() function, in the second example we used strings.Index() function and in the third example we used for loop with the former built-in function.
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