Trending December 2023 # No Santa Claus Rally For Ethereum! Analysts Predict A 50% Crash # Suggested January 2024 # Top 21 Popular

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It has been no Santa Claus rally for Ethereum due to the token’s historical holiday performances

It’s no Santa Claus rally for Ethereum cryptocurrency. As we approach the end of the year and the Christmas holiday, it’s a good time to reflect on the Ethereum token’s historical performance during the holiday season and forecast how it will perform in 2023.

According to historical statistics, Ethereum had had a constant year-on-year (YoY) rise during the previous three Christmases, reaching US$4,093 on December 25, 2023. Based on several technical analysis (TA) indicators, Ethereum’s Christmas positive trend will not continue the Santa Claus rally in 2023, with the asset expected to trade around US$915 on December 25, according to chúng tôi Ethereum price is now at US$1,255, implying a 23% drop if the forecasts come true. Only time will tell how closely Ethereum and Bitcoin will adhere to the holiday season predictions. Ethereum crash might soon be a reality for investors.

Notably, Christmas 2023 saw a 400% gain from 2023, with the first cryptocurrency changing hands for US$125 on December 25, 2023, followed by another jump on the yearly chart on December 25, 2023, when Bitcoin traded 543% higher than US 626 the previous year.

However, market shocks such as Russia’s invasion of Ukraine and the widely publicized collapse of the Terra (LUNA) ecosystem, as well as inflation and the recent crash of FTX, once one of the world’s largest crypto exchanges, have significantly altered the landscape, making it unlikely that this year will be a merry Christmas in terms of year-on-year growth.

Indeed, the price of Ethereum has plunged 69% since last Christmas, and it is currently trading at US$1,255, with little sign that this trend will reverse by Christmas 2023. With a total market cap of US$153.6 billion, the current ETH price is 0.84% down on the day but up 3.88% over the past seven days.

Observing the ETH technical analysis, it is evident that it is leaning to the sell side, with the summary on the 1-day gauge indicating ‘sell’ at 10 vs ‘buy’ at 7 and ‘neutral’ at 9.

Moving averages (MA) are in the sell zone with 8, according to a thorough examination of these indicators. Meanwhile, oscillators are at 8 indicating a ‘neutral’ attitude. According to a bottom fractal observed by independent market researcher Wolf, Ethereum’s native cryptocurrency, Ether, is poised for a significant bullish reversal after falling 25% from its November high of US$1,675.

Wolf contrasts Ethereum’s multi-month downturn from May 2023 to March 2023 with a comparable but shorter correction after July 2023. According to the researcher, if the trend continues, the price of Ether will have bottomed out in November 2023.

Wolf takes inspiration from the March 2023 Ethereum price fall, which was precipitated by the COVID-19 pandemic – a black swan event. Similarly, the bankruptcy of cryptocurrency exchange FTX in November 2023 pulled down the price of ETH. Furthermore, Cold Blood Shiller, an independent market analyst, observes an “obvious breakthrough point” on Ethereum’s daily chart, namely its Awesome Oscillator (AO) and Relative Strength Index (RSI) (RSI). Both indications look to have lately turned bullish.

Nonetheless, Ether is already down 75% from its peak in November 2023, with the market experiencing many bull traps since then.

Aditya Siddhartha Roy, a market analyst, observes the possibility of a similar bull trap forming in the current micro rally, which he believes risks exhaustion at a multi-month descending resistance trendline.

A substantial pullback from the declining trendline would bring Ether toward $700, which Roy describes as a “potential bottom.”

However, following the March 2023 drop, ETH/USD rallied strongly, aided by the Federal Reserve’s rate cuts, which pumped more money into the economy, some of which went into the crypto market.

Similarly, Ether’s slight comeback post-FTX “black swan” in November 2023 aligns with increased anticipation of the Fed halting its rate rises. As a result, Ether is likely to repeat the March 2023 fractal to new monthly highs.

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Engineering Professor Elise Morgan Looks For A Better Way To Predict Spine Fractures

Bonecrusher Engineering professor Elise Morgan looks for a better way to predict spine fractures

Using the tools of engineering, BU Professor of Mechanical Engineering Elise Morgan wants to find a better way to predict spine fractures. Photo by Cydney Scott

As far as age-related fractures go, broken hips are the biggie, the injury that people fear most—the one that could end your life as you know it, or end your life, period. But while broken hips garner the most attention, spine fractures are far more common, affecting approximately 25 percent of all postmenopausal women in the United States, according to the journal American Family Physician. The prevalence of these fractures rises as we get older, affecting 40 percent of women who reach age 80 and nearly 20 percent of men over the age of 50. And the consequences can be serious: back pain, height loss, hunched posture, and immobility.

Spine fractures are insidious because many go undiagnosed, brushed aside as the back pain that comes with getting older. But they indicate risk for even more spine fractures, and hip fractures as well. If a doctor misses someone at high risk, the patient may have more—and worse—broken bones down the road.

Despite the big numbers and enormous consequences, age-related spine fractures remain difficult to predict, so people who might benefit from preventative treatment often don’t get it (and some people who do get treatment may not need it most). Elise Morgan, a professor of mechanical engineering in Boston University’s College of Engineering (ENG), wants to change that, by finding a better way to predict age-related spinal fractures. “We’re trying to develop methods for identifying who is really at risk for fracture,” she says, “methods that are much better than what’s currently used.” She’s doing it in an unusual way, by using the tools of mechanical engineering, techniques more commonly used to measure stress and strain on bridges and buildings.

“Elise’s engineering background gives her a fundamental understanding of mechanics, which informs every part of her work,” says Paul Barbone, an ENG professor of applied mechanics and theoretical acoustics who collaborates with Morgan. “She can look at the problem from both sides: how do the bones hold up as a structure, and how does bone growth and development play a role? It gives her a unique perspective.”

Doctors currently assess a patient’s risk of spine fracture with a bone density scan, basically an X-ray that measures how much bone you have and how dense it is. The scan generates a “T-score,” which classifies a person’s bone density as compared to the rest of the population. Using the T-score, a doctor categorizes you as either normal, osteoporotic, or “osteopenic”—a term meaning almost-but-not-quite osteoporotic. The problem, says Morgan, is this: about half the people who fracture their spine are classified as normal based on their T-score. “Clearly the scan is missing something,” she says.

Morgan and her team, with funding from the National Institutes of Health (NIH), want to develop a screening technique that’s better able to predict who will actually fracture. That’s difficult, though, because it’s not always clear how spines fracture. That’s where Morgan’s engineering background comes in.

“As a mechanical engineer I’m thinking, well, we’ve got this exquisite structure that is the spine, and even a single vertebra itself is a fairly complicated structure,” she says. “The first thing we need to do is really understand how these fractures occur. Otherwise, it’s hard to predict whether they will occur. And so that’s really a mechanical engineering problem: How is a structure failing?”

To answer this question, Morgan and her colleagues took human vertebrae—yes, actual human vertebrae from people who donated their bodies to science—and squeezed them until they broke. This type of testing is unusual in itself: usually when engineers want to examine the mechanical properties of a material, they take small samples and craft them into a standardized size and shape for testing. This makes sense for, say, titanium alloys, but not for bone. “Bones don’t come in standard shapes and sizes, and the properties of the tissue vary throughout the bone,” says Barbone. Testing whole vertebrae, he adds, “is like testing hundreds of samples, all at once, in their native configuration, interacting with their neighbors as they would normally.”

Morgan’s team squashes the vertebrae using a pair of machines that not only measure force and deformation but also grab images of the vertebrae with a micro-CT scan. “We do that imaging while we’re doing the mechanical testing, so we get time-lapse series of images of how the vertebrae are failing,” says Morgan.

Much of the original testing fell to Amira Hussein (ENG’13), a postdoctoral fellow on BU’s Medical Campus who worked on the project as a PhD candidate in Morgan’s lab. “The human skeleton is a mechanical system but it grows and adapts in response to stress, so everyone’s vertebrae are different,” she says. “That’s really cool.”

Morgan’s team used computational tools developed in Barbone’s lab to interpret and analyze the image data. Hussein says this step was critical for finding exactly when and where a vertebra “failed,” or lost its ability to support a load: “Once we could see where the failure took place, we looked to see what was special about the structure of that part of the bone. Was there anything different that could help us predict fracture?”

The tests revealed some surprises, says Morgan. They found that almost all vertebrae failed when depressions and cracks appeared in the center of the top endplate—the flat circle of bone tissue that caps each vertebra—rather than in the very low density bone inside. It didn’t matter if the researchers tested vertebrae from the lower or middle portion of the spine, and it didn’t matter if the machine squeezed the samples vertically or flexed them in an arch, the way the spine bends when someone reaches down to pick up a bag of groceries—the vertebrae all failed on the top endplate. But why there?

Further investigation showed that the endplate tended to fail first where it was the most porous, with the weakest internal microstructure. While one might expect weak, porous bone to fail first, nobody had experimentally identified this link between endplate microstructure and failure of the entire vertebra. This finding may prove useful for diagnosis, says Morgan: doctors viewing X-rays and CT scans of patients’ spines often report depressions of the top endplate, she says, a finding that may likely indicate a spine fracture. The research, conducted mainly by Hussein and her fellow graduate student Timothy Jackman (ENG’15), was published in the Journal of Orthopaedic Research in July 2014, with follow-up studies set to appear in the Journal of Bone and Mineral Research and the Journal of Biomechanics in early 2023.

Morgan’s team used this data to build computer models that simulate spine fracture, models that led them one step closer to a predictive test. “With the 3D imaging, we were able to see the whole volume of the vertebra and where it fails, and then to compare this to the model to determine whether the model is working correctly,” says Hussein. “That was a major contribution.”

Morgan is now using CT scans from Framingham Heart Study participants—a large and well-established study group—to further test and refine her diagnostic model. She hopes that in a few years, this data may supply enough information to create a reliable test.

“Right now it’s really hard to identify who is being over-treated for osteoporosis, and who’s not being treated at all but should be,” says Morgan. “We want to change that.”

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Analysts Still Bullish On Rim

Research In Motion disappointed investors with solid — but not spectacular — numbers for its first-quarter earnings yesterday, but analysts are still bullish on the BlackBerry maker.

RIM’s (NASDAQ: RIMM) adjusted earnings of $0.98 per share topped Wall Street expectations by four cents, the company reported a 53 percent jump in sales to $3.42 billion, in line with analyst estimates. But RIM’s 3.8 million new subscribers came in just below expectations.

“We are starting fiscal 2010 with strong financial performance and impressive market share gains, including a 55 percent share of the U.S. smartphone market according to IDC’s latest estimate,” Jim Balsillie, RIM’s co-CEO, said during the company’s earnings call. “The industry-leading BlackBerry product portfolio is driving strong customer demand around the world and our penetration of new market segments continues to expand.”

Peter Misek, analyst at Canaccord Adams, came away from the results with a mixed outlook.

“On one hand, device sales were weak, gross margins could again see the low 40s and enterprise net [subscription] adds were soft,” Misek wrote today in a report. “On the other hand, operating margins were solid and inventory levels remain very low, which could fuel unit upside when carriers start to replenish stock.”

Though he cited some concern, Misek remains optimistic about RIM’s future performance in the fiercely competitive smartphone market.

“In the end, we saw nothing that changes our very bullish long-term outlook on RIM, especially with a series of impressive new product launches on the horizon,” he wrote. “However, we are becoming increasingly concerned by the broader consumer electronics spending outlook. Our checks continue to suggest that the bounce we saw in early spring has begun to fade and, without the consumer tailwind at RIM’s back, we believe it prudent to remain on the sidelines.”

RIM’s earnings report comes amid ever-increasing competition in the smartphone segment, with the new iPhone 3G S going on sale today and the Palm Pre entering its second week of availability.

When asked during yesterday’s earnings call how RIM will fare given the competition — including Apple’s move to cut the price of the previous-generation iPhone 3G to $99 — Balsillie said RIM and the BlackBerry were ready for a fight, though he refused to mention his rivals by name.

“We’ve had very aggressive promotions through carriers, BlackBerries for $49 and so on, and I’m not one to follow these other things too closely, but that one $99 model, that’s a year-old product, so I don’t think that’s a big structural thing happening,” he said. “The other product [the Palm Pre] is brand new, so it’s too early to tell what will happen, but we’ve always focused on our own value proposition.”

“We’ve demonstrated a huge surge of strength recently and we’re not taking our foot off the gas, so extrapolate from that as you wish,” he added.

Balsillie also pointed to the fact that a record 80 percent of RIM’s new subscriptions came from the consumer side. That’s an area the company’s business — which has long centered purely on the enterprise space — that it’s been aiming to expand.

“We’ve made dramatic progress in penetration of new market segments,” he said. “Non-enterprise customers … now represent more than half of BlackBerry accounts.”

Will Stofega, analyst at IDC, said the boost in non-enterprise signals RIM’s success in diversifying its customer base.

“There are some issues related to the enterprise — budgets are being cut, there’s seasonal issues with the buying cycle — but they’re picking up the slack on the consumer side,” he said. “Overall, they’re doing everything they need to be doing.”

And though Apple will be grabbing headlines today with sales of the iPhone 3G S, for its part, RIM just unveiled the BlackBerry Tour, which analysts say will help strengthen its position in the market.

Meanwhile, industry watchers expect RIM this summer to release an update to the Storm, its answer to the iPhone’s touchscreen interface.

Article courtesy of chúng tôi

Top 50 Google Interview Questions For Data Science Roles


Cracking the code for a career at Google is a dream for many aspiring data scientists. But what does it take to clear the rigorous data science interview process? To help you succeed in your interview, we compiled a comprehensive list of the top 50 Google interview questions covering machine learning, statistics, product sense, and behavioral aspects. Familiarize yourself with these questions and practice your responses. They can enhance your chances of impressing the interviewers and securing a position at Google.

Google Interview Process for Data Science Roles

Getting through the Google data scientist interview is an exciting journey where they assess your skills and abilities. The process includes different rounds to test your knowledge in data science, problem-solving, coding, statistics, and communication. Here’s an overview of what you can expect:

StageDescriptionApplication SubmissionSubmit your application and resume through Google’s careers website to initiate the recruitment process.Technical Phone ScreenIf shortlisted, you’ll have a technical phone screen to evaluate your coding skills, statistical knowledge, and experience in data analysis.Onsite InterviewsSuccessful candidates proceed to onsite interviews, which typically consist of multiple rounds with data scientists and technical experts. These interviews dive deeper into topics such as data analysis, algorithms, statistics, and machine learning concepts.Coding and Analytical ChallengesYou’ll face coding challenges to assess your programming skills and analytical problems to evaluate your ability to extract insights from data.System Design and Behavioral InterviewsSome interviews may focus on system design, where you’ll be expected to design scalable data processing or analytics systems. Additionally, behavioral interviews assess your teamwork, communication, and problem-solving approach.Hiring Committee ReviewThe feedback from the interviews is reviewed by a hiring committee, which collectively makes the final decision regarding your candidacy.

We have accumulated the top 50 Google interview questions and answers for Data Science roles.

Top 50 Google Interview Questions for Data Science

Prepare for your Google data science interview with this comprehensive list of the top 50 interview questions covering machine learning, statistics, coding, and more. Ace your interview by mastering these questions and showcasing your expertise to secure a position at Google. 

Google Interview Questions on Machine Learning and AI

1. What is the difference between supervised and unsupervised learning?

A. Supervised learning involves training a model on labeled data where the target variable is known. On the other hand, unsupervised learning deals with unlabeled data, and the model learns patterns and structures on its own. To know more, read our article on supervised and unsupervised learning.

2. Explain the concept of gradient descent and its role in optimizing machine learning models.

A. Gradient descent is an optimization algorithm used to minimize the loss function of a model. It iteratively adjusts the model’s parameters by calculating the gradient of the loss function and updating the parameters in the direction of the steepest descent.

3. What is a convolutional neural network (CNN), and how is it applied in image recognition tasks?

A. A CNN is a deep learning model designed explicitly for analyzing visual data. It consists of convolutional layers that learn spatial hierarchies of patterns, allowing it to automatically extract features from images and achieve high accuracy in tasks like image classification.

4. How would you handle overfitting in a machine-learning model?

A. Overfitting occurs when a model performs well on training data but poorly on unseen data. Techniques such as regularization (e.g., L1 or L2 regularization), early stopping, or reducing model complexity (e.g., feature selection or dimensionality reduction) can be used to address overfitting.

A. Transfer learning involves using pre-trained models on large datasets to solve similar problems. It allows leveraging the knowledge and features learned from one task to improve performance on a different but related task, even with limited data.

6. How would you evaluate the performance of a machine learning model?

A. Common evaluation metrics for classification tasks include accuracy, precision, recall, and F1 score. For regression tasks, metrics like mean squared error (MSE) and mean absolute error (MAE) are often used. Also, cross-validation and ROC curves can provide more insights into a model’s performance.

7. What is the difference between bagging and boosting algorithms?

A. The main difference between bagging and boosting algorithms lies in their approach to building ensemble models. Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and combining their predictions through averaging or voting. It aims to reduce variance and improve stability. On the other hand, boosting algorithms, such as AdaBoost or Gradient Boosting, sequentially train models, with each subsequent model focusing on the samples that were misclassified by previous models. Boosting aims to reduce bias and improve overall accuracy by giving more weight to difficult-to-classify instances.

8. How would you handle imbalanced datasets in machine learning?

A. Imbalanced datasets have a disproportionate distribution of class labels. Techniques to address this include undersampling the majority class, oversampling the minority class, or using algorithms designed explicitly for imbalanced data, such as SMOTE (Synthetic Minority Over-sampling Technique).

Google Data Scientist Interview Questions on Statistics and Probability

9. Explain the Central Limit Theorem and its significance in statistics.

A. The Central Limit Theorem states that the sampling distribution of the mean of a large number of independent and identically distributed random variables approaches a normal distribution, regardless of the shape of the original distribution. It is essential because it allows us to make inferences about the population based on the sample mean.

10. What is hypothesis testing, and how would you approach it for a dataset?

A. Hypothesis testing is a statistical method used to make inferences about a population based on sample data. It involves formulating a null and alternative hypothesis, selecting an appropriate test statistic, determining the significance level, and making a decision based on the p-value.

11. Explain the concept of correlation and its interpretation in statistics.

A. Correlation measures the strength and direction of the linear relationship between two variables. It ranges from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation. The correlation coefficient helps assess the degree of association between variables.

12. What are confidence intervals, and how do they relate to hypothesis testing?

A. Confidence intervals provide a range of plausible values for a population parameter based on sample data. They are closely related to hypothesis testing as they can test hypotheses about population parameters by examining whether the interval contains a specific value.

13. What is the difference between Type I and Type II errors in hypothesis testing?

A. Type I error occurs when a true null hypothesis is rejected (false positive), while Type II error occurs when a false null hypothesis is not rejected (false negative). Type I error is typically controlled by selecting an appropriate significance level (alpha), while the power of the test controls Type II error.

14. How would you perform hypothesis testing for comparing two population means?

A. Common methods for comparing means include the t-test for independent samples and the paired t-test for dependent samples. These tests assess whether the observed mean difference between the two groups is statistically significant or occurred by chance.

15. Explain the concept of p-value and its interpretation in hypothesis testing.

A. The p-value is the probability of obtaining results as extreme as or more extreme than the observed data, assuming the null hypothesis is true. A lower p-value indicates stronger evidence against the null hypothesis, leading to its rejection if it is below the chosen significance level.

16. What is ANOVA (Analysis of Variance), and when is it used in statistical analysis?

A. ANOVA is a statistical method used to compare multiple groups or treatments. It determines whether there are statistically significant differences between the group means by partitioning the total variance into between-group and within-group variance.

Google Interview Questions on Coding 17. Write a Python function to calculate the factorial of a given number. def factorial(n):     if n == 0:         return 1     else:         return n * factorial(n-1) 18. Write a Python code snippet to reverse a string. def reverse_string(s): return s[::-1] 19. Write a function in Python to find the maximum product of any two numbers in a given list of integers. def max_product(numbers):     numbers.sort()     return numbers[-1] * numbers[-2] 20. Implement a Python class named Stack with push and pop operations.  class Stack:     def __init__(self):         self.stack = []     def push(self, item):         self.stack.append(item)     def pop(self):         if self.is_empty():             return None         return self.stack.pop()     def is_empty(self):         return len(self.stack) == 0 21. Given a list of integers, write a Python function to find the longest increasing subsequence (not necessarily contiguous) within the list.  def longest_increasing_subsequence(nums):     n = len(nums)     lis = [1] * n     for i in range(1, n):         for j in range(i):                 lis[i] = lis[j] + 1     return max(lis) 22. Implement a Python function to count the number of inversions in an array. An inversion occurs when two elements in the collection are out of their sorted order. def count_inversions(arr):     count = 0     for i in range(len(arr)):         for j in range(i + 1, len(arr)):                 count += 1     return count 23. Write a Python code snippet to find the median of two sorted arrays of equal length. def find_median_sorted_arrays(arr1, arr2):     merged = sorted(arr1 + arr2)     n = len(merged)     if n % 2 == 0:         return (merged[n     else:         return merged[n 24. Write a Python code snippet to check if a given string is a palindrome. def is_palindrome(s):     return s == s[::-1] 25. Implement a Python function to find the missing number in a given list of consecutive integers starting from 1. ofdef find_missing_number(nums):     n = len(nums) + 1     expected_sum = (n * (n + 1))     actual_sum = sum(nums)     return expected_sum - actual_sum 26. Write a Python function to remove duplicate elements from a given list. def remove_duplicates(nums):     return list(set(nums)) Google Interview Questions on Product Sense

27. How would you design a recommendation system for an e-commerce platform like Amazon?

A. To design a recommendation system, I would start by understanding the user’s preferences, historical data, and business goals. I recommend collaborative techniques, content-based filtering, and hybrid approaches to personalize recommendations and enhance the user experience.

28. Suppose you are tasked with improving user engagement on a social media platform. What metrics would you consider, and how would you measure success? 29. How would you design a pricing model for a subscription-based service like Netflix?

A. Designing a pricing model for a subscription-based service would involve considering factors such as content offerings, market competition, customer segmentation, and willingness to pay. Conducting market research, analyzing customer preferences, and conducting price elasticity studies would help determine optimal pricing tiers.

30. Imagine you are tasked with improving the search functionality of a search engine like Google. How would you approach this challenge?

A. Improving search functionality would involve understanding user search intent, analyzing user queries and feedback, and leveraging techniques like natural language processing (NLP), query understanding, and relevance ranking algorithms. User testing and continuous improvement based on user feedback would be crucial in enhancing the search experience.

31. How would you measure the impact and success of a new feature release in a mobile app?

A. To measure the impact and success of a new feature release, I would analyze metrics such as user adoption rate, engagement metrics (e.g., time spent using the feature), user feedback and ratings, and key performance indicators (KPIs) tied to the feature’s objectives. A combination of quantitative and qualitative analysis would provide insights into its effectiveness.

32. Suppose you are tasked with improving the user onboarding process for a software platform. How would you approach this?

A. Improving user onboarding would involve understanding user pain points, conducting user research, and implementing user-friendly interfaces, tutorials, and tooltips. Collecting user feedback, analyzing user behavior, and iteratively refining the onboarding process would help optimize user adoption and retention.

33. How would you prioritize and manage multiple concurrent data science projects with competing deadlines?

A. Prioritizing and managing multiple data science projects require practical project management skills. I would assess the project goals, resource availability, dependencies, and potential impact on business objectives. Techniques like Agile methodologies, project scoping, and effective stakeholder communication help manage and meet deadlines.

34. Suppose you are asked to design a fraud detection system for an online payment platform. How would you approach this task?

A. Designing a fraud detection system would involve utilizing machine learning algorithms, anomaly detection techniques, and transactional data analysis. I would explore features like transaction amount, user behavior patterns, device information, and IP addresses. Continuous monitoring, model iteration, and collaboration with domain experts would be essential for accurate fraud detection.

Additional Practise Questions

35. Explain the concept of A/B testing and its application in data-driven decision-making.

A. A/B testing is a method used to compare two versions (A and B) of a webpage, feature, or campaign to determine which performs better. It helps evaluate changes and make data-driven decisions by randomly assigning users to different versions, measuring metrics, and determining statistical significance.

36. How would you handle missing data in a dataset during the analysis process?

A. Handling missing data can involve techniques such as imputation (replacing missing values), deletion (removing missing observations), or considering missingness as a separate category. The choice depends on the nature of the missingness, its impact on analysis, and the underlying assumptions of the statistical methods.

37. Explain the difference between overfitting and underfitting in machine learning models.

A. Overfitting occurs when a model performs well on training data but poorly on new data due to capturing noise or irrelevant patterns. On the other hand, underfitting happens when a model fails to capture the underlying patterns in the data and performs poorly on training and new data.

38. What are regularization techniques, and how do they help prevent overfitting in machine learning models?

A. Regularization techniques (e.g., L1 and L2 regularization) help prevent overfitting by adding a penalty term to the model’s cost function. This penalty discourages complex models, reduces the impact of irrelevant features, and promotes generalization by balancing the trade-off between model complexity and performance.

39. What is the curse of dimensionality in machine learning, and how does it affect model performance? 40. Explain the concept of bias-variance trade-off in machine learning models.

A. The bias-variance trade-off refers to the balance between a model’s ability to fit the training data (low bias) and generalize to new, unseen data (low variance). Increasing model complexity reduces bias but increases variance while decreasing complexity increases bias but reduces variance.

41. What is the difference between supervised and unsupervised learning algorithms?

A. Supervised learning involves training a model with labeled data, where the target variable is known, to make predictions or classifications on new, unseen data. On the other hand, unsupervised learning involves finding patterns and structures in unlabeled data without predefined target variables.

42. What is cross-validation, and why is it important in evaluating machine learning models?

A. Cross-validation is a technique used to assess a model’s performance by partitioning the data into multiple subsets (folds) and iteratively training and evaluating the model on different combinations of folds. It helps estimate a model’s ability to generalize to new data and provides insights into its robustness and performance.

Behavioral Questions

43. Tell me about when you had to solve a complex problem in your previous role. How did you approach it?

A. In my previous role as a data scientist, I encountered a complex problem where our predictive model was not performing well. I approached it by conducting thorough data analysis, identifying potential issues, and collaborating with the team to brainstorm solutions. Through iterative testing and refining, we improved the model’s performance and achieved the desired outcomes.

44. Describe a situation where you had to work on a project with a tight deadline. How did you manage your time and deliver the results?

A. We had a tight deadline to develop a machine learning model during a previous project. I managed my time by breaking down the tasks, prioritizing critical components, and creating a timeline. I communicated with stakeholders to set realistic expectations and gathered support from team members.

45. Can you share an experience when you faced a disagreement or conflict within a team? How did you handle it?

A. In a team project, we disagreed regarding the approach to solving a problem. I initiated an open and respectful discussion, allowing everyone to express their views. I actively listened, acknowledged different viewpoints, and encouraged collaboration. We reached a consensus by finding common ground and combining the strengths of various ideas. The conflict resolution process strengthened our teamwork and led to a more effective solution.

46. Tell me about when you had to adapt to a significant project or work environment change. How did you handle it?

A. In a previous role, our project requirements changed midway, requiring a shift in our approach and technologies. I embraced the change by researching and learning the tools and techniques. I proactively communicated with the team, ensuring everyone understood the revised objectives and milestones. We successfully navigated the change and achieved project success.

47. Describe a situation where you had to work with a challenging team member or stakeholder. How did you handle it?

A. I encountered a challenging team member with a different working style and communication approach. Therefore, I took the initiative to build rapport and establish open lines of communication. I listened to their concerns, found common ground, and focused on areas of collaboration.

48. Can you share an experience where you had to make a difficult decision based on limited information or under time pressure?

A. In a time-sensitive project, I faced a situation where critical data was missing, and a decision must be made urgently. I gathered available information, consulted with subject matter experts, and assessed potential risks and consequences. I made a decision based on my best judgment at that moment, considering the available evidence and the project objectives. Although it was challenging, the decision proved to be effective in mitigating potential issues.

49. Tell me about when you took the initiative to improve a process or implement an innovative solution in your work.

A. In my previous role, I noticed inefficiencies in the data preprocessing pipeline, which impacted the overall project timeline. I took the initiative to research and propose an automated data cleaning and preprocessing solution using Python scripts. I collaborated with the team to implement and test the solution, significantly reducing manual effort and improving data quality. This initiative enhanced the project’s efficiency and showcased my problem-solving skills.

50. Describe a situation where you had to manage multiple tasks simultaneously. How did you prioritize and ensure timely completion?

A. I had to juggle multiple projects with overlapping deadlines during a busy period. Hence, I organized my tasks by assessing their urgency, dependencies, and impact on project milestones. I created a priority list and allocated dedicated time slots for each task. Additionally, I communicated with project stakeholders to manage expectations and negotiate realistic timelines. I completed all tasks on time by staying organized, utilizing time management techniques, and maintaining open communication.

Questions to Ask the Interviewer at Google

Can you provide more details about the day-to-day responsibilities of a data scientist at Google?

How does Google foster collaboration and knowledge-sharing among data scientists within the company?

What current challenges or projects is the data science team working on?

How does Google support the professional development and growth of its data scientists?

Can you tell me about the tools and technologies data scientists commonly use at Google?

How does Google incorporate ethical considerations into its data science projects and decision-making processes?

What opportunities exist for cross-functional collaboration with other teams or departments?

Can you describe the typical career progression for a data scientist at Google?

How does Google stay at the forefront of innovation in data science and machine learning?

What is the company culture like for data scientists at Google, and how does it contribute to the team’s overall success?

Tips for Acing Your Google Data Scientist Interview

Understand the company: Research Google’s data science initiatives, projects, and technologies. Familiarize yourself with their data-driven approach and company culture.

Strengthen technical skills: Enhance your knowledge of machine learning algorithms, statistical analysis, and coding languages like Python and SQL. Practice solving data science problems and coding challenges.

Showcase real-world experience: Highlight your past data science projects, including their impact and the methodologies used. Emphasize your ability to handle large datasets, extract insights, and provide actionable recommendations.

Demonstrate critical thinking: Be prepared to solve complex analytical problems, think critically, and explain your thought process. Showcase your ability to break down problems into smaller components and propose innovative solutions.

Communicate effectively: Clearly articulate your ideas, methodologies, and results during technical interviews. Practice explaining complex concepts simply and concisely.

Practice behavioral interview questions: Prepare for behavioral questions that assess your teamwork, problem-solving, and leadership skills. Use the STAR method (Situation, Task, Action, Result) to structure your responses.

Be adaptable and agile: Google values individuals who can adapt to changing situations and are comfortable with ambiguity. Showcase your ability to learn quickly, embrace new technologies, and thrive in a dynamic environment.

Ask thoughtful questions: Prepare insightful questions to ask the interviewer about the role, team dynamics, and the company’s data science initiatives. This demonstrates your interest and engagement.

Practice, practice, practice: Use available resources, such as mock interviews and coding challenges, to simulate the interview experience. Practice time management, problem-solving, and effective communication to build confidence and improve performance.

Meet Data Scientists at Google

Source: Life at Google



Callback A Function In The Event That A Video Is 50 Buffered

In this tutorial, we will show how you can get the buffer percentage of a video in chúng tôi and use a callback function when the video has buffered more than 50%. chúng tôi is a well-known online video player JavaScript toolkit that is used to create web browser video players for a range of video formats. chúng tôi is a very flexible and customizable library to create modern web video players. It supports a wide range of packages, plugins, and options. Using chúng tôi any part of an HTML video player can be configured as per your liking.

For the purpose of this tutorial, we’re going to invoke a callback function when a video is 50% buffered in chúng tôi First, we’re going to learn how to get the buffer percentage of a video using chúng tôi and then we’ll add a callback function to trigger when the video has buffered by 50%. Let’s move on to the next section of this tutorial to understand how can we achieve the same using video.js.

Callback a Function in the event that a Video is 50% Buffered

Video buffering is the pre-loading of video segments for streaming the video. This technique has been used by many popular video streaming sites for streaming the video. Some part or segments of the video is preloaded and the video is played so that the end user doesn’t have to wait for downloading the complete video.

Prerequisite − Assuming that you know how to create a basic video player using the chúng tôi library.

Video Buffer Percentage is the amount of video buffered out of the total length of the video. We can get the buffer percentage of a video using chúng tôi by making use of the bufferPercentage() method on video player reference. This method return a percentage of video buffered till that specific time in decimal format.

However, the value of buffer percentage changes with the progress of the video i.e. it goes from 0% buffer to 100% buffered depending on how much the video has played, how fast is the bandwidth of the user, etc. So, to keep track of the buffer percentage with the progress of the video, we need to listen to the ‘progress’ event

Progress Event Listener

The progress event occurs in the web browser whenever a resource is being loaded, i.e., whenever a video or audio is being loaded from a URL or a third-party source, this event is triggered. So, the progress event can be used to track the buffer percentage of our video as soon as the loading progress of a video changes.

You can use this event listener by using ‘play’ method on the reference of your chúng tôi player using the player.on() method. Callback functions passed (as an argument) to this event listener are going to be executed as soon as the video starts loading.

You will need the following code to use the progress event to get the buffer percentage on the chúng tôi player −

const player = videojs(‘my-video-player’); player.on(‘progress’, function () { const buffPercentage = player.bufferedPercent(); console.log(“Buffer Percentage: “, buffPercentage); });

Then we used the progress event to capture the changes in the loading state of a video and in the callback function of this event, we used the bufferPercent() method to get the latest percentage of the video that is buffered.

When you execute the above code, the video buffer percentage will be logged in the browser console video as soon as the video is pre-loaded. You’ll see multiple logs like this −

Buffer Percentage: 0 Buffer Percentage: 0.010983981771910626 Buffer Percentage: 0.02974826966353168 Buffer Percentage: 0.05308923092884176 Buffer Percentage: 0.0733713463484793 Buffer Percentage: 0.09037202366684796 Buffer Percentage: 0.1020040768163907

These are the multiple buffer percentages as the video is being pre-loaded. Since these values are decimal, a buffer percentage of 0.1 means that 10% of the video has buffered.

Now that, we’ve understood how to get the video percentage of the video, we can now check if the video percentage is above 50% (or 0.5 decimal value) and call a function or do some task basis on that.

Consider the below code snippet for alerting a text on the browser window if the video buffer percentage is more than 50%:

const player = videojs(‘my-video-player’);

const buffPercentage = player.bufferedPercent(); console.log(“Buffer Percentage”, buffPercentage);

window.alert(‘50% of the video has been buffered.’); } });

As you can see in the above code, we’re getting the buffer percentage of the video using the bufferedPercent method. Then we checked if the buffer percentage is greater than 0.5 i.e if the video buffered more than 50%. If that’s the case, we’ve alerted ‘50% of the video has been buffered.’ in the buffered windows.

Instead of alerting something, you can also use a callback function to do a specific task.

Example 1

Using the above code snippet with the complete example for alerting a text in the browser window if the video has buffered more than 50% will look this −


preload=”true” controls=”true” fluid=”true” muted=”true” autoplay=”true” poster=”assets/sample.png” data-setup='{}’ <source type=”video/mp4″

const player = videojs(‘my-video-player’);

const buffPercentage = player.bufferedPercent(); console.log(“Buffer Percentage”, buffPercentage);

window.alert(‘50% of the video has been buffered.’); } });

In the above code example, we’ve implemented the following −

For the source, we’ve used an mp4 video, for which we’ve mentioned the MIME type as video/mp4 in the source tag.

After initializing the video player, we used the player.on method to invoke an event listener. The event used is ‘progress’, which is responsible for invoking the callback function whenever the video is being buffered or preloaded

We then used the bufferedPercent() method on the video player instance to get the current percentage of video buffered and if the video has buffered more than 50%, a text is shown in the browser window.

When you execute the above code in a web browser, it will show ‘50% of the video has been buffered.’ when the video is buffered more than 50%. So, we’ve successfully learned how to handle the event when a video is 50% buffered in video.js.


In this tutorial, we understood how to capture an event when a video is 50% in chúng tôi We used the bufferedPercent() method exposed by chúng tôi to get the current buffered percentage of the video. Apart from that, the ‘progress’ event listener was used to capture the changes in the buffered state of the video. Finally, we showed an alert in the browser window if the video buffer percentage was more than 50%. We also had a look at fully working example for the same.

Ethereum Signals A Strong Green When Bitcoin Is Still Flickering

Ethereum is will overtake Bitcoin to become the largest asset in the crypto market

Bitcoin and Ethereum, the two most powerful cryptocurrencies in the world have plunged by over 40% in the past couple of months. Recently Bitcoin dipped below US$20k, dragging Ethereum’s price down to US$900, which is its lowest since January 2023. With continued inflation, changing monetary regulations, and falling values of the S&P 500 stocks, it is quite evident that the investment community is in immense turmoil, the falling value of the crypto market is an added illness that does not seem will cure so soon. Ethereum has undergone weeks of bearish price movements after which almost all altcoins fell below investors’ expectations. Nevertheless, the ETH token is still in a far better position than Bitcoin. Even though bears are in control of the market, Ethereum seems to be undertaking Bitcoin. As the Bitcoin price is having a hard time recovering from its fall below the US$20k, Ethereum has already grown by almost 11% just within a couple of days. Maybe predictions about how ETH might become the new crypto king are about to become true!

According to coinmarketcap, the Ethereum price dropped to US$800 on June 19, but gradually it rose to around US$990, later the same day. Currently, at the time of writing this article, Ether is trading at around US$1079.81. Coming to Bitcoin, the crypto fell below US$18,000 and for a brief moment was revolving around US$17,000. Currently, it might seem like Bitcoin is recovering from its drastic fall, but its recovery has been extremely slow. At the time of writing its article, BTC is priced at US$20,000. For the past several days, the crypto has been valued between the US$19k to US$20 range. These price movements surely indicate that Ethereum is a stronger competitor than Bitcoin and might soon rise in value, facilitating more profits for ETH users.

How is Ethereum recovering so fast?

Ethereum did lose much of its intrinsic value this week. The value of Ethereum did go down over the past several days, which also led the crypto to lose investors’ trust in it. The media coverage on Ethereum has also not been quite positive since Buterin pushed back the launch of the Ethereum 2.0 upgrade again! But it seems like the investor community might have forgiven Buterin for his steps. The price action for Ethereum has been insanely bearish, but the crypto has been recovering at a pretty rapid pace.

The Ethereum upgrade is nearing its launch and might even entice investors to invest in it. The goal of the upgrade is to make Ethereum more scalable, secure, and sustainable. Besides, the crypto would also make its crypto mining obsolete, which will eventually reduce the massive amounts of energy required to create tokens. Ethereum currently possesses a market cap of US$360 billion, whereas, Bitcoin has acquired a market value of US$804 billion, but experts believe that if Ether continues with its current price movements, it might soon become the leader of the crypto market.

What will happen to Bitcoin?

The future of Bitcoin is quite unpredictable at this point. Academics say that Bitcoin might not last much longer if its price continues to fall this rapidly. Some critics have even joked about BTC’s falling market value and have said that the price of the token might soon fall down to US$0, making cryptocurrencies obsolete as an investment asset. Even though Bitcoin is the underlying technology behind most cryptocurrencies. It is essentially a digital ledger of virtual currency transactions which is distributed across a global network of computers.

Bitcoin’s price recently fell down to US$18,000 from its all-time highs of US$68,000 which makes it one of the most volatile cryptocurrencies in the market. But the competition might yet not be over for BTC because most investors who still believe in BTC’s cause continue to buy the dip. A few days ago, experts estimated that the number of Bitcoin wholesalers has risen to new all-time highs, which indicates that BTC is surely not going to lose its prominence anytime soon.

In a nutshell, it is yet not clear how which cryptocurrency which eventually become the leader of the cryptocurrency market, but it is quite evident that Ethereum’s robust market movements might attract the investors that the market lost within the past few months.

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