You are reading the article Top 10Computer Science (Data Analytics) Courses To Take Up updated in December 2023 on the website Cancandonuts.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 Top 10Computer Science (Data Analytics) Courses To Take UpTo grow your career in the field of analytics, these M. Sc. Computer Science (Data Analytics) courses are the best.
High-end fields likeOUCW KOTI – Osmania University College For Women
Master of Science (M.Sc) inSt. Xavier’s College (Autonomous)
The chúng tôi in BigUniversity of Bath
This online MSc course is focused on teaching the principles of programming in general. For example, in the first unit, you will learn the basics of programming using the C language and object-oriented programming using Java. You will subsequently be introduced to other languages (such as Python, Haskell, and SQL), and the course will cover which problems these languages are specifically suited to solving. Learning the principles behind programming equips you with a skill set that enables you to effectively approach various new problems.Central University of Rajasthan
The Department of Data Sciences and Analytics aims to train students in the rapidly growing field of Data Science, to be a vibrant research hub in this area, and interact with other disciplines to evaluate, analyze and utilize the knowledge.Ramakrishna Mission Vivekananda University-Howrah
Ramakrishna Mission Vivekananda Educational and Research Institute along with Tata Consultancy Services has first launched a Master’s level degree course in chúng tôi Big Data Analytics in India. It was designed by TCS. The postgraduate program spans four semesters. The content of the course includes Mathematics, Statistics, Economics, Computer Science, and applications in Big Data Analytics.MIA digital university
By considering the rapid rise of innovative tech as drivers of the global economy, the Master in Computer Science: Cybersecurity, Artificial Intelligence, and Data Science offers students a broad foundation in modern software and hardware systems and an in-depth knowledge of the new applications of IT. The master program covers the latest technologies from Artificial Intelligence to Data Science, Internet of Things, and machine learning and their applications to the cloud and mobile apps and systems.St. Joseph College- Bangalore
The Department of Big Data Analytics established in the year 2023-17 is committed to training students to analyze big data and to arrive at valuable insights in the domain. The course was designed to equip students with a Mathematical foundation, statistical and machine learning theories for analytics. Graduates with a mathematics or statistics background with 40% in the cognate subject and 50% marks are eligible.MIT World Peace University
Master of Science in Data Science and Big Data Analytics is a two-year full-time program. This is a niche program that focuses on Data Science and Big data tools and technologies. The course builds on the strength and innovation power of Science & Engineering. The program ensures to provide students with the flexibility to focus on Big Data Tools & technologies and Data Sciences.St. Aloysius College
M.Sc. Big Data Analytics has been developed to create Postgraduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. The course is aimed to help the learner to develop the skills and expertise needed to face the industry world. The program splits into three areas: statistics, computing, and management. The eligible percentage is 50%.International University of Applied Science
High-end fields like artificial intelligence , networking, embedded systems , knowledge-based systems, and bio-informatics require very qualified professionals. In this context, there is a tremendous opportunity for specialists (not just programmers). If you have the right academic credentials and are interested in establishing a technically focused career in IT, you should consider doing a high-quality/high-end Master’s program. With the growing popularity of computer science , here are the top 10 chúng tôi computer science courses for you to take up in 2023.Master of Science (M.Sc) in Computer Science is a full-time two-year postgraduate course offered by the Osmania University, Hyderabad. Candidates holding a Bachelor’s degree of 3 years duration (B.Sc. or equivalent), under 10+2+3 pattern from any recognized university, are chúng tôi chúng tôi in Big Data Analytics in India program offered by St. Xavier’s College can help in the overall development of the student by enhancing the quality and consistency of talent. The course has gained great admiration from academia, students, and various stakeholders across India. The courses consist of Statistical Methods, Probability & Stochastic Process, Linear Algebra & Linear Programming, Computing for Data Sciences, and Data chúng tôi online MSc course is focused on teaching the principles of programming in general. For example, in the first unit, you will learn the basics of programming using the C language and object-oriented programming using Java. You will subsequently be introduced to other languages (such as Python, Haskell, and SQL), and the course will cover which problems these languages are specifically suited to solving. Learning the principles behind programming equips you with a skill set that enables you to effectively approach various new chúng tôi Department of Data Sciences and Analytics aims to train students in the rapidly growing field of Data Science, to be a vibrant research hub in this area, and interact with other disciplines to evaluate, analyze and utilize the knowledge.Ramakrishna Mission Vivekananda Educational and Research Institute along with Tata Consultancy Services has first launched a Master’s level degree course in chúng tôi Big Data Analytics in India. It was designed by TCS. The postgraduate program spans four semesters. The content of the course includes Mathematics, Statistics, Economics, Computer Science, and applications in Big Data chúng tôi considering the rapid rise of innovative tech as drivers of the global economy, the Master in Computer Science: Cybersecurity, Artificial Intelligence, and Data Science offers students a broad foundation in modern software and hardware systems and an in-depth knowledge of the new applications of IT. The master program covers the latest technologies from Artificial Intelligence to Data Science, Internet of Things, and machine learning and their applications to the cloud and mobile apps and chúng tôi Department of Big Data Analytics established in the year 2023-17 is committed to training students to analyze big data and to arrive at valuable insights in the domain. The course was designed to equip students with a Mathematical foundation, statistical and machine learning theories for analytics. Graduates with a mathematics or statistics background with 40% in the cognate subject and 50% marks are eligible.Master of Science in Data Science and Big Data Analytics is a two-year full-time program. This is a niche program that focuses on Data Science and Big data tools and technologies. The course builds on the strength and innovation power of Science & Engineering. The program ensures to provide students with the flexibility to focus on Big Data Tools & technologies and Data chúng tôi Big Data Analytics has been developed to create Postgraduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. The course is aimed to help the learner to develop the skills and expertise needed to face the industry world. The program splits into three areas: statistics, computing, and management. The eligible percentage is 50%.In IU’s Master in Computer Science, you continue your journey with a focus on data science, cybersecurity, and artificial intelligence. Due to the unmet demand for experts on these topics, you’ll become a sought-after specialist who can operate in almost any sector. The Master’s degree in Computer Science will give you all the skills you need to get started in the international job market.
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Analytics Insight features top data science training courses for beginners in 2023
Data science is thriving in the global tech-driven market owing to its unprecedented potential to help organizations make smarter decisions to yield higher revenue efficiently. Aspiring data scientists are aiming to join popular and reputed global companies to have a successful professional career in data management. But to add value to CVs in this competitive world, these aspiring data scientists should have a strong understanding of concepts and mechanisms of data science. It must be overwhelming to select any one data science course that includes data management and data visualization. Thus, let’s explore some of the top data science training courses for beginners to learn in 2023.Top data science training courses for aspiring data scientists Applied Data Science with Python from Michigan University at Coursera
Applied Data Science with Python from Michigan University at Coursera is one of the top data science training courses for aspiring data scientists. They can learn to apply data science methods and techniques by enrolling for free from today. Beginners can conduct an inferential statistical analysis, data visualization, data analysis, and many more. There are five courses for aspiring data scientists to learn data science through Python. There are flexible schedules with approximately five months to complete and earn a valuable certificate. This course consists of hands-on projects for a strong practical understanding of the subject.Introduction to Data Science using Python at Udemy
Introduction to Data Science using Python at Udemy offers aspiring data scientists to understand the basics of data science and analytics, Python and Scikit learn with online video content, a valuable certificate, and an instructor direct message facility. Udemy is well-known for offering highly-related data science training courses for learning data visualization and effective data management.Analyze Data with Python at Codeacademy
Analyze Data with Python at Codeacademy offers the fundamentals of data analysis while building Python skills efficiently and effectively in the data science training course. Aspiring data scientists can learn about Python, NumPy, SciPy, and many more to gain Python skills, data management, data visualization, etc. to earn a valuable certificate after completion. There are multiple practical projects to gain a strong understanding of data science such as FetchMaker, A/B Testing, and so on. There are eight courses for aspiring data scientists to get specialized skills and step-by-step guidance to gain sufficient knowledge in a few months.Data Science Specialization from Johns Hopkins University at Coursera
Data Science Specialization from Johns Hopkins University at Coursera offers a ten-course introduction to data science from eminent teachers. Aspiring data scientists can learn to apply data science methods and techniques by enrolling for free from today. They can also gain knowledge of using R for data management and data visualization, navigating the data science pipeline for data acquisition, and many more. This data science training course provides a flexible schedule for approximately 11 months with seven hours per week. It offers hands-on projects for aspiring data scientists to complete for earning a valuable certificate to add value to the CV.Programming for Data Science with Python at Udacity
Programming for Data Science with Python at Udacity is a well-known data science training course for beginners. It helps to prepare a data science career with programming tools such as Python, SQL, and git. The estimated time to complete this data science course is three months at ten hours per week. Aspiring data scientists should enroll by November 3, 2023, to solve problems with effective data management and data visualization. There are real-world projects from industry experts with technical mentor support and a flexible learning program.Data Science for Everyone at DataCamp
Data Science for Everyone at DataCamp is one of the top data science training courses for beginners. It provides an introduction to data science without any involvement in coding. It includes 48 exercises with 15 videos for aspiring data scientists. They can learn about different data scientist roles, foundational topics, and many more. The course curriculum includes the introduction to data science, data collection and storage, data visualization, data preparation, and finally the experimentation and prediction.
Data analytics jobs have been well paid and in high demand for some time.
The “IT Skills and Certifications Pay Index” by Foote Partners shows that such skills often merit a pay premium, and the average salary of these specialists has been steadily rising. Among the high-paying areas currently are risk analytics, big data analytics, data science, prescriptive analytics, predictive analytics, modeling, Apache Hadoop, and business analytics.
But data analytics is a broad term. It encompasses business intelligence (BI) and visualization as well as the application of analytics to other functions, such as IT and cybersecurity.
Here are some of the five top trends in data analytics jobs:
See more: The Data Analytics Job Market
Experience or certification in a specific programming language or analytics discipline used to be a passport to good jobs. It will still gain people some positions, but they need more if they hope to move up the pay scale.
“For analytics professionals, listing proficiency in SAS, Python, or R may get someone past the initial HR screening, but that’s about it,” said Sean O’Brien, SVP of education at SAS.
Data analytics candidates need experience, certification, and other human skills to succeed in today’s market.
It used to be enough to crunch some numbers and then tell the business an outcome or prediction using regular language.
These days, executives demand more. A top trend for data analytics jobs is the increasing importance of communication skills and storytelling. The rise of chief data officers and chief analytics officers is the clearest indication that analytics has moved from the backroom to the boardroom, and more often, it’s data experts that are setting strategy.
“The ability to make analytics outputs relatable to stakeholders across the business will set them apart,” said O’Brien with SAS.
“It’s not enough to be able to clean, integrate, and analyze huge amounts of data. Analytics pros have to understand how data and analytics directly support business goals and be able to communicate the story the data is telling. They need to be able to not just present trends and reports but communicate their meaning.”
Cybersecurity trends apply to data analytics in two ways: Analysts need to be aware of and possess some security skills if they are to keep their platforms and models secure. But perhaps even more importantly, analytics jobs are becoming available in greater frequency in security. Analysts are needed who can unlock the vast troves of data available in system logs, alerts, and organizational data to find the potential incursions and isolate threats.
“Flexibly and securely viewing trusted data in context through shared applications across an industry ecosystem also enables process and governance improvement,” said Jeffrey Hojlo, an analyst at IDC.
Storage, too, has transitioned into the analytics arena. Storage administrators are spending less time managing storage devices and more time managing data. This entails being more strategic about data mobility, data management, data services, and delivering the foundation for generating value from unstructured data.
“Storage administrators must leverage analytics about files, such as types of files, access times, owners, and other attributes,” said Randy Hopkins, VP of global systems engineering and enablement at Komprise.
See more: Top Data Analytics Certifications
Risk is a hot area across the business world. And it is up to risk management and risk analysts to identify, analyze, and accept or mitigate any uncertainty that may exist in business or investment decisions.
A variety of tactics are used to determine risk. For example, a common tool is known as standard deviation, which is a statistical measure where data is plotted around a central tendency. Management can then see how much risk might be involved and how to minimize that risk.
Those skilled in modern risk analytics are now in greater demand, as the risk management field transitions from manual or traditional methods. Accordingly, risk analytics and risk assessment jobs rose by 5.3% in value over a six-month period, according to surveys by Foote Partners. This form of business intelligence exploits structured and unstructured data as a way to model scenarios and outcomes and provide insight into potential fraud, market risk, credit risk, financial risk, supply chain risk, and other areas of risk.
As a sign that there was definite substance to the hype around big data, Foote Partners notes that big data analytics jobs continue to be in demand. They have risen in value by 13.3% over a six-month period.
See more: 10 Top Companies Hiring for Data Analytics Jobs
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 =  * 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 GoogleConclusion
These paying data science jobs in 2023
There is a rising interest in data science experts all over the planet. These open positions would keep on flooding past 2023, adding more than 1.5 lakh new positions. This pattern is a characteristic reaction to information being a significant asset for associations in the digital age. A survey recorded the main 10 most lucrative data science occupations in India. Here is the list of the top 10 highest paying data science jobs in 2023:What Does Data Science Involve?
Data science includes gathering, manipulating, storing, and analysing data. It works with data-driven approaches for decision-making, thus fostering an environment of continuous growth. Amazon’s online shopping site fills in as a prime example of how data collection can further develop execution. Amazon tweaks the landing page perspectives on clients depending on what they search, buy, and spend. As such, it recalls datasets and gives valuable item proposals to fit client needs.Infrastructure Architect
Role: An infrastructure architect oversees the existing business systems to ensure that they support the new technological requirements. Nowadays, organizations also hire cloud infrastructure architects to supervise their cloud strategies. Preferred qualifications: A degree in computer engineering or software development with adequate training in database administration, information system development, and system maintenance. Infrastructure architect has become one of the highest salary data science jobs in India due to its demand. Salary:25, 00,000 INREnterprise Architect
Job: As an enterprise architect, the duties incorporate adjusting the organization’s procedure to innovative solutions. You assist organizations with accomplishing their destinations by recognizing needs and afterward planning architecture design to meet explicit requirements. Preferred qualifications: A bachelor-level education combined with a master’s degree and field instruction in enterprise architecture can assist you with entering the labor force as an enterprise architect. The exorbitant and developing demand makes enterprise architects land on one of the highest salary data science occupations in India. Salary:24,81,452 INRApplications Architect
Role: These practitioners track applications, supervising how they are operating within the company and how users are interacting with them. As the job title suggests, their job is to build the architecture of applications, replete with components like the user interface and app infrastructure. In addition to being one of the highest-paid data science jobs in India, this is also a fast-paced one. Preferred qualifications: To qualify for an opening for applications architect, you would generally need a computer science degree, along with industry certifications in programming and architectural design. The excessive & growing demand makes application architects land one of the highest salary data science jobs in India. Salary:24,00,000 INRData Architect
Role: One of the highest-paid data science occupations around the world, a data architect makes new data set frameworks, use performance, and plan examination to further develop the interconnected information biological system inside the organization. The ultimate objective is to make the data effectively available for use by information researchers. It has forever been probably the best datum science occupation in India, and managing cash – yours and others – is the stuff of dreams. Preferred qualifications: To turn into an information modeler, you would require a computer engineering education with adequate control over applied mathematical and statistical ideas. Ideally, you ought to have finished coursework in subjects like data management, programming, big data development, system analytics, technology architecture. Salary:20,06,452 INRData Scientist
Role: It is a more technical position than a data analyst. Data scientists might perform data preparation tasks (cleaning, putting together, etc) that permit companies to make key moves. They handle large datasets and uncover valuable trends and patterns in the data. Preferred qualifications: A master’s degree or progressed capabilities, for example, PhD is alluring for the assignment of a data scientist. Some of the time, organizations look for area subject matter experts (medical care, retail, data innovation, IT, and so on) to fill high-responsibility positions. Active experience is basic for data scientist jobs, aside from having a sound foundation in IT, CS, math, and other such disciplines. Salary: 9,84,488 INRMachine Learning Engineer
Role: As an ML engineer, you are liable for making data funnels and conveying programming solutions. Moreover, your occupation would include running tests and trials to screen the framework’s usefulness and execution. Preferred qualifications: Machine learning engineers are relied upon to have solid factual and programming abilities. Computer programmers with adequate ML experience are liked for such jobs. You can brush hypothetical points with online courses and gain viable experience by executing projects. Numerous online certifications with integrated tutoring are additionally accessible on the lookout. Salary:8,41,476Business Intelligence Analyst
Role: BI analysts form key designs for organizations while guaranteeing that the necessary data can be used easily. They likewise work with end-user entertainment of the BI tools and applications created by them. Preferred qualifications: The work of BI analysts requires a blend of specialized aptitude with the expertise of business and the board ideas of management. Many candidates hold an MBA with a specialization in analytics. Having business research and project coordination experience can give you an upper hand. Salary: 7,28,541 INRData Analyst
Role: Data analysts change and control huge data sets. They likewise help more significant level chiefs in gathering bits of knowledge from their analytics. Analysts ought to have sufficient knowledge of A/B testing and tracking web analytics. It has forever been perhaps the best datum science occupation in India, and managing cash – yours and others – is the stuff of dreams. Preferred Qualifications: Entry-level openings in this space require at least a four-year certification (with accentuation on science/math/measurements). You ought to show fitness in science and sensible capacity. Normally, those capable in programming—with abilities in SQL, Python, Oracle, and so forth—are given inclination by employing administrators. Salary:7,12,965 INRMachine Learning Scientist
Role: As an ML scientist, you are entrusted with exploring new methodologies, like calculations, administration, and solo learning strategies. Associations enlist these experts in places with work titles like research scientist or research engineer. Preferred qualifications: Job postings for this job list the ideal profile as “somebody with a science certificate with fitting postgraduate studies and extensive proven research experience.” Salary:6,71,958 INRStatisticians
Role: Statisticians are recruited to gather, examine and decipher the information, in this way helping the leaders with their work. Their everyday responsibilities likewise incorporate imparting discoveries (data relationships and patterns) to partners and adding to setting functional techniques. As well as being one of the most lucrative data science occupations in India, it is additionally a high-speed one. Preferred qualifications: Entry-level openings might accept competitors with a four-year certification. However, most statisticians hold no less than a postgraduate diploma in math, computer science, economics, or other quantitative fields.
HQL or Hive Query Language is a simple yet powerful SQL like querying language which provides the users with the ability to perform data analytics on big datasets. Owing to its syntax similarity to SQL, HQL has been widely adopted among data engineers and can be learned quickly by people new to the world of big data and Hive.
In this article, we will be performing several HQL commands on a “big data” dataset containing information on customers, their purchases, InvoiceID, their country of origin and many more. These parameters will help us better understand our customers and make more efficient and sound business decisions.
For the purpose of execution, we would be using Beeline command Line Interface which executes queries through HiveServer2.
Next, we type in the following command which connects us to the HiveServer2.
It requires authentication so we input the username and password for this session and provide the location or path where we have our database stored. The commands(underlined in red) for this are given below.set chúng tôi = /user/username/warehouse;
Now that we are connected to HiveServer2 we are ready to start querying our database. Firstly we create our database “demo01” and then type in the command to use it.Use demo01;
Now we are going to list all the tables present in the demo01 database using the following commandshow tables;
As we can see above 2 tables “emp” and “t1” are already present in the demo01 database. So for our customer’s dataset, we are going to create a new table called “customers”.CREATE TABLE IF NOT EXISTS customers (InvoiceNo VARCHAR(255),Stock_Code VARCHAR(255),Description VARCHAR(255),Quantity INT,UnitPrice DECIMAL(6,2),CustomerID INT,Country VARCHAR(255)) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' ;
Now if we run the “show tables” command we see the following output.
We can see that a table named customers has been created in the demo01 database. We can also see the schema of the table using the following command.desc customers;
Now we upload our chúng tôi file containing customer records to our hdfs storage using this command.hdfs dfs -put chúng tôi /user/itv001775/warehouse/demo01.db/customers
Now we have to load this data into the customer’s table we created above. To do this we run the following command.load data inpath '/user/itv001775/warehouse/demo01.db/customers/new.csv' into table customers;
This concludes the part where we have uploaded the data on hdfs and loaded it into the customer’s table we created in the demo01 database.
Now we shall do a bit of data eyeballing meaning to have a look at the data and see what insights can be extracted from it. As the dataset contains over 580,000 records we shall have a look at the first 5 records for convenience using this command.select * from customers limit 5;
We can see above it has 7 columns namely invoiceno, stock_code, description, quantity, unitprice, customerid and country. Each column brings value and insights for the data analysis we are going to perform next.DATA ANALYSIS THROUGH HQL
stock code 3427AB, quantity 2 at a unit price of 9000.
QUERY:insert into customers values (‘610221’,’3427AB’,’Gaming PC’,2,9000,18443,’Germany’);
Now we can query the database to see if the record was inserted successfully.
select * from customers limit 5;
As we can see record has been inserted.
CASE 2: We want to see the sum of the purchases made by each customer along with invoiceno in descending order.
QUERY: (for convenience we limit our output to the first 10 records)select customerid, sum(unitprice) as total_purchase from customers group by customerid order by total_purchase desc limit 10;
In the above query, we are grouping our records together based on the same customers id’s and ordering the results by total purchase made by each customer.
Apart from the customers without a customerid, we are able to find out our top 10 customers according to the amount of their purchase. This can be really helpful in scouting and targeting potential customers who would be profitable for businesses.
CASE 3: We want to find out the average price of bags being sold to our customers.
QUERY:select avg(unitprice) as average_bagPrice from customers where description like '%BAG%';
Note that in the above query we used the “like” logical operator to find the text from the description field. The “%” sign represents that anything can come before and after the word “bag” in the text.
We can observe that the average price across the spectrum of products sold under the tag of bags is 2.35(dollars, euros or any other currency). The same can be done for other articles which can help companies to determine the price ranges for their products for better sales output.
price of products for top 10 countries in descending order by count.
QUERY:select count(*) as number_of_products, sum(unitprice) as totalsum_of_price, country from customers group by country order by totalsum_of_price desc limit 10;
Here count(*) means counting all the records separately for each country and ordering the output by total sum of price of goods sold in that country.
Through this query, we can infer the countries the businesses should target the most as the total revenue generated from these countries is maximum.
quantity for top 20 customers.
For each customer, we are grouping their records by their id and finding the number of products they bought in descending order of that statistic. We also employ the condition that only those records are selected where a number of products are greater than 10.
Note that we always use the “having” clause with the group by when we want to specify a condition.
Through this, we can see our top customers based on the number of products they ordered. The customers ordering the most generate the most amount of profit for the company and thus should be scouted and pursued the most, and this analysis helps us find them efficiently.BONUS
Hive has an amazing feature of sorting our data through the “Sort by” clause. It almost does the same function as the “order by” clause as in they both arrange the data in ascending or descending order. But the main difference can be seen in the working of both these commands.
We know that in Hive, the queries we write in HQL are converted into MapReduce jobs so as to abstract the complexity and make it comfortable for the user.
So when we run a query like :Select customerid, unitprice from customers sort by customerid;
Multiple mappers and reducers are deployed for the MapReduce job. Give attention to the fact that multiple reducers are used which is the key difference here.
Multiple mappers output their data into their respective reducers where it is sorted according to the column provided in the query, customerid in this case. The final output contains the appended data from all the reducers, resulting in partial sorting of data.
Whereas in order by multiple mappers are used along with only 1 reducer. Usage of a single reducer result in complete sorting of the data passed on from the mappers.Select customerid, unitprice from customers order by customerid;
The difference in the Reducer output can be clearly seen in the data. Hence we can say that “order by” guarantees complete order in the data whereas “sort by” delivers partially ordered results.ENDNOTES
In this article, we have learned to run HQL queries and draw insights from our customer dataset for data analytics. These insights are valuable business intelligence and are very useful in driving business decisions.
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