Q: What Is Root Cause Analysis How To Identify A Cause Vs A Correlation Give Examples
Root cause analysis: a method of problem-solving used for identifying the root cause of a problem
Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
Example: a higher crime rate is associated with higher sales in ice cream in Canada, aka they are positively correlated. However, this doesnt mean that one causes another. Instead, its because both occur more when its warmer outside.
You can test for causation using hypothesis testing or A/B testing.
Q33 What Steps Are Involved In Making A Decision Tree
What Is The F1 Score And How To Calculate It
F1 score helps us calculate the harmonic mean of precision and recall that gives us the tests accuracy. If F1 = 1, then precision and recall are accurate. If F1 < 1 or equal to 0, then precision or recall is less accurate, or they are completely inaccurate. See below for the formula to calculate the F1 score:
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Additional Personal Data Scientist Interview Questions
|Please tell me about yourself.|
|What are your best qualities professionally? What are your areas of weakness?|
|Is there one Data Scientist you admire most?|
|What inspired your interest in data science?|
|What unique skills or characteristics do you bring that would help the team?|
|What made you decide to leave your last job?|
|What level of compensation are you expecting from this job?|
|Do you prefer to work alone or as a part of a team of Data Scientists?|
|Where do you see your career in five years?|
|Whats your approach for handling stress on the job?|
|How do you find motivation?|
|Whats your method for measuring success?|
|How would you describe your ideal work environment?|
|What are your passions or hobbies outside of data science?|
Ready To Take The Next Step Towards A Career In Data Science
Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses, build up step-by-step experience with SQL, Python, R, and Tableau, and upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. If you still arent sure you want to turn your interest in data science into a solid career, we also offer a free preview version of the Data Science Program. Youll receive 12 hours of beginner to advanced content for free. Its a great way to see if the program is right for you.
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In Your Choice Of Language Write A Program That Prints The Numbers Ranging From One To 50
But for multiples of three, print “Fizz” instead of the number, and for the multiples of five, print “Buzz.” For numbers which are multiples of both three and five, print “FizzBuzz”
The code is shown below:
Note that the range mentioned is 51, which means zero to 50. However, the range asked in the question is one to 50. Therefore, in the above code, you can include the range as .
The output of the above code is as shown:
What Do You Understand By Sensitivity How Is It Calculated
Sensitivity is commonly used to validate the accuracy of a classifier Sensitivity is nothing but “Predicted TRUE events/Total events”.True events here are the events that were true and model also predicted them as true. Calculation of sensitivity can be done as follows:
Sensitivity = True Positives/Positives in Actual Dependent Variable Where True positives are positive events that are correctly classified.
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How Do You Avoid The Overfitting Of Your Model
Overfitting basically refers to a model that is set only for a small amount of data. It tends to ignore the bigger picture. Three important methods to avoid overfitting are:
- Keeping the model simpleusing fewer variables and removing major amount of the noise in the training data
- Using cross-validation techniques. E.g.: k folds cross-validation
- Using regularisation techniques like LASSO, to penalise model parameters that are more likely to cause overfitting.
Data Science Probability Interview Questions
40. What do you understand by Hypothesis in the content of Machine Learning?
In machine learning, a hypothesis represents a mathematical function that an algorithm uses to represent the relationship between the target variable and features.
41. How will you tackle an exploding gradient problem?
42. Is Naïve Bayes bad? If yes, under what aspects.
Naïve Bayes is a machine learning algorithm based on the Bayes Theorem. This is used for solving classification problems. It is based on two assumptions, first, each feature/attribute present in the dataset is independent of another, and second, each feature carries equal importance. But this assumption of Naïve Bayes turns out to be disadvantageous. As it assumes that the features are independent of each other, but in real-life scenarios, this assumption cannot be true as there is always some dependence present in the given set of features. Another disadvantage of this algorithm is the zero-frequency problem where the model assigns value zero for those features in the test dataset that were not present in the training dataset.
43. How would you develop a model to identify plagiarism?
Follow the steps below for developing a model that identifies plagiarism:
Tokenise the document.
Use the NLTK library in Python for the removal of stopwords from data.
Create LDA or SDA of the document and then use the GenSim library to identify the most relevant words, line by line.
Use Google Search API to search for those words.
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What Is The Benefit Of Using The R Language For The Visualization Of Data
R programming language offers a huge number of built-in functions and libraries that can help for visualizing the data like ggplot2, leaflet, lattice, etc. Using R language we can develop any kind of graph and helps in exploratory data analysis. R language supports more graphical requirements than any other language.
Q: To Further Test The Hospital Triage System Administrators Selected 200 Nights And Randomly Assigned A New Triage System To Be Used On 100 Nights And A Standard System On The Remaining 100 Nights They Calculated The Nightly Median Waiting Time To See A Physician The Average Mwt For The New System Was 4 Hours With A Standard Deviation Of 05 Hours While The Average Mwt For The Old System Was 6 Hours With A Standard Deviation Of 2 Hours Consider The Hypothesis Of A Decrease In The Mean Mwt Associated With The New Treatment What Does The 95% Independent Group Confidence Interval With Unequal Variances Suggest Vis A Vis This Hypothesis
Assuming we subtract in this order :
confidence interval formula for two independent samples
mean = new mean old mean = 46 = -2
z-score = 1.96 confidence interval of 95%
st. error = sqrt/) * sqrtstandard error = 0.205061lower bound = -21.96*0.205061 = -2.40192upper bound = -2+1.96*0.205061 = -1.59808
confidence interval =
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Write Code To Calculate The Root Mean Square Error Given The Lists Of Values As Actual And Predicted
To calculate the root mean square error , we have to:
The code in Python for calculating RMSE is given below:
def rmse: errors = - predicted) for i in range)] squared_errors = mean = sum / len return mean ** .5
Check out this Machine Learning Course to get an in-depth understanding of Machine Learning.
Additional Situational Data Scientist Interview Questions
|Can you think of a professional situation where you had the opportunity to demonstrate leadership?|
|What is your approach to conflict resolution?|
|What is your approach for building professional relationships with colleagues?|
|Whats an example of a successful presentation you gave? Why was it so compelling?|
|If you are talking to a colleague or client from a non-technical background, how do you explain complex technical problems or challenges?|
|Please recall a situation when you had to handle sensitive information. How did you approach the situation?|
|From your own perspective, how would you rate your communication skills?|
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Q100 What Are The Different Layers On Cnn
There are four layers in CNN:
Convolutional Layer the layer that performs a convolutional operation, creating several smaller picture windows to go over the data.
ReLU Layer it brings non-linearity to the network and converts all the negative pixels to zero. The output is a rectified feature map.
Pooling Layer pooling is a down-sampling operation that reduces the dimensionality of the feature map.
Fully Connected Layer this layer recognizes and classifies the objects in the image.
What Is A Linear Regression Model List Its Drawbacks
A linear regression model is a model in which there is a linear relationship between the dependent and independent variables.
Here are the drawbacks of linear regression:
- Only the mean of the dependent variable is taken into consideration.
- It assumes that the data is independent.
- The method is sensitive to outlier data values.
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What Is Star Schema
It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes, star schemas involve several layers of summarization to recover information faster.
Top 20 Data Science Interview Questions And Answers
As the world becomes more data-driven, the demand for Data Science professionals continues to increase. Data Science roles exist in large numbers across a wide range of organizations including corporations, non-profit organizations, and government agencies.
If you have decided to kickstart your career in this happening field, you are certainly treading the right path. Here are top 20 Data Science interview questions and answers to help you prepare for your next interview.
1. What do you understand by the term Data Science? What is the significance of Data Science in the modern world?
Ans. Data Science is an interdisciplinary domain that makes use of algorithms, machine learning techniques, and scientific processes, to detect patterns in data and to uncover insights using statistical and mathematical analysis.
The following are the basic steps involved in Data Science:
- Collating business requirements
- Collecting relevant data
- Performing the following processes in sequential order – data cleaning, data warehousing, data staging, and data architecture
- Subjecting the cleansed data to various algorithms based on business requirements
- Visualizing the insights in order to communicate to the business
2. What does a low or high p-value mean?
Ans. A p-value refers to the measure of the probability of having results equal to/more than the results you achieve under a certain hypothesis, where a null hypothesis is correct.
There are three instances here:
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Explain The Wide Data Format And Long Data Format
A wide data format is a type of writing data where each row is unique and has many columns for different attributes. In wide data, format supposes we have an entity and it has many attributes, each and every attribute will be written in different columns for a single row . In wide data format, there will be a large number of columns for each row. here we can group categorical data
A long data format is writing data that has only a limited number of columns for each row. In this model, the row is not unique which will be repeated for different attributes for that entity.
Common Data Science Interview Questions & Answers
Hone yourself to be the ideal candidate at your next data scientist job interview with these frequently asked data science interview questions. Data Scientist interview questions asked at a job interview can fall into one of the following categories –
Data Science Technical Interview Questions based on data science programming languages like Python, R, etc.
Data Science Technical Interview Questions based on statistics, probability, math, machine learning, etc.
Practical experience or Role-based data scientist interview questions based on the projects you have worked on and how they turned out.
Apart from interview questions, we have also put together a collection of 100+ ready-to-use Data Science solved code examples. Each code example solves a specific use case for your project. These can be of great help in answering interview questions and also a handy guide when working on data science projects.
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Q27 What Is Machine Learning
Machine Learning is the part of Data Science which enables the system to process datasets autonomously without any human interference by utilizing various algorithms to work on a massive volume of data generated and extracted from numerous sources. A social media platform i.e. Facebook is a decent example of machine learning implementation where fast and furious algorithms are used to gather the behavioral information of every user on social media and recommend them appropriate articles, multimedia files and much more according to their choice.
What Is The Difference Between A Box Plot And A Histogram
The frequency of a certain features values is denoted visually by both box plots
Boxplots are more often used in comparing several datasets and compared to histograms, take less space and contain fewer details. Histograms are used to know and understand the probability distribution underlying a dataset.
The diagram above denotes a boxplot of a dataset.
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R Data Science Interview Questions
47. What are the key differences between R and Python?
Some of the common differences between R and Python are:
- R is mostly used for statistical analysis, whereas Python offers a more comprehensive data science approach.
- R’s major goal is data analysis and statistics, whereas Python’s primary goal is deployment and production.
- Scholars and R& D professionals are the majority of R users, whereas Programmers and Developers constitute the majority of Python users.
- R allows you to leverage pre-existing libraries, but Python allows you to create new models from scratch.
- R is tough to understand at first, but Python is linear and easy to pick up.
- Locally, R is integrated with Run, but Python is well-integrated with applications.
- R and Python are both capable of handling large databases.
- R is supported by the R Studio IDE, while Python is supported by Spyder.
48. What are the different data types in R?
R has 6 basic data types.
49. Why use R?
R is a programming language, not only a statistics package and it is built to work in the same manner that people think about problems. R is a versatile and powerful programming language.
50. Why would you use factor variables?
Factor variables can be utilized in statistical modeling where they will be correctly implemented, i.e., the correct amount of degrees of freedom will be assigned to them. Factor variables are also useful in a variety of graphic kinds.
51. How do you concatenate strings in R?
- Bubble Sort
- Quick Sort
How Do I Prepare For A Data Science Interview
As you would for any other technical interview make sure that youve got the basics down, and can execute ideas in code. Of course, you should also present a good resume and be prepared to summarize past experiences.
On a more general note, you should also research the company and the specific role youre applying for. You want to ask questions about the software and the company itself, as it serves to highlight your enthusiasm for the role. It may also be worth looking at reviews on Glassdoor to get a sense of the company and past employees experiences.
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What Is A Normal Distribution
Data distribution is a visualization tool to analyze how data is spread out or distributed. Data can be distributed in various ways. For instance, it could be with a bias to the left or the right, or it could all be jumbled up.
Data may also be distributed around a central value, i.e., mean, median, etc. This kind of distribution has no bias either to the left or to the right and is in the form of a bell-shaped curve. This distribution also has its mean equal to the median. This kind of distribution is called a normal distribution.
What Is Meant By Sampling Explain Some Sampling Methods That You Know
Data sampling is a statistical analysis method in which a particular portion of data is taken to analyze, identify the hidden trends and patterns in data. With the help of the sampling method, a larger set of data being examined. It helps the data scientists to work with a limited portion of data to produce accurate results rather than working on entire data sets.
Types of sampling methods are:
- Simple random sampling method
- Systematic sampling
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