Data Science Career Guide Interview Preparation


Explain The Steps In Making A Decision Tree

Data Science Interview Questions | Data Science Interview Questions Answers And Tips | Simplilearn
  • Take the entire data set as input
  • Calculate entropy of the target variable, as well as the predictor attributes
  • Calculate your information gain of all attributes
  • Choose the attribute with the highest information gain as the root node
  • Repeat the same procedure on every branch until the decision node of each branch is finalized
  • For example, let’s say you want to build a to decide whether you should accept or decline a job offer. The decision tree for this case is as shown:

    It is clear from the decision tree that an offer is accepted if:

    • Salary is greater than $50,000
    • The commute is less than an hour
    • Incentives are offered

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    Q100 What Are The Different Layers On Cnn

    There are four layers inCNN:

  • 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.

  • Be Prepared To Discuss Salary

    If you find salary discussions awkward or discomforting, youâll want to practice your responses, or at the very least have a firm idea of what your expectations are. Itâs common for salary expectations to come up in an interview, and you should be ready for this to come up at any time sometimes they will come up in the first interview, and other times it wonât come up until the final interview.

    It is best to use a salary range as opposed to a single number, and you should have a salary in mind going into it. This shouldnât just be an arbitrary amount that you expect, but a value that you can justify based on the requirements and responsibilities of the role, and the expertise and experience you bring to it. This means that your salary range will likely â and should â change depending on the role youâre interviewing for.

    There are a number of services that are helpful in identifying a reasonable salary range for different jobs in various industries.

    In some cases, you wonât have enough information or wonât feel comfortable listing a salary range. If you donât want to, itâs okay to tell them you donât feel confident listing a salary. This is especially true if you donât have a lot of information about the requirements of the role, such as the weekly hours, vacation time, benefits, and more. The base salary doesnât always tell the whole story, so make sure to ask questions when appropriate.

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    The Ultimate Insider Interview Guide

  • Complete

    Technical questions, how-to-answer guides, best practices, example replies, and more are covered in this comprehensive guide.1

  • Focused

    Helping people launch and advance a career in data science has been our passion and focus for over 5 years.2

  • Expert Advice

    With 38 dedicated courses in data science and over 2M students trained, we know exactly what it takes to land the data science interview.3

  • Do The Pros / Benefits Of Dscg

    Top 100 Frequently Asked Data Science Interview Questions and Answers ...

    Ideally, if youve gone through the evaluation steps above, you have a list of positive things about the Data Science Career Guide Interview Preparation training that looks something like this:

    • The purpose of DSCG-IP can be clearly grasped and understood, and its lesson structure is clear, specific, and well organized
    • Jose Portilla is well qualified to teach this subject matter, has good teaching abilities, and is responsive to student questions
    • Other DSCG-IP students have great things to say about the program

    Other benefits include:

    • You get to go through DSCG-IP at your own pace
    • You join a community of 4,080 other students taking the course
    • You get lifetime access to the training
    • All updates to the training are free
    • You have a 30 day money back guarantee

    Even if there are some things that you dont like about the program, so what?

    The question is simply this: do you think that DSCG-IP would be worth your time, even if there are some things that could be better about it?

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    Common Questions Youll Be Asked In A Data Science Job Interview

    Although you canât predict all the questions youâll be asked in an interview, you should still try to think about what will likely be asked of you. Going over practice questions and technical refreshers can be extremely helpful when preparing for your interview.

    Below, we list some of the best resources for finding questions you are likely to be asked:

    Q109 What Is Back Propagation And Explain Its Working

    Backpropagation is a training algorithm used for multilayer neural network. In this method, we move the error from an end of the network to all weights inside the network and thus allowing efficient computation of the gradient.

    It has the following steps:

    • Forward Propagation of Training Data

    • Derivatives are computed using output and target

    • Back Propagate for computing derivative of error wrt output activation

    • Using previously calculated derivatives for output

    • Update the Weights

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    Regression Mle Em And Map

    • Regression: Investigate the relationship between two variables
    • Assumption of Linear Regression and common interview questions such as what if one of the assumptions doesnât work?
    • Least Square Estimator vs. Maximum Likelihood Estimator: Under what conditions they are the same?
    • Ridge Regression vs. Lasso Regression: Which regression can make a certain coefficient to exactly zero and how?
    • Likelihood function: Measure how well observed data fits the assumed distribution
    • Maximum Likelihood Estimation: A car speed on the highway follows a normal distribution: N, After observing the n car speed, what is the MLE for μ?
    • Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
    • Maximum a posteriori and how itâs different from Maximum Likelihood Estimation

    What Is Pruning In A Decision Tree Algorithm

    Machine Learning Interview Questions And Answers | Data Science Interview Questions | Simplilearn

    In Data Science and Machine Learning, Pruning is a technique which is related to decision trees. Pruning simplifies the decision tree by reducing the rules. Pruning helps to avoid complexity and improves accuracy. Reduced error Pruning, cost complexity pruning etc. are the different types of Pruning.

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    Python And R Programming

    Datacamp and Codecademy are good to learn and improve your programming skills for data science. They have amazing courses on R and Python. They do support learning SQL. They both are interactive platforms that make learning to code much easier and accessible.

    The programming courses on these platforms are beginner-friendly and easy to learn. Those who are new to programming must definitely try these platforms.

    Top Advanced Data Scientist Interview Questions

    Whats the difference between support a vector machine and logistic regression? Please provide examples of situations where you would choose to use one rather than the other.
    If removing missing values from a dataset causes bias, what would you do?
    When looking at a products health, engagement, or growth, what metrics would you assess?
    When trying to address or solve business problems related to our product, what metrics would you assess?
    How do you judge product performance?
    How do you know if a new observation is an outlier?
    What is the bias-variance trade-off?
    What is your method for randomly selecting a sample from a product user population?
    What is your process for data wrangling and cleaning before applying machine learning algorithms?
    How do you differentiate between good and bad data visualization?


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    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

    and histograms.

    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.

    Is Jose Portilla Responsive To Student Questions In The Dscg

    Google Analytics

    You can see what other students have to say about this in their DSCG-IP reviews.

    But, our simple all time favorite way of gauging an instructors responsiveness is to simply email the instructor and see if or how they respond.

    In this case, Udemy has a messaging system for students / anyone who has an account, and you can send Jose Portilla a message through this system quite easily, even if you havent bought DSCG-IP yet.

    For example, you could say, Hi, and I came across DSCG-IP while looking at Development courses on Udemy. If I enroll in your training, would you mind if I asked you any questions along the way?

    If you use this approach, the response from the professor will tell you everything.

    Obviously, the quicker the response the better!

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    Did Data Science Career Guide Interview Preparation Appeal To You In 30 Seconds Or Less

    Now that youve done the 30 Second Test with DSCG-IP above, what is your gut reaction to this Development course with only the basic information of its title, subtitle, and the first few opening lines of its official course summary?

    Did Jose Portilla do a good job conveying its subject matter, and did it immediately get your attention and appeal to you?

    If so, Jose Portillas online course is certainly worth considering some more.

    But if not, perhaps its in your best interest to consider some other Development courses instead, because clear communication and being able to hook and maintain your interest are two very important qualities for your online learning success.

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    Can You Comfortably Afford Dscg

    Can you comfortably afford the cost of Data Science Career Guide Interview Preparation?

    This is an important question to answer, because even if you think DSCG-IP sounds like the greatest online class in the world, its still not worth taking if you cant comfortably afford it!

    Before October 14, 2022, the price was $14.99 before any Udemy discount, and you were able to pay with a credit card.

    Keep in mind that this is a Udemy online course, and theres a great chance that you can get a solid discount on DSCG-IP with Udemy coupons / promo codes, especially with the strategies we provide for helping you find the best, most popular coupons available.

    Well cover that in greater detail in the next section, because at the end of the day, its important that you can learn whatever you want to learn without getting into a lot of credit card debt.

    Brushing Up The Right Skillset From The Right Resources For The Right Job

    A/B Testing in Data Science Interviews by a Google Data Scientist | DataInterview

    These are unprecedented times where many of us are looking to switch or land a job. Interview preparation has come to the limelight. And interviews are a big deal for everyone.

    Uncertainty, randomness, and human errors make an interview damn scary. Adrenaline rushing through your veins, you are on the verge of messing it all up.

    Preparedness is the only solution to minimize your losses during an interview. As Benjamin Franklin said:

    One of my last weeks post was on building an effective Data Science portfolio where I shared a comprehensive and actionable guide to building a portfolio.

    A good portfolio most of the time helps you get the first call and if you really know your thing, you are almost 90% there. The rest 10% is accounted for by the three traits mentioned in the first line of this post.

    So, this post intends to provide you actionable tips and resources to prepare well for your next data science interview.

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    What Is A Gradient And Gradient Descent

    Gradient: Gradient is the measure of a property that how much the output has changed with respect to a little change in the input. In other words, we can say that it is a measure of change in the weights with respect to the change in error. The gradient can be mathematically represented as the slope of a function.

    Gradient Descent: Gradient descent is a minimization algorithm that minimizes the Activation function. Well, it can minimize any function given to it but it is usually provided with the activation function only.

    Gradient descent, as the name suggests means descent or a decrease in something. The analogy of gradient descent is often taken as a person climbing down a hill/mountain. The following is the equation describing what gradient descent means:

    So, if a person is climbing down the hill, the next position that the climber has to come to is denoted by b in this equation. Then, there is a minus sign because it denotes the minimization . The Gamma is called a waiting factor and the remaining term which is the Gradient term itself shows the direction of the steepest descent.

    This situation can be represented in a graph as follows:

    Here, we are somewhere at the Initial Weights and we want to reach the Global minimum. So, this minimization algorithm will help us do that.

    Q64 Explain Svm Algorithm In Detail

    SVM stands for support vector machine, it is a supervised machine learning algorithm which can be used for both Regression and Classification. If you have n features in your training data set, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate. SVM uses hyperplanes to separate out different classes based on the provided kernel function.

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    How Should You Maintain A Deployed Model

    The steps to maintain a deployed model are:


    Constant monitoring of all models is needed to determine their performance accuracy. When you change something, you want to figure out how your changes are going to affect things. This needs to be monitored to ensure it’s doing what it’s supposed to do.


    Evaluation metrics of the current model are calculated to determine if a new algorithm is needed.


    The new models are compared to each other to determine which model performs the best.


    The best performing model is re-built on the current state of data.

    Common Situational Data Science Interview Questions

    Starting a Career in Data Science: The Ultimate Guide

    Leadership and communication are two valuable skills for Data Scientists. Employers value job candidates who can show initiative, share their expertise with team members, and communicate data science objectives and strategies.

    Here are some examples of leadership and communication data science interview questions:

    Question: What do you like about working on a multi-disciplinary team?

    Answer: A Data Scientist collaborates with a wide variety of people in technical and non-technical roles. It is not uncommon for a Data Scientist to work with developers, designers, product specialists, data analysts, sales and marketing teams, and top-level executives, not to mention clients.

    In your answer to this question, you need to illustrate that youre a team player who relishes the opportunity to meet and collaborate with people across an organization.

    Choose an example of a situation where you reported to the highest-level people in a company to show not only that you are comfortable communicating with anyone, but also to show how valuable your data-driven insights have been in the past.

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    Common Personal Data Science Interview Questions

    Along with testing your data science knowledge and skills, employers will likely also ask general questions to get to know you better. These questions will help them understand your work style, personality, and how you might fit into their company culture.

    Personal Data Scientist interview questions may include:

    Question: What makes a good Data Scientist?

    Answer: Your response to this question will tell a hiring manager a lot about how you see your role and the value you bring to an organization. In your answer, you could talk about how data science requires a rare combination of competencies and skills.

    A good Data Scientist needs to combine the technical skill needed to parse data and create models with the business sense necessary to understand the problems theyre tackling as well as recognize actionable insights in their data.

    You could also discuss a Data Scientist you look up to, whether its a colleague you know personally or an insightful industry figure.

    Be Honest About Your Experience

    Confidence and honesty can go a long way during interviews. You should remain upfront about the skills you possess and those that you dont. Do not pretend to know it all, since dishonesty does not help in the long run. You will be surprised at how recruiters appreciate honest responses, since integrity and dedication are attributes most companies look for. They may be willing to teach you how to use tools and software and may consider you a good fit for their data science team and the company in general.

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