Harvard Business Review referred to it as “The Sexiest Job of the 21st Century.” Glassdoor placed it in the first position on the 25 Best Jobs in America list. According to IBM, demand for this role will soar 28% by 2020.

It should come as no surprise that in the new era of Big Data and machine learning, data scientists are becoming rock stars. Companies that can leverage massive amounts of data to improve the way they serve customers, build products, and run their operations will be positioned to thrive in this economy.

Here is a video on Why Data Scientist is the best job. I hope you find it beneficial.

It’s simply impossible to ignore the importance of data and our capacity to analyze, consolidate, and contextualize it. Data scientists are relied upon to fill this need, but there is a serious shortage of qualified candidates worldwide.

If you’re moving down the path to be a data scientist, you need to be prepared to impress prospective employers with your knowledge. In addition to explaining why data science is so important, you’ll need to show that you're technically proficient with Big Data concepts, frameworks, and applications.

Here's a list of 20 of the most popular questions you can expect in an interview and how to frame your answers.

**Answer: **

A feature vector is an n-dimensional vector of numerical features that represent some object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics, called features, of an object in a mathematical, easily analyzable way.

**Answer:**

- Take the entire data set as input.
- Look for a split that maximizes the separation of the classes. A split is any test that divides the data into two sets.
- Apply the split to the input data (divide step).
- Re-apply steps 1 to 2 to the divided data.
- Stop when you meet some stopping criteria.
- This step is called pruning. Clean up the tree if you went too far doing splits.

**Answer:**

Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from reoccurring.

**Answer:**

Logistic Regression is also known as the logit model. It is a technique to forecast the binary outcome from a linear combination of predictor variables.

**Answer:**

Recommender systems are a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product.

**Answer:**

It is a model validation technique for evaluating how the outcomes of a statistical analysis will generalize to an independent data set. It is mainly used in backgrounds where the objective is forecast, and one wants to estimate how accurately a model will accomplish in practice. The goal of cross-validation is to term a data set to test the model in the training phase (i.e., validation data set) to limit problems like overfitting and gain insight on how the model will generalize to an independent data set.

**Answer:**

The process of filtering used by most recommender systems to find patterns and information by collaborating perspectives, numerous data sources, and several agents.

**Answer:**

No, they do not because, in some cases, they reach a local minima or a local optima point. You would not reach the global optima point. This is governed by the data and the starting conditions.

**Answer:**

This is a statistical hypothesis testing for randomized experiments with two variables, A and B. The objective of A/B testing is to detect any changes to a web page to maximize or increase the outcome of a strategy.

**Answer:**

Some drawbacks of the linear model are:

- The assumption of linearity of the errors.
- It can’t be used for count outcomes or binary outcomes
- There are overfitting problems that it can’t solve

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**Answer:**

It is a theorem that describes the result of performing the same experiment a large number of times. This theorem forms the basis of frequency-style thinking. It says that the sample means, the sample variance and the sample standard deviation converge to what they are trying to estimate.

**Answer:**

These are extraneous variables in a statistical model that correlates directly or inversely with both the dependent and the independent variable. The estimate fails to account for the confounding factor.

**Answer:**

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.

**Answer:**

You will want to update an algorithm when:

- You want the model to evolve as data streams through infrastructure
- The underlying data source is changing
- There is a case of non-stationarity

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**Answer:**

Eigenvectors are for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvalues are the directions along which a particular linear transformation acts by flipping, compressing, or stretching.

**Answer:**

Resampling is done in any of these cases:

- Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points
- Substituting labels on data points when performing significance tests
- Validating models by using random subsets (bootstrapping, cross-validation)

**Answer:**

Selection bias, in general, is a problematic situation in which error is introduced due to a non-random population sample.

**Answer:**

- Selection bias
- Under coverage bias
- Survivorship bias

**Answer:**

It is the logical error of focusing aspects that support surviving some process and casually overlooking those that did not work because of their lack of prominence. This can lead to wrong conclusions in numerous different means.

**Answer:**

The underlying principle of this technique is that several weak learners combined to provide a keen learner. The steps involved are

- Build several decision trees on bootstrapped training samples of data
- On each tree, each time a split is considered, a random sample of mm predictors is chosen as split candidates, out of all pp predictors
- Rule of thumb: At each split m=p√m=p
- Predictions: At the majority rule

For data scientists, the work isn’t easy, but it’s rewarding, and there are plenty of available positions out there. Be sure to prepare yourself for the rigors of interviewing and stay sharp with the nuts-and-bolts of data science.

Name | Date | Place | |
---|---|---|---|

Data Science Certification Training - R Programming | 11 Nov -6 Dec 2019, Weekdays batch | Your City | View Details |

Data Science Certification Training - R Programming | 23 Nov -4 Jan 2020, Weekend batch | New York City | View Details |

Data Science Certification Training - R Programming | 30 Nov -11 Jan 2020, Weekend batch | Atlanta | View Details |

An experienced process analyst, Bhargav specializes in adapting current quality management best practices to the needs of fast-paced digital businesses.

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