Data Science
Data Science is an interdisciplinary field that uses scientific methods, algorithms and systems to extract knowledge and insights from structured and unstructured data. It combines the principles and practices from a variety of fields such as mathematics, statistics, computer engineering and more.
The data science life cycle looks something like this:
Data science uses various tools and techniques including data analytics to gather meaningful insights and present them to business stakeholders. On the other hand, data analytics is one of the techniques that analyzes raw data to determine trends and patterns. These trends and patterns can help guide businesses in making effective and efficient decisions. Data analytics uses historical and present data to understand current trends. Whereas, data science uses predictive analytics to determine future problems and drive innovations. Answering this data science interview question can distinguish you from the rookies.
Sampling is at the core of data science and hence, this data science interview question gives you the opportunity to display your core knowledge. When the data set is very large in size, it is not feasible to conduct an analysis on the entire data set. In such cases, it is critical to select a sample from the given population and conduct data analytics on the selected dataset. This requires caution as a representative sample that represents the true characteristics of the entire population must be selected. The two main sampling techniques used as per statistical needs are:
This is an important data science statistics interview question. Let’s outline the differences:
Underfitting: Underfitting means that the statistical model does not fit the existing data set. Underfitting occurs when less training data is provided. The statistical model in underfitting is extremely weak in identifying the relationship in the data and thus, unable to identify any underlying trends. Underfitting can ruin the accuracy of the machine learning model. It can be avoided if more data is used and the number of features is reduced by using feature selection.
Overfitting: A statistical model is overfitted when a lot of data is used to train it. When too much data is used the model learns from the noise and inaccurate data as well, resulting in the inability of the model to categorize the data accurately. Overfitting occurs when non-parametric and non-linear methods are used. Solutions include using a linear algorithm and using parameters such as maximal depth.
Sometimes simple data science interview questions like the above can catch you off-guard, make sure you are prepared with such questions.
When there is an unequal distribution of data across categories, the data is said to be imbalanced. Imbalanced data produces inaccurate results and model performance errors. Additionally, when training a model using an imbalanced dataset, the model pays more attention to the highly populated classes and poorly identifies the less populated classes.
Python is the most popular language for data science, followed by R. This is so because Python provides great functionality for statistics, mathematics and scientific functions. Further, it offers rich libraries for data science applications.
Structured, semi-structured, and unstructured data are the three types of data in big data.
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, either to classify data or predict outcomes.
Volume, Velocity, Variety, Veracity, and Value are the five V’s of big data.
Raw data can be processed more than once. This is often done to clean or transform the data.
MongoDB is a form of a NoSQL database.
Enumeration is a process of assigning a numerical value to each member of a set or group. This can be used to count things or to identify members of a group.
MICE is a data imputation package, which can be used to fill in missing values in data.
Outliers are values that deviate significantly from the rest of the data and are sometimes caused by errors.
Relational databases use a language called SQL (Structured Query Language) that is useful in manipulating data in the database.
Python would be best suited for text analytics because of rich libraries like Pandas.
A P-value greater than 0.5 indicates that the null hypothesis is more likely true than the alternative hypothesis.
Yes, a tuple is an immutable data structure, which means that once it is created, it cannot be modified.
A lambda function has only one expression.
NLP stands for Natural Language Processing, which is a process of extracting information from text data.
Disaggregation of data is the process of breaking down data into smaller, more manageable pieces.
To normalize variables, you need to standardize the data so that each variable has a mean of 0 and a standard deviation of 1.
Deep learning is a subset of machine learning that that enables machines to learn from experience and understand the world in terms of a hierarchy of concepts. Deep learning can be used to build intelligent systems that can make decisions and predictions based on data.
The vertical representation of data is known as column, while the horizontal representation of data is known as rows.
The "K" in K-means algorithm stands for the number of clusters that the algorithm will form. K-means is an unsupervised learning algorithm that clusters data into K distinct clusters.
This extensive list of data science interview questions is designed to cater to the needs of both developers and technical recruiters. These interview questions test developers on different topics, including mathematics, statistics, programming, ML, etc. Whether you are a fresher or a developer who is looking for a job change, these data science interview questions and answers will help you prepare for the job. For hiring managers, these questions can serve as a reference to assess the proficiency of Data Scientists.
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