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      Data Science Interview Questions and Answers

      Data Science Interview Questions With Answers
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      Data Science Interview Questions and Answers

      Data Science Interview Questions and Answers - 2023

      Top 30 Data Science Interview Questions and Answers

      1. What is data science in simple words?

      Data science is a field of Big Data geared toward providing meaningful information based on large amounts of complex data. Data science, or data-driven science, combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.

      2. What is data science and why is it important?

      Data science is about solving business problems. To anyone still asking is data science important, the answer is actually quite straightforward. It’s important because it solves business problems. … Too often businesses want machine learning, big data projects without thinking about what they’re really trying to do.

      3. What is data simple language?

      Data especially refers to numbers, but can mean words, sounds, and images. Metadata is data about data. It is used to find data. Originally, data is the plural of the Latin word datum, from dare, meaning “give”.

      4. What is the eligibility for data science?

      Education – Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.

      5. What are the different types of data?

      Understanding Qualitative, Quantitative, Attribute, Discrete, and Continuous Data Types. At the highest level, two kinds of data exist: quantitative and qualitative. There are two types of quantitative data, which is also referred to as numeric data: continuous and discrete.

      6. How do you define a set in Python?

      Set in Python is a data structure equivalent to sets in mathematics. It may consist of various elements; the order of elements in a set is undefined. You can add and delete elements of a set, you can iterate the elements of the set, you can perform standard operations on sets (union, intersection, difference).

      7. Can you iterate through a set Python?

      Iterate over a set in Python. In Python, Set is an unordered collection of data type that is iterable, mutable and has no duplicate elements. There are numerous ways that can be used to iterate over a Set. … Some of these ways include, iterating using for/while loops, comprehensions, iterators and their variations.

      8. Why list is mutable in python?

      You have to understand that Python represents all its data as objects. … Some of these objects like lists and dictionaries are mutable , meaning you can change their content without changing their identity. Other objects like integers, floats, strings and tuples are objects that can not be changed.

      9. What is hashable Python?

      From the Python glossary: … All of Python’s immutable built-in objects are hashable, while no mutable containers (such as lists or dictionaries) are. Objects which are instances of user-defined classes are hashable by default; they all compare unequal, and their hash value is their id() .

      10 .Are sets ordered?

      The Set Interface. A Set is a Collection that cannot contain duplicate elements. It models the mathematical set abstraction. … LinkedHashSet , which is implemented as a hash table with a linked list running through it, orders its elements based on the order in which they were inserted into the set (insertion-order).

      11. How do you add an element to a set in Python?

      set add() in python. The set add() method adds a given element to a set if the element is not present in the set. Syntax: set.add(elem) The add() method doesn’t add an element to the set if it’s already present in it otherwise it will get added to the set.

      12. Is Python necessary for data science?

      Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. This is why 40 percent of respondents surveyed by O’Reilly use Python as their major programming language.

      13. Is Python enough for data science?

      R and Python are the two most popular programming languages used by data analysts and data scientists. Both are free and open source – R for statistical analysis and Python as a general-purpose programming language. Excellent range of high-quality, domain specific and open source packages.

      14. How long does it take to learn Python for Data Science?

      To learn all the concepts it would take you about two weeks (assuming you study two hours a day and assuming you know a little python ) but then that is not enough because you would only know how to use those concepts with experimentation and practice which is never enough.

      15. What should I study to become a data scientist?

      There are three general steps to becoming a data scientist: Earn a bachelor’s degree in IT, computer science, math, physics, or another related field; Earn a master’s degree in data or related field; Gain experience in the field you intend to work in (ex: healthcare, physics, business).

      16. Which is better Python or R for data science?

      In a nutshell, he says, Python is better for for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets. … R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.

      17. What is SAS in data science?

      Tech and Telecom companies require huge volumes of unstructured data to be analyzed, and hence data scientists use machine learning techniques for which R and Python are more suitable. SAS is an expensive commercial software and is mostly used by large corporations with huge budgets.

      18. Which language is best for data science?

      The Most Popular Languages for Data Science

      Python. Python is at the top of all other languages and is the most popular language used by data scientists…R. R has been kicking around since 1997 as a free alternative to pricey statistical software, such as Matlab or SAS…Java….

      Scala.

      19. Is Java necessary for data science?

      If you’re starting out to build up your application from the ground level, it’s good to choose Java as your programming language. Java is Fast: Unlike some of the other widely used languages for Data Science, Java is fast. Speed is critical for building large-scale applications and Java is perfectly suited for this

      20. Does data scientist need to know programming?

      Data scientists usually have a Ph.D. or Master’s Degree in statistics, computer science or engineering. … Programming: You need to have the knowledge of programming languages like Python, Perl, C/C++, SQL and Java—with Python being the most common coding language required in data science roles.

      21. Which language is better for data science?

      Both Python and R are popular programming languages for statistics. While R’s functionality is developed with statisticians in mind (think of R’s strong data visualization capabilities!), Python is often praised for its easy-to-understand syntax.

      22. How is Python used in data science?

      Pandas is the Python Data Analysis Library, used for everything from importing data from Excel spreadsheets to processing sets for time-series analysis. … SciPy is the scientific equivalent of NumPy, offering tools and techniques for analysis of scientific data. Statsmodels focuses on tools for statistical analysis.

      23. What does data science mean?

      Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

      24. Is Data Science easy?

      Data science is easy if you have the right data scientists. I am not in any way saying that the complex discipline known as data science is easy or that becoming a proper data scientist is simple. … If you get them the data, they can create a model that delivers value where there is value to be had.

      25. What is data science with example?

      A common question among directors, managers and the C-suite is what are some examples of business cases using data science. Data science is a tool that can be used to help reduce costs, find new markets and make better decisions.

      26. Is Data Science in demand?

      Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. … I scoured job listing websites to find which skills are most in demand for data scientists.

      27. What is data science with Python?

      Pandas is the Python Data Analysis Library, used for everything from importing data from Excel spreadsheets to processing sets for time-series analysis. … SciPy is the scientific equivalent of NumPy, offering tools and techniques for analysis of scientific data. Statsmodels focuses on tools for statistical analysis.

      28. What is data science with R?

      It’s many things: R is data analysis software: Data scientists, statisticians, and analysts—anyone who needs to make sense of data, really—can use R for statistical analysis, data visualization, and predictive modeling. … R’s open interfaces allow it to integrate with other applications and systems.

      29. What does a data science do?

      “More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean.

      30. Why is data science important?

      Data Science can do more than that. Data Science helps humans make better decisions; either quicker decisions or better decisions. Companies invest a lot of money in data science so they could get the right information to make the right decisions.

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