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Data Science Interview Questions and Answers

Interview Question & Answers
Interview Q & A

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.

RPA (Robotic Process Automation) is an automated rule-based business processes to do efficient execution of deploying robots with cost-effective development. RPA reduces the human involvement in the process of automating workflow with the help of robots or software applications. There are three main terms you as a beginner need to understand:  Robotic, Process, and Automation.

Robotic: Entities that are mimic human actions.

Process: Systematic process steps for executing a meaningful activity.

Automation: Automatic action that is done by a robot without any human intervention.

These three terms together make mimicking the human actions by performing the sequence of steps and brings an effective activity without human interaction is known as Robotic Process Automation.RPA requires some basic skills for learning it with capable of understanding the business requirements and convert them into Automated Process using some RPA tools like Blue Prism, UiPath, Automated Anywhere, and so on.

Thankfully, learning RPA need not any in-depth knowledge on coding, because all the RPA tools have kind of unique mechanism to learn it quickly and deeply. However, you need to develop your logical and analytical skills and strong future is guaranteed for the one who is practicing on it.

Here, the list of skills given below which are required for learning RPA and we are sure it will help you to equip for the better software development and outshine your skills in developing RPA applications.

  • Basic Knowledge in VB and .Net framework as most of the RPA tools are developed in it.
  • Understanding of VBA Macros, Excel, and its implementation
  • Fundamental of writing and integrating Python scripts with RPA toolset
  • Basic understanding about building components like Auto-ML, NLP, and AI integrations such as Microsoft Luis or IBM Watson
  • Basics of document capture technologies such as ABBYY
  • Fundamentals of Business Process Modelling or UML (Unified Modelling Language)
  • Knowledge on Business Exceptions Handling
  • Understanding of Data Parsing with the use of XML, JSON
  • Practice with APIs in workflow development

Other than these technical skills, there are some more logical skills required to enhance in the following fields:

  • Systematic thinking
  • High level programming mindset
  • Active learning with update awareness
  • Basic mathematical concepts
  • Science and applied mathematics knowledge
  • Good judgmental and decision-making ability
  • Expert level in communication to deliver your ideas
  • Ability to solve complex problems in an easy way
  • Basic knowledge about technology design
  • Persistence in the field in any kind of situation

These basic skills will help you to learn RPA technology effectively and apply them in real time on the project development at the time of learning itself. Investing your time on developing the above said skills bring more opportunities on RPA development projects with more productivity and result-oriented outputs to the enterprises.

We can understand the requirement of RPA developers through the wide range of applications that are found in the market. Many reputed companies like Amazon, Google, and Facebook are continually in the making of RPA projects to meet the global needs of automating process like data analytics, transactions, and some other functions.

RPA Developers generally have three basic roles such as Process Designer, Automation Architect, and Production Manager. Some of the required skills for RPA developers are listed below for the on-demand roles of top companies:

Role: Process Designer

Requires Skills:

  1. Strong Analytical and Problem-Solving skills
  2. Basic experience in one or more RPA tools (Blue Prism, UI Path, and Automation Anywhere)
  3. Minimum one year of experience in coding or scripting in any programming languages, SQL databases, and application development
  4. Practice in Process Analysis, Design, and Implementation even as internship level
  5. Ability to prioritize and handle multiple portfolios
  6. Basic knowledge in Lean Six Sigma process methodologies

Role: Automation Architect

Required Skills:

  1. Better to have certifications in any of the field such as ITIL, TOGAF, CoBIT, PMP, Lean Six Sigma, and Prince2
  2. Ability to narrate technical specification documentation for required RPA projects
  3. Ability to develop complete technical architecture for any kind of RPA projects with extensible and scalable features
  4. Adequate hands-on experience in any of the following RPA tools such as Automation Anywhere, Blue Prism, UiPath, Open Span, Redwood, and WorkFusion, etc.
  5. Strong knowledge in any of the programming languages like C/C++, Python, VB Script, Java, Ruby, JavaScript, and .Net
  6. Basic knowledge in handling tools like NICE, Nuance, Enterprise Systems SAP, OCR Tools, Oracle, Custom Apps, PeopleSoft, ITSM Tools Service Now, Jira, BMC Remedy, etc.
  7. Fundamentals of Automation platforms, frameworks, and tools, etc.

Role: Production Manager

Required Skills:

  1. Minimum 2 to 3 years of experience in RPA Project development and implementations
  2. Strong knowledge in Architecture and Delivery experience
  3. More hands-on experience with real time projects using RPA tools
  4. Well-built knowledge in innovativeness and ability to integrate creative technologies
  5. Minimum 5 years of technical experience in IT industry
  6. Minimum 0-3 years of experience in Robotic Process Automation using any RPA tools and in-depth knowledge in it
  7. Aware of related RPA technologies and its version up to date

End Note:

Learning RPA and its tools will be complicated without developing the required skills for producing the unique software application development in the market. Because many companies are involved today in the process of developing the efficient RPA applications and they required developers with strong skills on the mentioned field along with the certification. Get valuable certification course in the best RPA Training Institute in Chennai at SLA Institute to acquire and equip the adequate skills to perform from day one in reputed companies.

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