Day: September 19, 2019

1. What is the importance of validation of data?

From a business perspective, at any stage, data validation is a very important tool since it
ensures reliability and accuracy. It is also to ensure that the data stored in your system is
accurate, clean and useful. Improper validation or incorrect data has a direct impact on sales,
revenue numbers and the overall economy.

2. What are the various approaches to dealing with missing values?

Missing values or missing data can be dealt with by taking the following approaches-
● Encoding NAs- this used to be a very common method initially when working with
machine learning and algorithms was not very common
● Deleting missing data case wise- this method works well for large datasets with very few
missing values
● Using mean/median value to replace missing values- this method works very well for
numerical features
● Run predictive models to impute missing values- this is highly effective as it works best
with the final model
● Linear regression- works well to provide good estimates for missing values

3. How do you know if a developed data model is good or bad?

A developed data model should fulfil the following criteria to qualify as a good model-
● Whether the data is the model can be easily consumed
● If the model is scalable in spite of good data changes
● Whether performance can be predicted or not
● How good and fast can a model adapt to changes

4. What are some of the challenges I can face if I were to perform a data analysis?

Performing data analysis may involve the following challenges-
● Too much data collection which can often overwhelm data analysts or employees
● Differentiation between meaningful and useless data
● Incoherent visual representation of data
● Collating and analysing data from multiple sources
● Storing massive amounts of generated data
● Ensuring and restoring both security and privacy of stored data as well as generated
data
● Inadequate experts or lack of industry professionals who understand big data in depth
● Exposure to poor quality or inaccurate data

5. Explain the method of KNN imputation.

The term imputation means replacing the missing values in a data set with some other possible
values. Using KNN imputation in data analysis helps in dealing with missing data by matching a
particular point with its nearest K neighbours assuming that it is a multi-dimensional space. This
has been a highly popular method in pattern recognition and statistical estimation since the
beginning of the 1970s.

6. What does transforming data mean?

Data transformation involves the process of converting data or information from a different
format into the required format in a system. While mostly transforming data involves the
conversion of documents, occasionally it also means conversion of a program from one
computer language to another in a format that is readable by the system.
Data transformation comprises of two key phases, data mapping to ensure smooth
transformation, and code generation, for the actual transformation to happen and run on
computer systems.

7. State the difference between null and alternative hypothesis.

It is a null hypothesis when there is no key significance or relationship between two variables
and is something that the researcher is trying to disprove. No effects are observed as a result of
null hypothesis and neither are there any changes in actions or opinions. The observations of
the researcher are a plain result of chance.
An alternative hypothesis on the other hand is just the opposite of a null hypothesis and has a
significant relationship between two measured and verified phenomena. Some effects are
observed as a result of an alternative hypothesis; and since this is something the researcher is
trying to prove, some amount of changes in opinions and actions are involved. An alternative
hypothesis is a result of a real effect.

8. What would you mean by principal component analysis?

Principal component analysis is a method used to reduce large data sets in dimension by
transforming larger sets of variables into smaller ones, while retaining the principal information.
This is majorly done with the intent of improving accuracy since smaller data sets are easier to
explore, as a result of which data analysis gets faster and quicker for machine learning.

9. Define the term – logistic regression.

Logistic regression is a form of predictive analysis in machine learning that attempts to identify
relationships between variables. It is used to explain the relationship between a binary variable
and one or multiple nominal, ordinal, interval or ratio-level variables, while also describing the
data. Logistic regression is used for categorical dependent variables.

10. How can I deal with multi-source problems?

Storing the same data can often cause quality hindrances in analytics. Depending on what the
magnitude of the issues are, a complete data management system needs to be put in place.
Data reconciliation, elaborate and informative databases and pooling in segmented data can
help in deal with multi-source problems. Aggregation and data integration is also helpful while
dealing with multi-source data.

11. List the most important types of clustering algorithms.

The most important types of clustering algorithms are-
● Connectivity models- based on the idea that farther data points from each other exhibit
less similarity when compared to closer data points in data space
● Centroid models- the closeness of a data point to the cluster centroid derives the notion
of similarity for this model
● Distribution models- based on the probability that all data points in the same cluster are
part of the same distribution
● Density models- search for varied density areas of data points in the data space

12. Why do we scale data?

Scaling is important because sometimes your data set will have a set of features that completely
or partially vary in terms of units, range and magnitude. While certain algorithms have minimum
or zero effects, scaling can actually have positive impacts on the data. It is an important step of
data pre-processing that also helps to normalise data within a given range. Scaling of data also
often helps in speeding up algorithm calculations.

Finally, your diligence has paid off, and you have now landed an interview. Going for an interview can be quite nerve-wracking and stressful.  While there are a plethora of things that can go wrong like a limp, awkward handshake to having to answer a question you have no clue about, however, there are definitely few things that you can control with a bit of preparation on your end. Here are 13 things about interview preparations you may not have known about but can surely help you to ace the interview.

• Pick an outfit

How you dress is quite a crucial part of preparing for your interview. A smart and well-fitted outfit makes the first impression when you walk in for the meeting. Get it properly cleaned and pressed, pair it with proper shoes and accessories. Out Tip: Try it out to see it fits you well, and you look smart and presentable in it.

• Learn and study your resume well

All your interviewer has is your resume to know you so it is a fair game that they can ask you to elaborate on any of the jobs that you have listed in it. Even if it is something you might have held a long time back, you should be ready to talk about when asked. If it is in the resume be well prepared to explain the job you did, your responsibility and any other questions related to it, and it’s a step you cannot avoid.

• Print Multiple Copies of Your Resume

Never take a single copy of your resume, instead take multiple copies so that you can hand it to every only person who is interviewing you.  Also, it is recommended that a copy of your resume be kept in front of you as it can be used as a cheat sheet for you to answer some behavioural questions.

• Bullet Points to behavioural questions

You might be asked behavioural questions by your interviewer, to ensure that you do not stumble while answering make points of five to seven essential things you want to address. It is advisable that you don’t look into a piece of paper to answer your interviewer. But in case you get stuck do refer to the pointers.

• Practice aloud

Not many do these but practice some general questions aloud so that you can modulate your voice. You would not want to be too loud or too soft for your interviewers.

• Carry a notebook and pen

One of the essential parts of preparing for an interview is to carry a couple of pens and a laptop. This establishes your desire to perform and your sincerity.

• In-depth research of the company

While preparing for the interview learn about the company in details. The sector that the company represents how it fits into the market and the company’s mission and vision. This will help you to align your answers to the question asked in the interview with the company’s profile.

• Think of an intelligent question

While you are studying the company profile and your job description in details if you are unsure about anything make some points. You can bring up those points and ask your interviewer questions related to them. When you ask intelligent questions, it represents that effort has been made by you to prepare for the interview.

• Know the type of interview

Talk to your recruiter if it is not already mentioned when you applied for the job about the kind of conversation you would be part of. Do not assume that it will be one-on-one; since there are group discussions and behavioural interviews that a firm can conduct.

• Know the direction of the venue

Even if you have a GPS print out the instructions to the site, because you never know when your internet might fail to work. Study the flow of traffic you might face during the day and area when your interview has been scheduled. Do have the phone number of the contact person of the company in case you are lost or late. You should always be on time, do not be more than 10 minutes early, as your interviewer might not be ready.

• Do not use certain words

It’s a big no to use phrases like ‘I am nervous’ instead say I am excited. Instead of ‘I don’t know’ use the phrase, “I am not sure of the answer, or I need to learn about it’. Never say‘I don’t’ have a question’ rather you can ask ‘ What is the biggest challenge of working in this industry’ or some open-ended question that is based on what you observed and learned about the interviewer or the company. Instead of using a word like ‘um…’ when not sure about our reply, and you need some time to think, say ‘ That’s an interesting question.’

• Carry Some Gums or Mints

The last thing you would want is your interviewer to breathe in some pungent odour from your mouth. A fresh smell is always recommended; so keep some gums or mints that you can use before entering the interview.

• Keep the basic hygiene items

No matter what the venue of the interview is, keep the essential hygiene items with you like tissues, hand sanitizer, lotion, chapstick and any small things that give you confidence. Remember to pack them in the bag you will be carrying to the interview.

At an interview be sure to be articulate and natural with your answers. A well-prepared discussion will give you a better chance of securing the job than the others. As said by some experts it is important to remember the 5Ps for preparing for an interview: Prior Planning Prevents Poor Performance.