Data Analyst Interview Questions And Answers

Data analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap and evaluate data.

The average or the arithmetic mean is defined as the sum of all measurements divided by the number of observations. For example, the average of the numbers 10, 12 and 22 is 14.6 because 10+12+22=44 and there are three observations, so 44 divided by three is 14.6. When there are only numbers involved, an average problem is quite simple.

★ Mode
★ Median

There are many data analysis topics that you should be familiar with. Many of them are described here. They include:
★ Averages
★ Box Plots
★ Normal Distributions
★ Counting Methods - Sets

The median of a set of numbers is the number that is exactly half way between the largest and smallest number, when the numbers are arranged from smallest to largest.

A box plot graphically displays groups of numerical data through their five-number summaries: the smallest observation (sample minimum), lower quartile (Q1), median (M), upper quartile (Q2) and largest observation (sample maximum).
The box plot is often called a "whisker" plot because "whiskers" extend outward from the boxes to the least and greatest values.

All normal distributions satisfy the following properties:

★ Approximately 68% of the observations fall within 1 standard deviation of the mean
★ Approximately 95% of the observations fall within 2 standard deviations of the mean
★ The mean, median and mode of the data are nearly all equal

A counting principle that you may appear on a data analysis question is the inclusion-exclusion principle, which states that the number of elements in the union of two finite sets equals the count of the members in the two sets minus the number of elements in the intersection of the two sets. The number of elements in a set is denoted using two parallel lines, ||. Using set notation, the inclusion-exclusion principle for sets D and E is written as the following:

|D∪E| = |D| + |E| - |D∩E|

Data analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap and evaluate data.

The mode of a set of numbers is the number that appears most often.

The quartiles of a set of values are the three points that divide the data set into four equal groups, each representing a fourth of the population being sampled.

A measure of the dispersion of the data that is shown in a box plot is called the interquartile range. It is the difference between the upper quartile and the lower quartile.

The normal distribution is a pattern for the distribution of data which follows a bell-shaped curve. The normal distribution is called normal because the data points are concentrated in the center near the mean. The data does not usually contain extreme values and the probability of deviations of the data points from the mean are (nearly) identical in either direction.

A set is a collection of distinct objects that have a specific property.
For example, the set of all digits greater than 5 includes the numbers 6, 7, 8 and 9.

There are two objects of set:
★ Members
★ Elements

A set that contains no members is called the empty set and is denoted with the symbol '∅'.

Two operations to perform sets are:
★ Intersection
★ Union

If two sets have no members in common then the two sets are called disjoint set.

A Venn Diagram is one way that sets can be visualized.

The purpose of the data analysis and interpretation phase is to transform the data collected into credible evidence about the development of the intervention and its performance.

This process usually includes the following steps:
★ Organizing the data for analysis (data preparation)
★ Describing the data
★ Interpreting the data (assessing the findings against the adopted evaluation criteria)

Where quantitative data have been collected, statistical analysis can help measure the degree of change that has taken place and allow an assessment to be made about the consistency of data.

★ Relevance
★ Results/Impact
★ Sustainability
★ Effectiveness
★ Efficiency

One of the most important issues in interpreting research findings is understanding how outcomes relate to the intervention that is being evaluated. This involves making the distinction between association and causation and the role that can be played by confounding factors in skewing the evidence.

An association exists when one event is more likely to occur because another event has taken place. However, although the two events may be associated. One does not necessarily cause the other. The second event can still occur independently of the first.

Example:
Some research supports an association between certain patterns of drinking and the incidence of violence. However, even though harmful drinking and violent behavior may co-occur, there is no evidence showing that it is drinking that causes violence.