Simplilearn Data Analyst Questions

Question 1

What is data mining?

Answer 1

process of finding new, relevant information; it takes raw data and transforms it into valuable information

Question 2

What is data profiling?

Answer 2

process of assessing a dataset for uniqueness, consistency, and logic; it usually doesn’t involving identifying incorrect/inaccurate data

Question 3

What is data wrangling?

Answer 3

the process of cleaning, structuring, and enriching raw data into a desired, usable format for better decision-making

Question 4

What is a simple process of data wrangling?

Answer 4

discover –> structure –> clean –> enrich –> validate –> analyze

Question 5

Data Wrangling vs Data Cooking?

Answer 5

Data cooking involves falsifying data or selectively deleting data to improve a hypothesis

An example is demographic data being manipulated by fieldworkers, researchers, etc. to support behavioral science theory.

Question 6

What are common problems data analysts encounter during analysis?

Answer 6

1) handling duplicate/missing values
2) collecting meaningful, correct data at the right time
3) making data secure
4) dealing with compliance issues (ensuring that sensitive data is organized and meets enterprise business rules and legal/govt regulations)
5) handling data purging (freeing up database space) and storage issues

Question 7

What are some steps in the analytics project?

Answer 7

1) state/understand the problem
2) collect data
3) clean data
4) explore and analyze the data
5) interpret results

Question 8

What are some technical tools used for analysis and presentation?

Answer 8

MS SQL Server, MySQL, MS Excel, IBM SPSS, Tableau, Python, MS PowerPoint

Question 9

What are some best practices for data cleaning?

Answer 9

Make a data cleaning plan by understanding where common errors happen and keep communications open;

Identify and remove duplicates before working with data;

Focus on accuracy, maintain value types of data, provide mandatory constraints, and set cross-field validation;

Standardize the data at the point of entry so that there’s less chaos and fewer errors occur

Question 10

How can you handle missing values in a dataset?

Answer 10

Listwise deletion: an entire record is excluded from analysis if any single value is missing

Average imputation: Use the average value of the responses from other participants to replace missing values

Regression substitution: use multiple-regression analysis to estimate a missing value

Multiple imputation: create plausible values based on correlations for missing data and then averages the simulated datasets by incorporating random errors in predictions

Question 11

What is a normal distribution?

Answer 11

It is a type of continuous probability distribution that is symmetric about a mean and appears as a bell curve.

mean = median = mode and they are located at the center of the graph

68% of data lies within 1 std away from the mean/avg

95% data falls within 2 std away from mean/avg

99.7% data falls within 3 std away from mean/avg

Question 12

What is time series analysis?

Answer 12

Time series analysis is a statistical method that deals with an ordered sequence of values of a variable at equally spaced time intervals

Question 13

In Tableau, what differs between joining and blending?

Answer 13

Data joining can only be done when data comes from the same source. So to combine two tables, the tables must be from the same databases or two/more sheets from the same Excel file.

Meanwhile, data blending is used when data is from 2/more different sources. An example would be combining an Oracle table with SQL Server or combining an Excel sheet and Oracle table.

In data joining, all combined tables/sheets contains a common set of dimensions/measures. On the other hand, data blending has each data source contain its own set of dimensions/measures.

Question 14

Overfitting vs Underfitting

Answer 14

Overfitting: A model trains the data too well using the training set, causing significant performance drops over the test/validation set. This happens when the model understands noise and random fluctuations too well and over-specifies.

Underfitting: A model is not able to train data well or generalize new data. It performs poorly on both training and testing data. This happens when there is less data to build an accurate model or if the model does not suit the data (e.g.: using a linear model on non-linear data).

Question 15

In MS Excel, a numeric value can be treated as a text value if it is preceded by an…

Answer 15

Apostrophe (‘)

Question 16

In Excel, what is the difference between COUNT, COUNTA, COUNTBLANK, and COUNTIF?

Answer 16

COUNT: returns the number of numeric cells in a range

COUNTA: returns the number of non-blank cells in a range

COUNTBLANK: returns the number of blank cells in a range

COUNTIF: returns the number of values by checking a given condition

Question 17

How does the function VLOOKUP work in Excel?

Answer 17

Can be used to find things in a table or a range by row.

VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])

lookup_value: value you want to look up

table_array: range where lookup_value is located

col_index_num: column number in the range that contains the return value

range_lookup: specify TRUE if you want to approximate match or FALSE if you want an exact match of the return value

Question 18

In SQL, how do you subset or filter data?

Answer 18

Use WHERE and HAVING clauses

Ex1: select * from MOVIES where Director = ‘Brad Bird’;

Ex2: 
select Director, 
sum(Duration) as total_duration,
avg(Duration) as avg_duration
from Movies
group by Director
having avg(Duration)>151;

Question 19

In SQL what is the difference between WHERE and HAVING clauses?

Answer 19

the WHERE clause: works on row data, filter occurs before any groupings; aggregate functions cannot be used

SELECT col1, col2,…
FROM table_name
WHERE condition;

Meanwhile…

the HAVING clause: works on aggregated data; used to filter values from a group; allows aggregate functions

SELECT col1, col2, ...
FROM table_name
WHERE condition
GROUP BY column_names
HAVING condition
ORDER BY column_names

Question 20

How do you use the reshape() function in NumPy?

Answer 20

reshape(array, shape)

Ex1:
import numpy as np
a = np.array([[1,2,3,4,5],[6,7,8,9,10]])
np.reshape(a, (2,5))

Question 21

What are different ways to create a dataframe in Pandas?

Answer 21

import pandas as pd…

1) by initializing a list
data = [[‘tom’, 30], [‘jerry’, 20], [‘angela’, 35]]
df = pd.DataFrame(data, columns = [‘Name’, ‘Age’])

2) by initializing a dictionary
data = {‘Name’:[‘Tom’, ‘Jerry’, ‘Angela’], ‘Age’: [20, 35, 19]}
df = pd.DataFrame(data)

Question 22

Suppose you have an “emp.csv” file. Using Python, how can you create an employees dataframe and display the head and summary?

Answer 22

import pandas as pd

employees = pd.read_csv(“File_path”)

employees. head()
employees. info()

Question 23

In Python, how do you select specific columns from a dataframe?

Answer 23

for one column: df[col_name]

for two or more columns: df[[col1, col2,…]]

Question 24

What criteria entails that a developed data model is good or bad?

Answer 24

Good models are…

intuitive, insightful, and self-explanatory

easily consumed by the clients for actionable and profitable results

easily adapted to changes according to business requirements

scale according to new data when data gets updated

Question 25

Why is Exploratory Data Analysis so important?

Answer 25

EDA helps understand data better.

Helps obtain confidence in data, giving reassurance to go ahead and engage in developing a ML algorithm

Allows you to refine your selection of feature variables for later model building

Allows you discover hidden trends and insights

Question 26

How do you treat outliers?

Answer 26

drop, cap your outliers, assign new values, or try a new transformation (as last resort)

Question 27

Descriptive vs predictive vs prescriptive (analytics)?

Answer 27

Descriptive: provides insights into past/history to answer “What has happened?”; uses data aggregation and mining techniques

Predictive: understands the futur and answers “What could happen?”; uses statistical models and forecasting techniques

Prescriptive: suggests various courses of action to answer “What should you do?”; uses optimization and simulation algorithms to advise possible outcomes

Question 28

What are different types of sampling techniques?

Answer 28

Sampling = statistical method that selects a subset of data from an entire dataset (population) to estimate the characteristics of the population

simple random sampling

systematic sampling

cluster sampling

stratified sampling

judgmental/purposive sampling

Question 29

What are the different types of hypothesis testing?

Answer 29

null hypothesis: states there’s no relation between the predictor and outcome variables in the population; denoted by H0

alternative hypothesis: states there’s some relation between the predictor and outcome variables in the population; denoted by H1