ITGSS Certified Professional: Data
analyst Exam Questions with Correct
Answers
Explain the typical data analysis process.(5) - Answer-Implementation of the Model
and Tracking
This is the final step of the data analysis process wherein the model is implemented
in production and is tested for accuracy and efficiency.
What is the difference between Data Mining and Data Profiling?(DM) - Answer-Data
Mining refers to the analysis of datasets to find relationships that have not been
discovered earlier. It focusses on sequenced discoveries or identifying
dependencies, bulk analysis, finding various types of attributes, etc.
What is the difference between Data Mining and Data Profiling?(DP) - Answer-Data
Profiling, also referred to as Data Archeology is the process of assessing the data
values in a given dataset for uniqueness, consistency and logic. Data profiling
cannot identify any incorrect or inaccurate data but can detect only business rules
violations or anomalies. The main purpose of data profiling is to find out if the
existing data can be used for various other purposes.
How often should you retrain a data model? - Answer-A good data analyst is the one
who understands how changing business dynamics will affect the efficiency of a
predictive model. You must be a valuable consultant who can use analytical skills
and business acumen to find the root cause of business problems.
How often should you retrain a data model? - Answer-The best way to answer this
question would be to say that you would work with the client to define a time period
in advance. However, I would refresh or retrain a model when the company enters a
new market, consummate an acquisition or is facing emerging competition. As a data
analyst, I would retrain the model as quick as possible to adjust with the changing
behaviour of customers or change in market conditions.
What is data cleansing? - Answer-From a given dataset for analysis, it is extremely
important to sort the information required for data analysis. Data cleaning is a crucial
step in the analysis process wherein data is inspected to find any anomalies, remove
repetitive data, eliminate any incorrect information, etc. Data cleansing does not
involve deleting any existing information from the database, it just enhances the
quality of data so that it can be used for analysis.
Mention few best practices that you have followed while data cleansing.(1) - Answer-
Developing a data quality plan to identify where maximum data quality errors occur
so that you can assess the root cause and design the plan according to that.
, Mention few best practices that you have followed while data cleansing.(2) - Answer-
Follow a standard process of verifying the important data before it is entered into the
database.
Mention few best practices that you have followed while data cleansing.(3) - Answer-
Identify any duplicates and validate the accuracy of the data as this will save lot of
time during analysis.
Mention few best practices that you have followed while data cleansing.(4) - Answer-
Tracking all the cleaning operations performed on the data is very important so that
you repeat or remove any operations as necessary.
How will you handle the QA process when developing a predictive model to forecast
customer churn? - Answer-Data analysts require inputs from the business owners
and a collaborative environment to operationalize analytics. To create and deploy
predictive models in production there should be an effective, efficient and repeatable
process. Without taking feedback from the business owner, the model will just be a
one-and-done model.
The best way to answer this question would be to say that you would first partition
the data into 3 different sets Training, Testing and Validation. You would then show
the results of the validation set to the business owner by eliminating biases from the
first 2 sets. The input from the business owner or the client will give you an idea on
whether you model predicts customer churn with accuracy and provides desired
results.
Mention some common problems that data analysts encounter during analysis.(1&2)
- Answer-Having a poor formatted data file. For instance, having CSV data with un-
escaped newlines and commas in columns.
Having inconsistent and incomplete data can be frustrating.
Mention some common problems that data analysts encounter during analysis.(3&4)
- Answer-Common Misspelling and Duplicate entries are a common data quality
problem that most of the data analysts face.
Having different value representations and misclassified data
What are the important steps in data validation process?(1) - Answer-Data Validation
is performed in 2 different steps-
Data Screening - In this step various algorithms are used to screen the entire data to
find any erroneous or questionable values. Such values need to be examined and
should be handled
What are the important steps in data validation process?(2) - Answer-Data Validation
is performed in 2 different steps-
Data Verification- In this step each suspect value is evaluated on case by case basis
and a decision is to be made if the values have to be accepted as valid or if the
analyst Exam Questions with Correct
Answers
Explain the typical data analysis process.(5) - Answer-Implementation of the Model
and Tracking
This is the final step of the data analysis process wherein the model is implemented
in production and is tested for accuracy and efficiency.
What is the difference between Data Mining and Data Profiling?(DM) - Answer-Data
Mining refers to the analysis of datasets to find relationships that have not been
discovered earlier. It focusses on sequenced discoveries or identifying
dependencies, bulk analysis, finding various types of attributes, etc.
What is the difference between Data Mining and Data Profiling?(DP) - Answer-Data
Profiling, also referred to as Data Archeology is the process of assessing the data
values in a given dataset for uniqueness, consistency and logic. Data profiling
cannot identify any incorrect or inaccurate data but can detect only business rules
violations or anomalies. The main purpose of data profiling is to find out if the
existing data can be used for various other purposes.
How often should you retrain a data model? - Answer-A good data analyst is the one
who understands how changing business dynamics will affect the efficiency of a
predictive model. You must be a valuable consultant who can use analytical skills
and business acumen to find the root cause of business problems.
How often should you retrain a data model? - Answer-The best way to answer this
question would be to say that you would work with the client to define a time period
in advance. However, I would refresh or retrain a model when the company enters a
new market, consummate an acquisition or is facing emerging competition. As a data
analyst, I would retrain the model as quick as possible to adjust with the changing
behaviour of customers or change in market conditions.
What is data cleansing? - Answer-From a given dataset for analysis, it is extremely
important to sort the information required for data analysis. Data cleaning is a crucial
step in the analysis process wherein data is inspected to find any anomalies, remove
repetitive data, eliminate any incorrect information, etc. Data cleansing does not
involve deleting any existing information from the database, it just enhances the
quality of data so that it can be used for analysis.
Mention few best practices that you have followed while data cleansing.(1) - Answer-
Developing a data quality plan to identify where maximum data quality errors occur
so that you can assess the root cause and design the plan according to that.
, Mention few best practices that you have followed while data cleansing.(2) - Answer-
Follow a standard process of verifying the important data before it is entered into the
database.
Mention few best practices that you have followed while data cleansing.(3) - Answer-
Identify any duplicates and validate the accuracy of the data as this will save lot of
time during analysis.
Mention few best practices that you have followed while data cleansing.(4) - Answer-
Tracking all the cleaning operations performed on the data is very important so that
you repeat or remove any operations as necessary.
How will you handle the QA process when developing a predictive model to forecast
customer churn? - Answer-Data analysts require inputs from the business owners
and a collaborative environment to operationalize analytics. To create and deploy
predictive models in production there should be an effective, efficient and repeatable
process. Without taking feedback from the business owner, the model will just be a
one-and-done model.
The best way to answer this question would be to say that you would first partition
the data into 3 different sets Training, Testing and Validation. You would then show
the results of the validation set to the business owner by eliminating biases from the
first 2 sets. The input from the business owner or the client will give you an idea on
whether you model predicts customer churn with accuracy and provides desired
results.
Mention some common problems that data analysts encounter during analysis.(1&2)
- Answer-Having a poor formatted data file. For instance, having CSV data with un-
escaped newlines and commas in columns.
Having inconsistent and incomplete data can be frustrating.
Mention some common problems that data analysts encounter during analysis.(3&4)
- Answer-Common Misspelling and Duplicate entries are a common data quality
problem that most of the data analysts face.
Having different value representations and misclassified data
What are the important steps in data validation process?(1) - Answer-Data Validation
is performed in 2 different steps-
Data Screening - In this step various algorithms are used to screen the entire data to
find any erroneous or questionable values. Such values need to be examined and
should be handled
What are the important steps in data validation process?(2) - Answer-Data Validation
is performed in 2 different steps-
Data Verification- In this step each suspect value is evaluated on case by case basis
and a decision is to be made if the values have to be accepted as valid or if the
