Do you know Data Scientists spend approximately 40 to 50% of their time cleaning data. Well, it’s only fair. Because quality data is central to good data analysis, building machine learning algorithms, and consequently producing quality results.
So, exactly what is data cleaning?
Data cleaning refers to removing and updating duplicate, incomplete, or inconsistent data. It is important to clean data as bad quality data hampers decision-making. To leverage data for profitability, companies look for Data Scientists with excellent data cleaning skills.
So, if you are interviewing for a Data Scientist role, you are likely to be asked questions on data cleaning. To help you prepare, we’ve curated a list of top data cleaning questions with answers which will help you crack the Data Scientist interview. Along with data cleaning, the list also covers some commonly asked data-related questions.
Let’s get started.
Data Cleaning Questions for Data Scientist Interview
1. List the best practices for cleaning data
The best practices for data cleaning include:
- Removing unwanted and duplicate data
- Fixing structural errors such as typos, inconsistent capitalization, and more
- Handling the missing values and data
- Filtering outliers to avoid misleading results
2. How to remove duplicate observations from a data frame in python?
3. How are null values stored in pandas data frames?
4. How to drop variables from pandas DataFrame?
5. How are missing values denoted in pandas, and which function is used to find missing values in a pandas DataFrame?
In pandas, missing values are denoted as NaN. You can find all missing values in a DataFrame by using the isna() function from the pandas library:
Common Data Scientist Interview Questions
6. What do you understand by unstructured data?
Unstructured data is a form that doesn’t explicitly have the structure, high degree, or organization. Examples of unstructured data include images, audio, and language text.
7. Write the code to load a Latin1 encoded dataset into the python environment.
8. How to see the first five rows of a data frame in Python?
9. Define data profiling
Data profiling refers to analyzing the attributes of data such as data type, frequency, length, discrete values, and value ranges.
10. How to check the class of each variable in a pandas DataFrame?
11. Write the code to see the dimensions of a data frame in Python.
12. Write the syntax to find the value counts of a variable.
13. Write the code for performing Pandas Profiling in python.
14. What do you understand by data mining?
Data mining is used to perform functions such as identifying unusual records, analyzing data clusters, and sequence discovery.
15. Explain the describe function in python.
The describe() function gives the mean, standard deviation, and Inter Quartile Range (IQR) values.
To know more about Data Scientist interviews, watch Neha’s story here. In this video, Neha, Data Scientist at Applied Materials shares her Data Science interview experience, what questions were asked, and how she answered them.
PinWe hope you found this article useful. Also, check out our blogs on Python and SQL questions for Data Scientist interview preparation.
Do you know Data Scientists spend approximately 40 to 50% of their time cleaning data. Well, it’s only fair. Because quality data is central to good data analysis, building machine learning algorithms, and consequently producing quality results.
So, exactly what is data cleaning?
Data cleaning refers to removing and updating duplicate, incomplete, or inconsistent data. It is important to clean data as bad quality data hampers decision-making. To leverage data for profitability, companies look for Data Scientists with excellent data cleaning skills.
So, if you are interviewing for a Data Scientist role, you are likely to be asked questions on data cleaning. To help you prepare, we’ve curated a list of top data cleaning questions with answers which will help you crack the Data Scientist interview. Along with data cleaning, the list also covers some commonly asked data-related questions.
Let’s get started.
Data Cleaning Questions for Data Scientist Interview
1. List the best practices for cleaning data
The best practices for data cleaning include:
- Removing unwanted and duplicate data
- Fixing structural errors such as typos, inconsistent capitalization, and more
- Handling the missing values and data
- Filtering outliers to avoid misleading results
2. How to remove duplicate observations from a data frame in python?
3. How are null values stored in pandas data frames?
4. How to drop variables from pandas DataFrame?
5. How are missing values denoted in pandas, and which function is used to find missing values in a pandas DataFrame?
In pandas, missing values are denoted as NaN. You can find all missing values in a DataFrame by using the isna() function from the pandas library:
Common Data Scientist Interview Questions
6. What do you understand by unstructured data?
Unstructured data is a form that doesn’t explicitly have the structure, high degree, or organization. Examples of unstructured data include images, audio, and language text.
7. Write the code to load a Latin1 encoded dataset into the python environment.
8. How to see the first five rows of a data frame in Python?
9. Define data profiling
Data profiling refers to analyzing the attributes of data such as data type, frequency, length, discrete values, and value ranges.
10. How to check the class of each variable in a pandas DataFrame?
11. Write the code to see the dimensions of a data frame in Python.
12. Write the syntax to find the value counts of a variable.
13. Write the code for performing Pandas Profiling in python.
14. What do you understand by data mining?
Data mining is used to perform functions such as identifying unusual records, analyzing data clusters, and sequence discovery.
15. Explain the describe function in python.
The describe() function gives the mean, standard deviation, and Inter Quartile Range (IQR) values.
To know more about Data Scientist interviews, watch Neha’s story here. In this video, Neha, Data Scientist at Applied Materials shares her Data Science interview experience, what questions were asked, and how she answered them.
PinWe hope you found this article useful. Also, check out our blogs on Python and SQL questions for Data Scientist interview preparation.
Do you know Data Scientists spend approximately 40 to 50% of their time cleaning data. Well, it’s only fair. Because quality data is central to good data analysis, building machine learning algorithms, and consequently producing quality results.
So, exactly what is data cleaning?
Data cleaning refers to removing and updating duplicate, incomplete, or inconsistent data. It is important to clean data as bad quality data hampers decision-making. To leverage data for profitability, companies look for Data Scientists with excellent data cleaning skills.
So, if you are interviewing for a Data Scientist role, you are likely to be asked questions on data cleaning. To help you prepare, we’ve curated a list of top data cleaning questions with answers which will help you crack the Data Scientist interview. Along with data cleaning, the list also covers some commonly asked data-related questions.
Let’s get started.
Data Cleaning Questions for Data Scientist Interview
1. List the best practices for cleaning data
The best practices for data cleaning include:
- Removing unwanted and duplicate data
- Fixing structural errors such as typos, inconsistent capitalization, and more
- Handling the missing values and data
- Filtering outliers to avoid misleading results
2. How to remove duplicate observations from a data frame in python?
3. How are null values stored in pandas data frames?
4. How to drop variables from pandas DataFrame?
5. How are missing values denoted in pandas, and which function is used to find missing values in a pandas DataFrame?
In pandas, missing values are denoted as NaN. You can find all missing values in a DataFrame by using the isna() function from the pandas library:
Common Data Scientist Interview Questions
6. What do you understand by unstructured data?
Unstructured data is a form that doesn’t explicitly have the structure, high degree, or organization. Examples of unstructured data include images, audio, and language text.
7. Write the code to load a Latin1 encoded dataset into the python environment.
8. How to see the first five rows of a data frame in Python?
9. Define data profiling
Data profiling refers to analyzing the attributes of data such as data type, frequency, length, discrete values, and value ranges.
10. How to check the class of each variable in a pandas DataFrame?
11. Write the code to see the dimensions of a data frame in Python.
12. Write the syntax to find the value counts of a variable.
13. Write the code for performing Pandas Profiling in python.
14. What do you understand by data mining?
Data mining is used to perform functions such as identifying unusual records, analyzing data clusters, and sequence discovery.
15. Explain the describe function in python.
The describe() function gives the mean, standard deviation, and Inter Quartile Range (IQR) values.
To know more about Data Scientist interviews, watch Neha’s story here. In this video, Neha, Data Scientist at Applied Materials shares her Data Science interview experience, what questions were asked, and how she answered them.
PinWe hope you found this article useful. Also, check out our blogs on Python and SQL questions for Data Scientist interview preparation.
