Data Analyst Interview Questions And Answers | Data Analytics Interview Questions | Simplilearn
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Data mining involves finding new information from raw data, while data wrangling focuses on cleaning and structuring data for analysis. Common issues for data analysts include handling missing values and ensuring data security, with steps in an analytics project including problem understanding, data collection, and analysis.
Insights
- Data wrangling, which involves cleaning and structuring raw data, is a critical step in data analytics, consuming around 80% of the analytics process.
- Best practices in data cleaning include making a detailed plan, eliminating duplicates, ensuring accuracy, and standardizing data entry, all crucial for effective decision-making and analysis.
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Recent questions
What is data mining?
Data mining is the process of discovering new relevant information from raw data by analyzing large datasets to identify patterns, trends, and relationships that can provide valuable insights for decision-making.
What are common problems for data analysts?
Common problems for data analysts include managing duplicate and missing values, ensuring data security, and addressing compliance issues to maintain data integrity and accuracy throughout the analytics process.
How can missing values be handled in data analysis?
Missing values in data analysis can be managed through techniques such as list-wise deletion, average imputation, regression substitution, and multiple imputation to maintain data completeness and accuracy for effective analysis and interpretation.
What is exploratory data analysis?
Exploratory data analysis is a crucial step in understanding data better, refining feature variables, and uncovering hidden trends by visualizing and summarizing data to gain insights and inform further analysis and decision-making processes.
What is the purpose of hypothesis testing?
Hypothesis testing involves formulating null and alternative hypotheses to evaluate and make decisions based on statistical evidence, determining whether to accept or reject the null hypothesis to draw meaningful conclusions from data analysis.
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Summary
00:00
"Data Analysis Essentials: Techniques and Tools"
- Data mining is the process of finding new relevant information from raw data, while data profiling assesses data sets for uniqueness, consistency, and logic.
- Data wrangling involves cleaning, structuring, and enriching raw data for better decision-making, with 80% of data analytics focusing on this step.
- Common problems for data analysts include handling duplicate and missing values, ensuring data security, and dealing with compliance issues.
- Steps in an analytics project include understanding the problem, data collection, cleaning, exploration, analysis, and interpreting results.
- Technical tools for analysis and presentation purposes include SQL Server, MySQL, Excel, SPSS, Tableau, and Python.
- Best practices for data cleaning involve making a plan, removing duplicates, focusing on accuracy, and standardizing data entry.
- Handling missing values can be done through list-wise deletion, average imputation, regression substitution, and multiple imputation.
- Normal distribution is a symmetric continuous probability distribution with mean, median, and mode at the center, with data falling within standard deviations.
- Time series analysis deals with ordered values at equally spaced time intervals, crucial for predicting future trends.
- Joining in Tableau combines data from the same source, while blending combines data from different sources, each with its own dimensions and measures.
16:41
"Data Frame Creation and Analysis Techniques"
- Reshaping data in pandas involves creating a data frame with two rows and five values in each row.
- Data frames in pandas can be created by initializing a list or from a dictionary.
- To create a data frame in Python, import the pandas library and use the read_csv function to load a CSV file.
- Selecting specific columns from a data frame in Python involves referencing the columns within brackets.
- A good data model should be intuitive, adaptable to changes, and easily consumed by clients for profitable results.
- Exploratory data analysis is crucial for understanding data better, refining feature variables, and discovering hidden trends.
- Outliers in a data set can be treated by dropping, capping, assigning new values, or transforming them.
- Descriptive analytics looks at past data, predictive analytics predicts the future, and prescriptive analytics suggests actions to take.
- Sampling techniques in data analysis include simple random, systematic, cluster, stratified, and judgmental sampling.
- Hypothesis testing involves null and alternative hypotheses to accept or reject statistical hypotheses.
34:08
Data Analysis Techniques in Tableau, Python, SQL
- Synchronize the right axis by right-clicking on the profit axis and change some cells to bar and sum profit to line under the marks card.
- Design a view in Tableau to show statewide sales and profits using the sample superstore dataset by dragging the country field onto the view section and expanding it to see the states.
- Extract the value 8 using 2D indexing from an array in Python using numpy, with the array being broken up into three groups.
- Create an array using np.arange(10) to display values 1, 3, 5, 7, 9 by filtering for values with a remainder of 1 when divided by 2.
- Stack arrays a and b horizontally in numpy using np.concatenate(a, b, axis=1) or hstack to concatenate them on axis 1.
- Add an address column to a pandas data frame by assigning values to the address column after creating the data frame.
- Create a pivot table in Tableau to find the total sales made by each sales representative for each item and display cells as a percentage of the grand total.
- Write an SQL query to find products with total units sold greater than 1.5 million by selecting specific columns and using group by and having clauses.
- Create a stored procedure in SQL to find the sum of squares of the first n natural numbers by declaring variables, setting the sum formula, and executing the procedure for a given value of n.
- Find the total even numbers between two user-given numbers by creating a procedure with variables for the range, counting even numbers, and printing the results.
