Infosys Data Analyst Interview Questions and Answers

Q1. SELF INTRODUCTION
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Q2. WHAT IS YOUR METHODS OF PYTON
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Python is a versatile programming language favored for data analysis due to its libraries and ease of use.
• Pandas: A powerful library for data manipulation and analysis, allowing for easy handling of structured data. Example: Using DataFrames for data cleaning.

• NumPy: Essential for numerical computations, providing support for arrays and matrices. Example: Performing mathematical operations on large datasets efficiently.

• ...read more
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Q3. WHAT IS DATA ANALYSIS
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Q4. WRITE SOME KEY SKILLS USUALLY REQUIRED FOR A DATA ANAYST
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Q5. WHAT IS THE DATA ANALYSIS PROCESS
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The data analysis process involves collecting, cleaning, analyzing, and interpreting data to derive insights and inform decisions.
• Data Collection: Gathering relevant data from various sources, such as surveys, databases, or APIs. For example, collecting sales data from a CRM system.

• Data Cleaning: Removing inaccuracies and inconsistencies in the data to ensure quality. This may involve handling missing values or correct...read more
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Q6. WHAT ARE THE DIFFERENT CHALLENGES ONE FACES DURING DATA ANALYSIS
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Q7. EXPLAIN DATA CLEANING
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Q8. WHAT ARE TOOLS USEFUL OF DATA ANALYSIS
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Data analysis tools help in collecting, processing, and visualizing data to derive insights and support decision-making.
• Excel: Widely used for data manipulation and basic analysis, offering functions, pivot tables, and charting capabilities.

• Python: A programming language with libraries like Pandas and NumPy for data manipulation, and Matplotlib and Seaborn for visualization.

• R: A statistical programming language ideal f...read more
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Q9. EXPLAIN OUTLIER
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An outlier is a data point that differs significantly from other observations, potentially indicating variability or error.
• Definition: Outliers are extreme values that lie outside the overall pattern of distribution in a dataset.

• Causes: They can result from measurement errors, data entry mistakes, or genuine variability in the data.

• Impact: Outliers can skew statistical analyses, affecting mean, standard deviation, and ...read more
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Q10. EXPLAIN NORMAL DISTRIBUTION
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Normal distribution is a probability distribution that is symmetric about the mean, depicting data that clusters around the mean.
• Bell-Shaped Curve: The graph of a normal distribution is bell-shaped, indicating that most observations cluster around the central peak.

• Mean, Median, Mode: In a normal distribution, the mean, median, and mode are all equal and located at the center of the distribution.

• 68-95-99.7 Rule: Approxi...read more
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Q11. HOW DOES THE DATA VISUALIZATION
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Data visualization transforms complex data into visual formats, making it easier to understand patterns, trends, and insights.
• Graphical Representation: Data visualization uses charts, graphs, and maps to represent data visually, such as bar charts for sales data.

• Pattern Recognition: Visual formats help identify trends and patterns quickly, like spotting seasonal sales spikes in line graphs.

• Interactive Dashboards: Tools...read more
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Q12. EXPLAIN A HASH TABLE
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A hash table is a data structure that stores key-value pairs for efficient data retrieval using a hash function.
• Key-Value Storage: Hash tables store data in pairs, where each key is unique and maps to a specific value, e.g., {'name': 'Alice', 'age': 30}.

• Hash Function: A hash function converts keys into hash codes, which determine the index in the array where the value is stored.

• Collision Resolution: When two keys hash ...read more
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Online aptitude test with maths and statistics and data analysis

Q1. Joins sql groups python numpy pandas
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Q1. What are the methods and best practices for cleaning up data?
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Methods and best practices for cleaning up data include removing duplicates, handling missing values, standardizing formats, and validating data accuracy.
• Remove duplicates by identifying and deleting identical records.

• Handle missing values by imputing with mean, median, or mode.

• Standardize formats by converting data into a consistent structure.

• Validate data accuracy by cross-referencing with external sources or using d...read more
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Q2. What are the best practices for optimizing SQL code?
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Best practices for optimizing SQL code
• Use indexes on columns frequently used in WHERE clauses

• Avoid using SELECT * and only retrieve necessary columns

• Optimize joins by using INNER JOIN instead of OUTER JOIN when possible

• Avoid using subqueries and instead use JOINs or CTEs

• Regularly analyze query performance using EXPLAIN or query execution plans
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Q1. What are Lod commands and use?
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Lod commands are used in data analysis to manage and manipulate data efficiently.
• Lod stands for Level of Detail, allowing for calculations at different granularities.

• Example: Using Lod to calculate average sales per region while displaying data at the city level.

• Common Lod commands include FIXED, INCLUDE, and EXCLUDE.

• FIXED: Calculates values using specified dimensions regardless of the view.

• INCLUDE: Adds dimensions to ...read more
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Q2. Sql statement execution?
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SQL statement execution involves parsing, optimizing, and executing queries to retrieve or manipulate data in a database.
• 1. Parsing: SQL statements are parsed to check for syntax errors. Example: 'SELECT * FROM table' is valid, while 'SELEC * FROM table' is not.

• 2. Optimization: The SQL engine optimizes the query for performance. Example: Choosing the best index to speed up 'SELECT' queries.

• 3. Execution: The optimized q...read more
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