Welcome to our guide on preparing for data analytics interviews at TCS! In today’s competitive job market, securing a position in data analytics requires more than just technical knowledge. With TCS being a leading player in the industry, it’s crucial to stand out during the interview process. In this blog, we’ll cover common interview questions, provide insightful answers, and equip you with the skills needed to ace your interview and land your dream job in data analytics at TCS. Let’s dive in!
Table of Contents
Questions based on Power BI
Question: What is Power BI, and how does it differ from Excel?
Answer: Power BI is a business analytics tool by Microsoft used for visualizing and analyzing data. Unlike Excel, Power BI is specifically designed for creating interactive reports, dashboards, and data visualizations from multiple data sources. It offers more advanced data modeling and visualization capabilities compared to Excel.
Question: Can you explain the Power BI architecture?
Answer: The Power BI architecture consists of various components such as Power BI Desktop (for report authoring), Power BI Service (for sharing and collaborating on reports), Data Gateway (for connecting on-premises data sources), and underlying data storage services like Azure SQL Database or Azure Analysis Services.
Question: How do you import data into Power BI?
Answer: Data can be imported into Power BI using different methods including importing data from files (Excel, CSV), connecting to databases (SQL Server, MySQL), using web services (REST APIs), or using direct query options to connect live to data sources.
Question: What are the different types of visualizations available in Power BI?
Answer: Power BI offers various visualization types such as bar charts, line charts, pie charts, scatter plots, maps, tables, matrices, and custom visuals obtained from AppSource.
Question: How do you optimize Power BI reports for performance?
Answer: Performance optimization in Power BI involves techniques like optimizing data model relationships, minimizing calculated columns and measures, reducing the number of visuals per report page, using appropriate data types, and optimizing data refresh schedules.
Question: Can you explain DAX (Data Analysis Expressions) and its significance in Power BI?
Answer: DAX is a formula language used to define calculated columns, calculated tables, and measures in Power BI. It is vital for creating complex calculations, aggregations, and manipulating data within Power BI reports and data models.
Question: How do you handle security in Power BI?
Answer: Security in Power BI can be managed through various methods including row-level security (RLS) to restrict data access based on user roles, implementing Azure Active Directory (AAD) authentication, and using data encryption for sensitive information.
Question: What are the advantages of using Power BI over other BI tools?
Answer: Some advantages of Power BI include its seamless integration with other Microsoft products, easy-to-use interface for both business users and developers, rich visualization capabilities, ability to handle large datasets, and robust community support.
Python skills questions
Question: Explain the differences between Python 2 and Python 3.
Answer: Python 2 is an older version of the language that is no longer actively developed or supported as of January 1, 2020. Python 3 is the current version, with improvements such as better Unicode support, cleaner syntax, and backward-incompatible changes from Python 2.
Question: What are the different data types in Python?
Answer: Python supports various data types including integers, floats, strings, lists, tuples, dictionaries, sets, and booleans.
Question: How do you handle exceptions in Python?
Answer: Exceptions in Python are handled using try-except blocks. The code that might raise an exception is placed inside the try block, and the exception handling code is placed inside the except block to handle any raised exceptions gracefully.
Question: What is the difference between ‘==’ and ‘is’ operators in Python?
Answer: The ‘==’ operator is used to compare the values of two objects, while the ‘is’ operator is used to compare the identities of two objects, checking if they refer to the same object in memory.
Question: Explain list comprehensions in Python.
Answer: List comprehensions provide a concise way to create lists in Python by iterating over an iterable and applying an expression to each element, optionally filtering elements based on a condition.
Question: What is the difference between a function and a method in Python?
Answer: A function is a block of code that performs a specific task and can be called independently, while a method is a function that belongs to a class and is called on an instance of that class.
Question: How do you open and read a file in Python?
Answer: Files in Python are opened using the open() function, and their contents can be read using methods such as read(), readline(), or by iterating over the file object directly.
Question: Explain the use of virtual environments in Python development.
Answer: Virtual environments are used to create isolated environments for Python projects, allowing for the installation of project-specific dependencies without affecting the system-wide Python installation. This helps manage dependencies and avoid conflicts between different projects.
Questions based on statistics
Question: What is the Central Limit Theorem, and why is it important?
Answer: The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. It’s important because it allows us to make inferences about population parameters based on sample statistics.
Question: Differentiate between descriptive and inferential statistics.
Answer: Descriptive statistics involve summarizing and describing the features of a dataset, such as mean, median, mode, standard deviation, etc. Inferential statistics, on the other hand, involve making inferences or predictions about a population based on a sample from that population.
Question: What is hypothesis testing, and explain the steps involved in hypothesis testing.
Answer: Hypothesis testing is a statistical method used to make inferences about a population parameter based on sample data. The steps involved include:
- Formulating the null hypothesis (H0) and alternative hypothesis (Ha).
- Choosing a significance level (alpha).
- Collecting sample data and calculating a test statistic.
- Comparing the test statistic to a critical value or calculating a p-value.
- Deciding to either reject or fail to reject the null hypothesis based on the comparison.
Question: What are Type I and Type II errors?
Type I error occurs when the null hypothesis is incorrectly rejected when it is true. Type II error occurs when the null hypothesis is incorrectly not rejected when it is false.
Question: Explain the concept of correlation.
Answer: Correlation measures the strength and direction of the linear relationship between two variables. It is represented by a correlation coefficient, which ranges from -1 to 1. A correlation coefficient of 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.
Question: What is regression analysis, and how is it used?
Answer: Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is used for predicting the value of the dependent variable based on the values of the independent variables.
Question: What is the difference between correlation and causation?
Answer: Correlation refers to a statistical relationship between two variables, where changes in one variable are associated with changes in another variable. Causation, on the other hand, implies that changes in one variable directly cause changes in another variable.
Question: Explain the concept of p-value in hypothesis testing.
Answer: The p-value is the probability of observing a test statistic as extreme as or more extreme than the one observed, assuming that the null hypothesis is true. It indicates the strength of evidence against the null hypothesis. A smaller p-value suggests stronger evidence against the null hypothesis.
SQL basics and Python questions related to data dictionary
Question: Differentiate between SQL’s DDL, DML, and DCL.
Answer: DDL (Data Definition Language) is used for defining database schema and structure (e.g., CREATE, ALTER, DROP). DML (Data Manipulation Language) is used for querying and modifying data within tables (e.g., SELECT, INSERT, UPDATE, DELETE). DCL (Data Control Language) is used for managing access permissions and security (e.g., GRANT, REVOKE).
Question: What is the SQL joining process?
Answer: SQL joining is a process used to combine rows from two or more tables based on a related column between them. This process allows you to retrieve data from multiple tables in a single query. There are different types of joins in SQL, including:
- INNER JOIN: Returns only the rows that have matching values in both tables.
- LEFT JOIN (or LEFT OUTER JOIN): Returns all the rows from the left table and the matching rows from the right table. If there is no matching row in the right table, NULL values are used.
- RIGHT JOIN (or RIGHT OUTER JOIN): Returns all the rows from the right table and the matching rows from the left table. If there is no matching row in the left table, NULL values are used.
- FULL JOIN (or FULL OUTER JOIN): Returns all the rows when there is a match in either the left or right table. If there is no match, NULL values are used.
- CROSS JOIN: Returns the Cartesian product of the two tables, meaning it combines each row of the first table with each row of the second table.
- SELF JOIN: When a table is joined with itself based on a related column within that table.
Question: What is a primary key in SQL, and why is it important?
Answer: A primary key is a column or combination of columns that uniquely identifies each row in a table. It ensures data integrity by enforcing uniqueness and providing a reference for establishing relationships with other tables.
Question: Explain the difference between INNER JOIN and LEFT JOIN in SQL.
Answer: INNER JOIN returns only the rows where there is a match in both tables based on the specified join condition. LEFT JOIN returns all the rows from the left table and the matching rows from the right table. If there are no matching rows in the right table, NULL values are returned.
Question: How do you create an empty data dictionary in Python?
Answer: You can create an empty data dictionary using either the curly braces {} or the dict() constructor.
# Using curly braces
my_dict = {}
# Using dict() constructor
my_dict = dict()
Question: How do you add key-value pairs to a data dictionary?
Answer: You can add key-value pairs to a data dictionary by assigning values to the keys.
my_dict = {} my_dict[“key1”] = “value1” my_dict[“key2”] = “value2”
Question: How do you access values from a data dictionary in Python?
Answer: You can access values from a data dictionary by using the keys as indices.
my_dict = {“key1”: “value1”, “key2”: “value2”}
value1 = my_dict[“key1”]
value2 = my_dict[“key2”]
Question: How do you check if a key exists in a data dictionary?
Answer: You can check if a key exists in a data dictionary using the in keyword.
my_dict = {“key1”: “value1”, “key2”: “value2”}
if “key1” in my_dict:
print(“Key ‘key1’ exists!”)
Question: How do you iterate over key-value pairs in a data dictionary?
Answer: You can iterate over key-value pairs in a data dictionary using a for loop.
my_dict = {“key1”: “value1”, “key2”: “value2”}
for key, value in my_dict.items():
print(key, “->”, value)
Other Technical Questions
Question: What is R programming?
Answer: R programming is a statistical computing and graphics language widely used in data analysis and statistical modeling. It offers a rich ecosystem of packages for various statistical techniques, machine learning algorithms, and data visualization. Key points about R programming include:
- Statistical Computing: R provides a comprehensive set of tools for statistical analysis, including regression analysis, hypothesis testing, and data manipulation.
- Graphics and Visualization: It offers powerful graphical capabilities for creating informative and visually appealing plots and charts.
- Extensibility: R is highly extensible, allowing users to develop custom functions and packages to address specific analysis needs.
- Community and Support: It has a vibrant community of users and developers who contribute packages, share knowledge, and provide support through forums and online resources.
- Integration with Other Tools: R can be seamlessly integrated with other programming languages and tools, facilitating data import/export, collaboration, and workflow automation.
Question: Data analytical tools and technology?
Answer: Data analytical tools and technologies are diverse and cater to various aspects of data analysis, visualization, and interpretation. Here are some prominent ones
- SQL (Structured Query Language): Widely used for querying and manipulating data in relational databases.
- Microsoft Excel: Commonly used for data manipulation, analysis, and visualization, especially for smaller datasets.
- Python: A versatile programming language with libraries such as Pandas, NumPy, and SciPy for data manipulation, analysis, and machine learning.
- R: Specifically designed for statistical computing and graphics, popular for data analysis, and modeling.
- Tableau: A powerful data visualization tool that enables users to create interactive and shareable dashboards.
- Power BI: Microsoft’s business analytics tool for creating interactive reports and dashboards with data from various sources.
Question: Explain the functionality of linked list.
Answer: A linked list is a fundamental data structure that consists of a sequence of elements, called nodes, where each node contains both data and a reference (or link) to the next node in the sequence. The main functionality of a linked list includes:
- Dynamic Memory Allocation: Linked lists allow dynamic creation of nodes, facilitating memory allocation as needed.
- Insertion and Deletion: Efficient insertion and deletion operations are supported, with pointers adjusted to maintain connectivity.
- Traversal: Nodes can be traversed sequentially, enabling access, search, and processing of individual elements.
- Flexible Size: Linked lists can dynamically adjust in size, expanding or shrinking without resizing operations.
- Dynamic Data Structures: Serve as a foundation for implementing complex structures like stacks, queues, and doubly linked lists.
- Memory Efficiency: Efficient memory usage due to allocation only for necessary data and pointers per node.
Question: What is data mining?
Answer: Data mining involves extracting patterns and insights from large datasets using statistical analysis, machine learning, and AI techniques. Its main objectives include discovering hidden patterns, trends, and associations within data, as well as making predictions and decisions based on these findings. Data mining methods include classification, clustering, regression, association rule mining, and anomaly detection, with applications across various industries for improving decision-making and business processes.
Question: What are data profiling and data wrangling techniques
Answer:
- Data Profiling:
Data profiling is the process of examining and analyzing data to gain insights into its structure, quality, and completeness.
It involves assessing various characteristics of the data, such as data types, distributions, uniqueness, missing values, and outliers.
Data profiling helps in understanding the data’s potential challenges and opportunities and guides decisions on data preparation, cleansing, and analysis.
Techniques used in data profiling include summary statistics, frequency distributions, histograms, and data visualization.
- Data Wrangling:
Data wrangling, also known as data preparation or data munging, refers to the process of cleaning, transforming, and enriching raw data into a suitable format for analysis.
It involves tasks such as handling missing values, removing duplicates, standardizing data formats, and merging or joining datasets.
Data wrangling aims to ensure data quality, consistency, and relevance for analysis, enabling more accurate and meaningful insights.
Techniques used in data wrangling include data cleaning operations (e.g., imputation, deduplication), transformation functions (e.g., scaling, encoding), and merging and reshaping operations (e.g., join, pivot).
Question: What is logistic regression, linear regression, and time series
Answer:
- Logistic Regression:
Logistic regression models the relationship between independent variables and a binary outcome, predicting probabilities of categorical responses.
It utilizes the logistic function to map the linear combination of features to a probability between 0 and 1.
Widely employed in binary classification tasks across diverse fields like healthcare and marketing.
- Linear Regression:
Linear regression models the relationship between independent variables and a continuous outcome, assuming a linear association.
It aims to minimize the difference between observed and predicted values by fitting a best-fitting line.
Frequently used for prediction and inference in economics, social sciences, and engineering.
- Time Series:
Time series analysis deals with sequential data collected at regular intervals.
Its objective is to identify patterns, trends, and seasonality within the data and make predictions about future values.
Techniques include moving averages, exponential smoothing, and ARIMA models, applied in finance, meteorology, and signal processing.
Question: What are hlookup and vlookup in Excel?
Answer:
- HLOOKUP (Horizontal Lookup):
HLOOKUP is a function in Excel used to search for a value in the top row of a table or range and return a value in the same column from a specified row below.
It is particularly useful when data is organized horizontally, with categories listed in rows and corresponding values spread across columns.
Syntax: HLOOKUP(lookup_value, table_array, row_index_num, [range_lookup]).
- VLOOKUP (Vertical Lookup):
VLOOKUP is a function in Excel used to search for a value in the leftmost column of a table or range and return a value in the same row from a specified column.
It is commonly used when data is organized vertically, with categories listed in columns and corresponding values in rows.
Syntax: VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup]).
Other Questions
- What are array, number data types, and linear regression?
- Questions based on SQL, joins, aggregate, subquery, etc
- Python, join, self join, python pandas.
- What is the difference between delete and truncate?
- what is a data warehouse?
- What is azure data lake?
- What are power bi service features?
- Difference between Azure SQL and SQL server?
- What is Dax?
- Parameters in Power BI?
- SQL: Joins, window functions,
- Tell me about data requirement gathering.
- Python data analysis techniques?
- Explain the joins in SQL
General Questions
- Tell me about yourself what is your aim in life?
- Where do you see yourself in 5 years?
- Why we are hiring you.
- What do you know about TCS
Conclusion
Preparing for a data analytics interview at TCS demands a blend of technical proficiency, problem-solving finesse, and polished communication skills. By mastering fundamental concepts, gaining hands-on experience with analytical tools, and articulating your insights effectively, you can position yourself as a standout candidate in the competitive landscape of data analytics. Keep honing your skills, stay abreast of industry trends, and approach each interview question with confidence to maximize your chances of success at TCS and beyond.
