𝐇𝐨𝐰 𝐭𝐨 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐝𝐚𝐭𝐚 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐚𝐬 𝐚𝐧 𝐚𝐬𝐩𝐢𝐫𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐭?

Here's a step-by-step guide for the same:

Step 1️⃣ - Download a practice dataset. I'd recommend the Codebasics resume project challenge dataset (as it contains multi-table datasets).

Step 2️⃣ - Open your preferred RDBMS tool (SQL server/MySQL). Create a local database to load the dataset.

Step 3️⃣ - Import the practice dataset (.xlsx/.csv) into this database by creating the tables (please google if you need help).

Step 4️⃣ - Now open Power BI desktop and connect to the local database using the appropriate connector.

Step 5️⃣ - Build the dashboard using the questions shared in the resume project challenge.

Step 6️⃣ - Now, you can validate the output of your dashboard by writing SQL queries.

Step 7️⃣ - Try to write an SQL query for a question asked in the challenge. You need to convert a natural language question into an SQL query.

Step 8️⃣ - Compare the query output with the dashboard output and check if the numbers are matching. If they aren't matching, either the query is wrong or the dashboard numbers are wrong. Hence, try to identify the gap.

Step 9️⃣ - Repeat the process for every question asked in the challenge.

Thus, you will learn and practice both SQL and Power BI simultaneously.

𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐭𝐫𝐲 𝐭𝐡𝐢𝐬 𝐦𝐞𝐭𝐡𝐨𝐝?

In real-world scenarios, 𝐝𝐚𝐭𝐚 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 is a very important step in every analytics project. One needs to compare the output of the report/dashboard with the data source and then launch it for usage, to avoid discrepancies.

This will help you weed out any mistakes that you have applied in your report/dashboard logic.

Best Telegram Channel for Data Analysts: https://news.1rj.ru/str/sqlspecialist

Q1: How do you ensure data consistency and integrity in a data warehousing environment?

Ans: I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.

Q2: Describe a situation where you had to design a star schema for a data warehousing project.

Ans: For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.

Q3: How would you use data analytics to assess credit risk for loan applicants?

Ans: I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.

Q4: Describe a situation where you had to ensure data security for sensitive financial data.

Ans: While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.

Q1: How would you analyze data to understand user connection patterns on a professional network? 

Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.

Q2: Describe a challenging data visualization you created to represent user engagement metrics. 

Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.

Q3: How would you identify and target passive job seekers on LinkedIn? 

Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.

Q4: How do you measure the effectiveness of a new feature launched on LinkedIn? 

Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.

Data Analyst Interview Questions 👇

1.How to create filters in Power BI?

Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.

Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)


2.How to sort data in Power BI?

Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.


3.How to convert pdf to excel?

Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.


4. How to enable macros in excel?

Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.

Q1: How would you handle real-time data streaming for analyzing user listening patterns?

Ans:  I'd use platforms like Apache Kafka for real-time data ingestion. Using Python, I'd process this stream to identify real-time patterns and store aggregated data for further analysis.

Q2: Describe a situation where you had to use time series analysis to forecast a trend. 

Ans:  I analyzed monthly active users to forecast future growth. Using Python's statsmodels, I applied ARIMA modeling to the time series data and provided a forecast for the next six months.

Q3: How would you segment and analyze user behavior based on their music preferences? 

Ans: I'd cluster users based on their listening history using unsupervised machine learning techniques like K-means clustering. This would help in creating personalized playlists or recommendations.

Q4: How do you handle missing or incomplete data in user listening logs? 

Ans: I'd use imputation methods based on the nature of the missing data. For instance, if a user's listening time is missing, I might impute it based on their average listening time or use collaborative filtering methods to estimate it based on similar users.

SQL (Structured Query Language) is a standard programming language used to manage and manipulate relational databases. Here are some key concepts to understand the basics of SQL:

1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns.

2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field.

3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database.

4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:
   - Create (INSERT): Adds new records to a table.
   - Read (SELECT): Retrieves data from one or more tables.
   - Update (UPDATE): Modifies existing records in a table.
   - Delete (DELETE): Removes records from a table.

5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal.

6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:
   - Primary Key: Uniquely identifies each record in a table.
   - Foreign Key: Establishes a relationship between two tables.
   - Unique: Ensures that all values in a column are unique.
   - Not Null: Specifies that a column cannot contain NULL values.

7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).

8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.

9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data.

10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order.

Understanding these basic concepts of SQL will help you write queries to interact with databases effectively. Practice writing SQL queries and experimenting with different commands to become proficient in using SQL for database management and manipulation.