𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 V/S 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (𝐁𝐀):

- Acts as a bridge between the business side and the IT side of an organization.
- Gathers and analyzes business requirements.
- Conducts stakeholder meetings.

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐁𝐈):

- Focuses on data analysis, reporting, and data visualization using BI tools.
- Extracts and transforms data from various sources into meaningful insights to support decision-making.
- Builds dashboards and reports.
- Identifies trends and patterns in data.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞:

𝐀𝐦𝐚𝐳𝐨𝐧: A BA might analyze customer feedback to improve delivery processes, while a BI professional could create dashboards to monitor sales trends and warehouse efficiency.

𝐆𝐨𝐨𝐠𝐥𝐞: A BA could work on improving user experience based on app usage data, whereas a BI expert might analyze advertising data to optimize ad campaigns.

Data Analyst vs Business Analyst

Python Interview Questions for Data/Business Analysts in MNC:

Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?

Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.

Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?

Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.

Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.

Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.

Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?

Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?

Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.

Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?

Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?

Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?

Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.

Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?

Question 15:
In a dataset, you observe that some numerical columns are highly skewed. How can you normalize or transform these columns using Python?

Python Interview Q&A: https://topmate.io/coding/898340

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Nyka Business Analyst Interview Question approach

Step 1: Market Analysis-
The objective here is to assess the market size and growth potential in both regions. We would start by gathering data from market research reports to understand current market sizes and growth forecasts. Additionally, we'll analyze prevailing trends, such as the increasing demand for organic products or shifts towards online shopping, using visual aids like graphs and maps to highlight these markets.

Step 2: Competitive Landscape-
We would identify the main competitors in these markets and evaluate Nykaa’s market positioning relative to them. This involves listing major beauty retailers in both regions and using pie charts to display market shares, alongside visual representations of competitor logos.

Step 3: Product Comparison-
Here, our focus would shift to comparing Nykaa’s product offerings with those of existing competitors. We'd discuss the range and exclusivity of our products and highlight our unique selling propositions. Side-by-side images or checklists would be used as visual aids to make these comparisons clear.

Step 4: Financial Analysis-
This step involves estimating the financial implications of market entry. We'd outline the costs associated with setup, marketing, and operations, and project potential earnings to discuss the return on investment. Simple bar charts would be useful here to visually compare initial costs against potential revenues.

Step 5: Regulatory and Logistical Review-
We'd examine the regulatory and logistical challenges expected in these regions. This includes discussing major legal hurdles and supply chain and distribution challenges, using icons or brief clips to represent regulatory bodies and logistics like trucks and warehouses.

Step 6: Final Recommendation-
Based on our comprehensive analysis, I would conclude with a strategic decision, recommending whether Nykaa should expand into Western Europe or Southeast Asia. This decision would be backed by a summary of key reasons drawn from our analysis, and I'd use a balance scale graphic to visually present the pros and cons that led to our final decision.

Top 5 Excel Mistakes to Avoid as a Business Analyst🤫🤔?
⚠️ Avoid These Common Excel Mistakes
1️⃣ Ignoring Data Cleaning: Always clean your data before analysis.
2️⃣ Using Hard-Coded Values: Use cell references, not hard-coded numbers in formulas.
3️⃣ Overcomplicating Formulas: Keep it simple to avoid errors and confusion.
4️⃣ Misusing Pivot Tables: Don’t forget to check the data source and formatting.
5️⃣ Lack of Documentation: Always document your analysis process for clarity.

#BusinessAnalyst

Business Analyst Interview Questions and Answers
👇👇

1. What is analysis in tableau?

Ans: Tableau comes with inbuilt features to analyze the data plotted on a chart. We have various tools such as adding an average line to the chart which tableau calculates itself after we drop the tool on the chart. Some other features include clustering, percentages, forming bands of a particular range and various other tools to explore and inspect data. All these tools are available in analyze tab on each sheet used to create any chart. The features become visible only when they are applicable to the worksheet.


2.How to create sets in tableau?

Ans: Sets are custom fields used to compare and ask questions about a subset of data. For creating a set on dimension, right-click on a dimension in data pane and select create -> set. In general tab select the fields that will be considered for computing the set. Specify the conditions to create set in conditions tab and you also have the option to select top N members in dataset based on any field in the top tab. When a set is created it divides the measure into two parts namely in and out of the set based on the conditions applied by the user.


3.Why and how would you use a custom visual file?

A custom visual file is used when none of the pre existing visuals fit the business needs. Custom visual files are generally created by Developers which can be used in the same way as prepackaged files.


4. What are the various type of users who can use Power BI?

Ans: PowerBI can be used by anyone for their requirements but there is a particular group of users who are more likely to use it:

Report Consumers: They consume the reports based on a specific information they need

Report Analyst: Report Analysts need detailed data for their analysis from the reports

Self Service Data Analyst: They are more experienced business data users. They have an in-depth understanding of the data to work with.

Basic Data Analyst: They can build their own datasets and are experienced in PowerBI Service

Advanced Data Analyst: They know how to write SQL Queries and have hands-on experience on PowerBI. They have experience in Advanced PowerBI with DAX training and data modelling.

🚀Roadmap to Becoming a Data Analyst🚀

Start your journey with these key steps:-

1️⃣ SQL: Master querying and managing data from databases.
2️⃣ Python: Use Python for data manipulation and automation.
3️⃣ Visualization: Present data using Matplotlib/Seaborn.
4️⃣ Excel: Handle data and create quick insights.
5️⃣ Power BI/Tableau: Build interactive dashboards.
6️⃣ Statistics: Understand key concepts for data interpretation.
7️⃣ Data Analytics: Apply everything in real-world projects!

#DataAnalyst

Data Analyst vs Business Analyst ✅

FRND is hiring Business Analyst 🚀

Experience : 1 Year
Location : Bangalore

Apply link : Check out this job at FRND: https://www.linkedin.com/jobs/view/4156719102

20 Must-Know Statistics Questions for Data Analyst and Business Analyst Role:



1️⃣ What is the difference between denoscriptive and inferential statistics?
2️⃣ Explain mean, median, and mode and when to use each.
3️⃣ What is standard deviation, and why is it important?
4️⃣ Define correlation vs. causation with examples.
5️⃣ What is a p-value, and how do you interpret it?
6️⃣ Explain the concept of confidence intervals.
7️⃣ What are outliers, and how can you handle them?
8️⃣ When would you use a t-test vs. a z-test?
9️⃣ What is the Central Limit Theorem (CLT), and why is it important?
🔟 Explain the difference between population and sample.
1️⃣1️⃣ What is regression analysis, and what are its key assumptions?
1️⃣2️⃣ How do you calculate probability, and why does it matter in analytics?
1️⃣3️⃣ Explain the concept of Bayes’ Theorem with a practical example.
1️⃣4️⃣ What is an ANOVA test, and when should it be used?
1️⃣5️⃣ Define skewness and kurtosis in a dataset.
1️⃣6️⃣ What is the difference between parametric and non-parametric tests?
1️⃣7️⃣ What are Type I and Type II errors in hypothesis testing?
1️⃣8️⃣ How do you handle missing data in a dataset?
1️⃣9️⃣ What is A/B testing, and how do you analyze the results?
2️⃣0️⃣ What is a Chi-square test, and when is it used?

Python Interview Questions for Data/Business Analysts:

Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?

Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.

Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?

Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.

Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.

Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.

Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?

Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?

Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.

Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?

Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?

Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?

Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.

Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?

Python Interview Q&A: https://topmate.io/coding/898340

Like for more ❤️

ENJOY LEARNING 👍👍

Business Analyst → Bridge Between Strategy and Data
Aligns business goals with insights
Uses Excel, SQL, Tableau, and domain knowledge
Answers “Why did this happen?”

Example: Analyzing customer churn and recommending solutions.

Data Scientist → Predicts Future Trends
Uses machine learning and analytics
Works with Python, R, and AI models
Answers “What’s next?”

Example: Forecasting sales based on past data.

Gamecrio Studios is hiring Business Analyst 🚀

Qualification : Bachelor's degree
Experience : 0-2 Years
Location : Ahmedabad

Apply link : Check out this job at Gamecrio Studios Pvt Ltd.: https://www.linkedin.com/jobs/view/4162966294

Business Analytics vs Data Analytics

STEM is hiring Junior Business Analyst 🚀

Qualification : Bachelor's degree
Experience : Freshers / Experienced
Location : Gurugram

Apply link : https://job-boards.greenhouse.io/stemhealthcare/jobs/6600836?gh_src=67402a401us

👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5

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All the best 👍 👍

#jobs #internships

SQL Questions

Business Analyst vs Data Analyst

Majority of top companies hiring for analytic roles (Data Analyst/Business Analyst) focus heavily on SQL understanding as a selection criteria, which according to me, should be the first thing you start your preparation with.

I have divided this SQL roadmap into 3 steps (Basics, Level Up & Practice), and it should take around 1 month to complete.

Step 1 - Basics 🔢 :

➡What is a Relational Database / RDBMS?
➡SQL Data Types - Varchar, text, int, number, date, float, boolean.
➡SQL commands - select, where, like, distinct, between, group by, having, order by, insert into, case when, update, truncate, delete, commit, rollback (basically all the DDL, DML, DCL, TCL commands in SQL).
➡Integrity Constraints - Primary key, foreign key, not null, unique.
➡Operators arithmetic, logical, and comparison operations.
➡Use of distinct, order by, limit, and top.
➡Use of union and union all.
➡Joins in SQL inner, left, right, outer, self, full outer, cross join.


Step 2 - Level up ⬆⬆ :

➡Normalization in SQL
➡Aggregate, date, and string functions
➡Sub-Queries
➡CTE table / with clause
➡In-built SQL functions
➡Window functions
➡Views


Step 3 - Practice SQL Questions on leetcode & hackerrank ✅

Hope it helps :)

Microsoft Excel is used by 99% of the World’s businesses.

But the truth is most people don't know how to use it.

10 must-have Excel skills to accelerate your career:

1. Wildcards
2. XLookup
3. Sparklines
4. Remove duplicates
5. Flash Fill
6. Transpose
7. Trim
8. Pivot tables
9. Upper, lower, proper case
10. Stock market data

Most people suck at using Microsoft Excel.

I'm not talking about formatting data/reports or writing formulas.

I'm talking about using Excel to analyze data and make an impact.

Here are 7 ways to stand out from the crowd:

1) Don't make PivotTables your hammer and every problem a nail.

PivotTables are like any other data analysis technique.

They have pros and cons.

Tables are good primarily at two things:

Looking up exact values
Comparing exact values

This alone is not enough for most analyses.

2) Use more charts.

Humans are visual creatures, and we can use this to analyze data.

The best use of PivotTables is to create PivotCharts.

For example, bar charts that use three or more columns of data.

It's way more powerful than a PivotTable.

3) Use line charts.

I can't stress this one enough.

The single most valuable data visualization in business analytics is a line chart.

Line charts allow you to see:

Trends
Variability
Cycles
Rate of change
Exceptions

Oh, and make sure to use line charts in your dashboards!

4) Learn data analysis fundamentals.

Microsoft Excel can be a potent tool - if you know how to analyze data.

Here are two fundamentals that 99% of Excel users don't know:

Distribution analysis
Correlation analysis

While this sounds scary, it isn't.

No fancy math is required.

5) Time to step up to PowerQuery.

It's a crying shame PowerQuery isn't more popular.

It's exceedingly powerful (pun intended) and allows you to automate many steps in your data analyses.

In 2025, however, PowerQuery is more critical than ever because of the following three words.

6) Python in Excel

Shortly, there will be two kinds of Excel users:

Those who use Python in Excel to have an impact at work using DIY data science.

Those that do not.

BTW - If you're the first kind of Excel user, you can make the most of AI by...

7) Use Copilot in Excel with Python

I'm going to be honest.

Vanilla Copilot in Excel isn't very impressive.

However, using the Copilot AI to generate Python code for DIY data science is a different story.

But you must have DIY data science skills to use Copliot, or you're playing with 🔥.

Free Excel Resources: https://news.1rj.ru/str/excel_data