𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

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Most Important Python Topics for Data Analyst Interview:

#Basics of Python:

1. Data Types

2. Lists

3. Dictionaries

4. Control Structures:

- if-elif-else

- Loops

5. Functions

6. Practice basic FAQs questions, below mentioned are few examples:

- How to reverse a string in Python?

- How to find the largest/smallest number in a list?

- How to remove duplicates from a list?

- How to count the occurrences of each element in a list?

- How to check if a string is a palindrome?

#Pandas:

1. Pandas Data Structures (Series, DataFrame)

2. Creating and Manipulating DataFrames

3. Filtering and Selecting Data

4. Grouping and Aggregating Data

5. Handling Missing Values

6. Merging and Joining DataFrames

7. Adding and Removing Columns

8. Exploratory Data Analysis (EDA):

- Denoscriptive Statistics

- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)

- Correlation and Covariance

- Handling Duplicates

- Data Transformation

#Numpy:

1. NumPy Arrays

2. Array Operations:

- Creating Arrays

- Slicing and Indexing

- Arithmetic Operations

#Integration with Other Libraries:

1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)

#Key Concepts to Revise:

1. Data Manipulation with Pandas and NumPy

2. Data Cleaning Techniques

3. File Handling (reading and writing CSV files, JSON files)

4. Handling Missing and Duplicate Values

5. Data Transformation (scaling, normalization)

6. Data Aggregation and Group Operations

7. Combining and Merging Datasets

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝟭𝟬𝟬% 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗔𝘇𝘂𝗿𝗲, 𝗔𝗜, 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗠𝗼𝗿𝗲😍

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Here are some most popular Python libraries for data visualization:

Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.

Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.

Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.

Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.

Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.

For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.

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𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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Excel vs SQL vs Python (pandas):

1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]

2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")

3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)

4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()

5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")

6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)

7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()

8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()

𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱 — 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗠𝗡𝗖𝘀😍

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Here is a powerful 𝗜𝗡𝗧𝗘𝗥𝗩𝗜𝗘𝗪 𝗧𝗜𝗣 to help you land a job!

Most people who are skilled enough would be able to clear technical rounds with ease.

But when it comes to 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹/𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗳𝗶𝘁 rounds, some folks may falter and lose the potential offer.

Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).

One needs to clear this round to reach the salary negotiation round.

Here are some tips to clear such rounds:

1️⃣ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.

2️⃣ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.

3️⃣ This shows that you have done good research and also helps strike a personal connection.

4️⃣ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.

5️⃣ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.

💡 𝗕𝗼𝗻𝘂𝘀 𝘁𝗶𝗽 - Be polite yet assertive in such interviews. It impresses a lot of senior folks.

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗦𝘁𝗮𝗿𝘁 𝗪𝗶𝘁𝗵😍

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Deloitte Recent Data Analyst Interview Questions Part-1

Deloitte Recent Data Analyst Interview Questions Part-2

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

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𝐖𝐡𝐲 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 & 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐒𝐡𝐨𝐮𝐥𝐝 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐚𝐧𝐝𝐚𝐬

When it comes to data analysis and machine learning, Pandas is non-negotiable. It’s the 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧, turning messy datasets into meaningful insights — and that’s exactly what makes it a 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐞𝐫 in real-world projects.

Recently, I explored an in-depth guide on 𝐏𝐚𝐧𝐝𝐚𝐬 𝐟𝐫𝐨𝐦 𝐁𝐚𝐬𝐢𝐜𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝, and here’s what stood out:-

- Use len() to analyze string data (e.g., name lengths in the Titanic dataset).
- Create pivot tables for grouped insights (like finding top batting averages per team).
- Simplify categories (e.g., replacing “male”/“female” with “M”/“F”).
- Merge and join datasets seamlessly, even with missing values.

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐲 𝐏𝐚𝐧𝐝𝐚𝐬 𝐢𝐬 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 & 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞:

- 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠:- Handle missing values, duplicates, and inconsistent formats.
- 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐨𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐄𝐃𝐀):- Quickly summarize patterns and anomalies.
- 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠:- Create meaningful features to improve model performance.
- 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧:- Combine multiple data sources with ease.
- 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐒𝐮𝐩𝐩𝐨𝐫𝐭:- Ideal for forecasting and trend analysis.

In short — 𝐏𝐚𝐧𝐝𝐚𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 𝐫𝐚𝐰 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬.

If you’re learning Python for ML or analytics, make Pandas your priority.

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Essential Pandas Functions for Data Analysis

Data Loading:

pd.read_csv() - Load data from a CSV file.

pd.read_excel() - Load data from an Excel file.


Data Inspection:

df.head(n) - View the first n rows.

df.info() - Get a summary of the dataset.

df.describe() - Generate summary statistics.


Data Manipulation:

df.drop(columns=['col1', 'col2']) - Remove specific columns.

df.rename(columns={'old_name': 'new_name'}) - Rename columns.

df['col'] = df['col'].apply(func) - Apply a function to a column.


Filtering and Sorting:

df[df['col'] > value] - Filter rows based on a condition.

df.sort_values(by='col', ascending=True) - Sort rows by a column.


Aggregation:

df.groupby('col').sum() - Group data and compute the sum.

df['col'].value_counts() - Count unique values in a column.


Merging and Joining:

pd.merge(df1, df2, on='key') - Merge two DataFrames.

pd.concat([df1, df2]) - Concatenate

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Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:

1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.

2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.

4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.

5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.

6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.

7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.

8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.

9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.

10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.

By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.

𝟱 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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