𝟯𝟬 𝗠𝗼𝘀𝘁 𝗖𝗼𝗺𝗺𝗼𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗬𝗼𝘂 𝗠𝘂𝘀𝘁 𝗞𝗻𝗼𝘄!😍

Are you preparing for a Data Analytics interview?🗣

Hiring managers often ask a mix of technical & problem-solving questions to evaluate your skills in SQL, Python, Excel, data visualization, & case studies🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4hbmjxf

Which question do you find the toughest? Drop a comment below!⬇️

𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:

df = pd.read_csv('your_dataset.csv')

𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:

1- View the first few rows:
df.head()

2- Summary of the dataset:
df.info()

3- Statistical summary:
df.describe()

𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:

1- Identify missing values:
df.isnull().sum()

2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()

𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:

1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()

2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()

3- Pair plots:
sns.pairplot(df)
plt.show()

4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()

𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:

plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()

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

Like for more ❤️

ENJOY LEARNING 👍👍

𝗧𝗼𝗽 𝟱 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝗧𝗼𝗱𝗮𝘆!😍

In today’s fast-paced tech industry, staying ahead requires continuous learning and upskilling✨️

Fortunately, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 is offering 𝗳𝗿𝗲𝗲 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 that can help beginners and professionals enhance their 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝗻 𝗱𝗮𝘁𝗮, 𝗔𝗜, 𝗦𝗤𝗟, 𝗮𝗻𝗱 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 without spending a dime!⬇️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3DwqJRt

Start a career in tech, boost your resume, or improve your data skills✅️

Python #Pandas Cheat Sheet 🐼

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝗘𝘅𝗰𝗲𝗹 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟳 𝗱𝗮𝘆𝘀?

📊 Here's a structured roadmap to help you go from beginner to pro in a week!

Whether you're learning formulas, functions, or data visualization, this guide covers everything step by step.

𝐋𝐢𝐧𝐤👇 :-

https://pdlink.in/43lzybE

All The Best 💥

Reverse a list in Python

𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊🚀

Want to master data analytics? Here are top free courses, books, and certifications to help you get started with Power BI, Tableau, Python, and Excel.

𝐋𝐢𝐧𝐤👇
https://pdlink.in/41Fx3PW

All The Best 💥

𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐏𝐫𝐞𝐩:

Must practise the following questions for your next Python interview:

1. How would you handle missing values in a dataset?

2. Write a python code to merge datasets based on a common column.

3. How would you analyse the distribution of a continuous variable in dataset?

4. Write a python code to pivot an dataframe.

5. How would you handle categorical variables with many levels?

6. Write a python code to calculate the accuracy, precision, and recall of a classification model?

7. How would you handle errors when working with large datasets?

I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340

Hope you'll like it

Like this post if you need more resources like this 👍❤️

Data Analysis with Python: Zero to Pandas

Data Analysis with Python: Zero to Pandas" is a practical and beginner-friendly introduction to data analysis covering the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis.

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https://jovian.ai/learn/data-analysis-with-python-zero-to-pandas

𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍

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𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4bQ6FpS

These resources will help you gain in-demand skills & boost your career in 2025!💫

Practice projects to consider:

1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.

2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.

3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.

4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘? 𝗛𝗲𝗿𝗲’𝘀 𝗛𝗼𝘄!😍

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𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4iIxCy8

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