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!😍

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Reverse a list in Python

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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.

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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 👍❤️

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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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Data Analyst INTERVIEW QUESTIONS AND ANSWERS
👇👇

1.Can you name the wildcards in Excel?

Ans: There are 3 wildcards in Excel that can ve used in formulas.

Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.

Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.

Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.

Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.


2.What is cascading filter in tableau?

Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.


3.What is the difference between .twb and .twbx extension?

Ans:
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.


4.What are the various Power BI versions?

Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subnoscription while workspaces are at Premium capacity.

ENJOY LEARNING 👍👍

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀!😍

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Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:

• Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.

• Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.

• Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.

But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.

No matter where your path leads, the key is to start now.

𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀!😍

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Showcase your SQL skills with real-world projects! 📊

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