Python Cheatsheet 🚀🔥

📚👀🚀Preparing for a Data science/ Data Analytics interview can be challenging, but with the right strategy, you can enhance your chances of success. Here are some key tips to assist you in getting ready:

Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.

Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.

Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.

Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.

Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.

Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.

🧠👍By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!

Hope this helps 👍❤️:⁠-⁠)

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https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

📊 Data Analyst Roadmap (2025)

Master the Skills That Top Companies Are Hiring For!

📍 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting

📍 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions

📍 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling

📍 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression

📍 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis

📍 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting

📍 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights

📍 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders

📍 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community

📍 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements

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Python Cheatsheet

Chatgpt guide 👆

Here are some of the amazing Websites to Learn Python from Beginning to Advanced. 👇👇

1. LearnPython
🔗 Playlist Link

2. W3Schools
🔗 Playlist Link

3. Khan Academy
🔗 Playlist Link

4. FreeCodeCamp
🔗 Playlist Link

5. Sololearn
🔗 Playlist Link

How To Code in Python 3
by Lisa Tagliaferri


📄 459 pages

🔗 Book link

SQL Essential Concepts for Data Analyst Interviews ✅

1. SQL Syntax: Understand the basic structure of SQL queries, which typically include SELECT, FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses. Know how to write queries to retrieve data from databases.

2. SELECT Statement: Learn how to use the SELECT statement to fetch data from one or more tables. Understand how to specify columns, use aliases, and perform simple arithmetic operations within a query.

3. WHERE Clause: Use the WHERE clause to filter records based on specific conditions. Familiarize yourself with logical operators like =, >, <, >=, <=, <>, AND, OR, and NOT.

4. JOIN Operations: Master the different types of joins—INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN—to combine rows from two or more tables based on related columns.

5. GROUP BY and HAVING Clauses: Use the GROUP BY clause to group rows that have the same values in specified columns and aggregate data with functions like COUNT(), SUM(), AVG(), MAX(), and MIN(). The HAVING clause filters groups based on aggregate conditions.

6. ORDER BY Clause: Sort the result set of a query by one or more columns using the ORDER BY clause. Understand how to sort data in ascending (ASC) or descending (DESC) order.

7. Aggregate Functions: Be familiar with aggregate functions like COUNT(), SUM(), AVG(), MIN(), and MAX() to perform calculations on sets of rows, returning a single value.

8. DISTINCT Keyword: Use the DISTINCT keyword to remove duplicate records from the result set, ensuring that only unique records are returned.

9. LIMIT/OFFSET Clauses: Understand how to limit the number of rows returned by a query using LIMIT (or TOP in some SQL dialects) and how to paginate results with OFFSET.

10. Subqueries: Learn how to write subqueries, or nested queries, which are queries within another SQL query. Subqueries can be used in SELECT, WHERE, FROM, and HAVING clauses to provide more specific filtering or selection.

11. UNION and UNION ALL: Know the difference between UNION and UNION ALL. UNION combines the results of two queries and removes duplicates, while UNION ALL combines all results including duplicates.

12. IN, BETWEEN, and LIKE Operators: Use the IN operator to match any value in a list, the BETWEEN operator to filter within a range, and the LIKE operator for pattern matching with wildcards (%, _).

13. NULL Handling: Understand how to work with NULL values in SQL, including using IS NULL, IS NOT NULL, and handling nulls in calculations and joins.

14. CASE Statements: Use the CASE statement to implement conditional logic within SQL queries, allowing you to create new fields or modify existing ones based on specific conditions.

15. Indexes: Know the basics of indexing, including how indexes can improve query performance by speeding up the retrieval of rows. Understand when to create an index and the trade-offs in terms of storage and write performance.

16. Data Types: Be familiar with common SQL data types, such as VARCHAR, CHAR, INT, FLOAT, DATE, and BOOLEAN, and understand how to choose the appropriate data type for a column.

17. String Functions: Learn key string functions like CONCAT(), SUBSTRING(), REPLACE(), LENGTH(), TRIM(), and UPPER()/LOWER() to manipulate text data within queries.

18. Date and Time Functions: Master date and time functions such as NOW(), CURDATE(), DATEDIFF(), DATEADD(), and EXTRACT() to handle and manipulate date and time data effectively.

19. INSERT, UPDATE, DELETE Statements: Understand how to use INSERT to add new records, UPDATE to modify existing records, and DELETE to remove records from a table. Be aware of the implications of these operations, particularly in maintaining data integrity.

20. Constraints: Know the role of constraints like PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, and CHECK in maintaining data integrity and ensuring valid data entry in your database.

Here you can find SQL Interview Resources👇
https://t.iss.one/DataSimplifier

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Data Science Learning Plan

Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)

Step 2: Python for Data Science (Basics and Libraries)

Step 3: Data Manipulation and Analysis (Pandas, NumPy)

Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)

Step 5: Databases and SQL for Data Retrieval

Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)

Step 7: Data Cleaning and Preprocessing

Step 8: Feature Engineering and Selection

Step 9: Model Evaluation and Tuning

Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)

Step 11: Working with Big Data (Hadoop, Spark)

Step 12: Building Data Science Projects and Portfolio

Data Science Interview Resources
👇👇
https://t.iss.one/DataScienceInterviews

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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://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a

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ENJOY LEARNING 👍👍

🔅 Printing colored output in Python