Data Science & Machine Learning
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from mytools import cleaner
Anonymous Quiz
6%
A. Creates a new module
72%
C. Imports the cleaner module from the mytools package
8%
D. Installs a module from pip
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When starting off your data analytics journey you DON'T need to be a SQL guru from the get-go.

In fact, most SQL skills you will only learn on the job with:

- real business problems.
- actual data sets.
- imperfect data architecture.
- other people to collaborate with.

So be kind to yourself, give yourself time to grow and above all...

try to become proficient at SQL rather than perfect.

The rest will take care of itself along the way! ๐Ÿ˜‰
โค7๐Ÿ‘1
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Select your experience & Complete The Registration Process

 Select the company name & apply for the role that matches you
โค2๐Ÿ”ฅ1
SQL Cheatsheet ๐Ÿ“

This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether youโ€™re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.

1. Database Basics
- CREATE DATABASE db_name;
- USE db_name;

2. Tables
- Create Table: CREATE TABLE table_name (col1 datatype, col2 datatype);
- Drop Table: DROP TABLE table_name;
- Alter Table: ALTER TABLE table_name ADD column_name datatype;

3. Insert Data
- INSERT INTO table_name (col1, col2) VALUES (val1, val2);

4. Select Queries
- Basic Select: SELECT * FROM table_name;
- Select Specific Columns: SELECT col1, col2 FROM table_name;
- Select with Condition: SELECT * FROM table_name WHERE condition;

5. Update Data
- UPDATE table_name SET col1 = value1 WHERE condition;

6. Delete Data
- DELETE FROM table_name WHERE condition;

7. Joins
- Inner Join: SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;
- Left Join: SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;
- Right Join: SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;

8. Aggregations
- Count: SELECT COUNT(*) FROM table_name;
- Sum: SELECT SUM(col) FROM table_name;
- Group By: SELECT col, COUNT(*) FROM table_name GROUP BY col;

9. Sorting & Limiting
- Order By: SELECT * FROM table_name ORDER BY col ASC|DESC;
- Limit Results: SELECT * FROM table_name LIMIT n;

10. Indexes
- Create Index: CREATE INDEX idx_name ON table_name (col);
- Drop Index: DROP INDEX idx_name;

11. Subqueries
- SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);

12. Views
- Create View: CREATE VIEW view_name AS SELECT * FROM table_name;
- Drop View: DROP VIEW view_name;
โค7๐Ÿ‘2
Since many of you were asking me to send Data Science Session

๐Ÿ“ŒSo we have come with a session for you!! ๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป

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Register here
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Only limited free slots are available so Register Now
โค2
๐Ÿš€ Complete Roadmap to Become a Data Scientist in 5 Months

๐Ÿ“… Week 1-2: Fundamentals
โœ… Day 1-3: Introduction to Data Science, its applications, and roles.
โœ… Day 4-7: Brush up on Python programming ๐Ÿ.
โœ… Day 8-10: Learn basic statistics ๐Ÿ“Š and probability ๐ŸŽฒ.

๐Ÿ” Week 3-4: Data Manipulation & Visualization
๐Ÿ“ Day 11-15: Master Pandas for data manipulation.
๐Ÿ“ˆ Day 16-20: Learn Matplotlib & Seaborn for data visualization.

๐Ÿค– Week 5-6: Machine Learning Foundations
๐Ÿ”ฌ Day 21-25: Introduction to scikit-learn.
๐Ÿ“Š Day 26-30: Learn Linear & Logistic Regression.

๐Ÿ— Week 7-8: Advanced Machine Learning
๐ŸŒณ Day 31-35: Explore Decision Trees & Random Forests.
๐Ÿ“Œ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

๐Ÿง  Week 9-10: Deep Learning
๐Ÿค– Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐Ÿ“ธ Day 46-50: Learn CNNs & RNNs for image & text data.

๐Ÿ› Week 11-12: Data Engineering
๐Ÿ—„ Day 51-55: Learn SQL & Databases.
๐Ÿงน Day 56-60: Data Preprocessing & Cleaning.

๐Ÿ“Š Week 13-14: Model Evaluation & Optimization
๐Ÿ“ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐Ÿ“‰ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

๐Ÿ— Week 15-16: Big Data & Tools
๐Ÿ˜ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ˜๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

๐Ÿš€ Week 17-18: Deployment & Production
๐Ÿ›  Day 81-85: Deploy models using Flask or FastAPI.
๐Ÿ“ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

๐ŸŽฏ Week 19-20: Specialization
๐Ÿ“ Day 91-95: Choose NLP or Computer Vision, based on your interest.

๐Ÿ† Week 21-22: Projects & Portfolio
๐Ÿ“‚ Day 96-100: Work on Personal Data Science Projects.

๐Ÿ’ฌ Week 23-24: Soft Skills & Networking
๐ŸŽค Day 101-105: Improve Communication & Presentation Skills.
๐ŸŒ Day 106-110: Attend Online Meetups & Forums.

๐ŸŽฏ Week 25-26: Interview Preparation
๐Ÿ’ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐Ÿ“‚ Day 116-120: Review your projects & prepare for discussions.

๐Ÿ‘จโ€๐Ÿ’ป Week 27-28: Apply for Jobs
๐Ÿ“ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.

๐ŸŽค Week 29-30: Interviews
๐Ÿ“ Day 126-130: Attend Interviews & Practice Whiteboard Problems.

๐Ÿ”„ Week 31-32: Continuous Learning
๐Ÿ“ฐ Day 131-135: Stay updated with the Latest Data Science Trends.

๐Ÿ† Week 33-34: Accepting Offers
๐Ÿ“ Day 136-140: Evaluate job offers & Negotiate Your Salary.

๐Ÿข Week 35-36: Settling In
๐ŸŽฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!

๐ŸŽ‰ Enjoy Learning & Build Your Dream Career in Data Science! ๐Ÿš€๐Ÿ”ฅ
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๐Ÿ”ฐ Data Science Roadmap for Beginners 2025
โ”œโ”€โ”€ ๐Ÿ“˜ What is Data Science?
โ”œโ”€โ”€ ๐Ÿง  Data Science vs Data Analytics vs Machine Learning
โ”œโ”€โ”€ ๐Ÿ›  Tools of the Trade (Python, R, Excel, SQL)
โ”œโ”€โ”€ ๐Ÿ Python for Data Science (NumPy, Pandas, Matplotlib)
โ”œโ”€โ”€ ๐Ÿ”ข Statistics & Probability Basics
โ”œโ”€โ”€ ๐Ÿ“Š Data Visualization (Matplotlib, Seaborn, Plotly)
โ”œโ”€โ”€ ๐Ÿงผ Data Cleaning & Preprocessing
โ”œโ”€โ”€ ๐Ÿงฎ Exploratory Data Analysis (EDA)
โ”œโ”€โ”€ ๐Ÿง  Introduction to Machine Learning
โ”œโ”€โ”€ ๐Ÿ“ฆ Supervised vs Unsupervised Learning
โ”œโ”€โ”€ ๐Ÿค– Popular ML Algorithms (Linear Reg, KNN, Decision Trees)
โ”œโ”€โ”€ ๐Ÿงช Model Evaluation (Accuracy, Precision, Recall, F1 Score)
โ”œโ”€โ”€ ๐Ÿงฐ Model Tuning (Cross Validation, Grid Search)
โ”œโ”€โ”€ โš™๏ธ Feature Engineering
โ”œโ”€โ”€ ๐Ÿ— Real-world Projects (Kaggle, UCI Datasets)
โ”œโ”€โ”€ ๐Ÿ“ˆ Basic Deployment (Streamlit, Flask, Heroku)
โ”œโ”€โ”€ ๐Ÿ” Continuous Learning: Blogs, Research Papers, Competitions

Like for more โค๏ธ
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Data Science Interview Questions

1. What are the different subsets of SQL?

Data Definition Language (DDL) โ€“ It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) โ€“ It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) โ€“ It allows you to control access to the database. Example โ€“ Grant, Revoke access permissions.

2. List the different types of relationships in SQL.

There are different types of relations in the database:
One-to-One โ€“ This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One โ€“ This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many โ€“ This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships โ€“ When a table has to declare a connection with itself, this is the method to employ.

3. How to create empty tables with the same structure as another table?

To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.

4. What is Normalization and what are the advantages of it?

Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security
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Complete Data Science Roadmap
๐Ÿ‘‡๐Ÿ‘‡

1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)

2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics

3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD

4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering

5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)

6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation

7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics

8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data

9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)

10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data

11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models

12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)

13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)

14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models

15. Tools for Data Science
- Jupyter, Git, Docker

16. Career Path & Certifications
- Building a Data Science Portfolio

Like if you need similar content ๐Ÿ˜„๐Ÿ‘

Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree

Python Project Ideas: https://t.iss.one/dsabooks/85

Best Resources to learn Data Science ๐Ÿ‘‡๐Ÿ‘‡

Python Tutorial

Data Science Course by Kaggle

Machine Learning Course by Google

Best Data Science & Machine Learning Resources

Interview Process for Data Science Role at Amazon

Python Interview Resources

Join @free4unow_backup for more free courses

Like for more โค๏ธ

ENJOY LEARNING๐Ÿ‘๐Ÿ‘
โค11
Common Machine Learning Algorithms!

1๏ธโƒฃ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.

2๏ธโƒฃ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.

3๏ธโƒฃ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.

4๏ธโƒฃ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.

5๏ธโƒฃ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.

6๏ธโƒฃ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.

7๏ธโƒฃ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.

8๏ธโƒฃ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.

9๏ธโƒฃ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.

๐Ÿ”Ÿ Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
โค5
Which algorithm is best for predicting house prices?
Anonymous Quiz
27%
a) Logistic Regression
58%
b) Linear Regression
12%
c) K-Means
3%
d) Naive Bayes
โค2
Which algorithm is best suited for spam detection?
Anonymous Quiz
33%
a) Decision Tree
22%
b) Linear Regression
29%
c) Naive Bayes
16%
d) K-Means
โค1
Which is not a supervised learning algorithm?
Anonymous Quiz
14%
a) Random Forest
47%
b) K-Means
20%
c) Logistic Regression
18%
d) SVM
โค1
What makes Random Forest better than a single Decision Tree?
Anonymous Quiz
9%
a) More memory
12%
b) More splits
75%
c) Uses multiple trees to reduce overfitting
3%
d) Less data used
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๐Ÿ“Š ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜

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โค1
Guys, Big Announcement!

Weโ€™ve officially hit 2.5 Million followers โ€” and itโ€™s time to level up together! โค๏ธ

Iโ€™m launching a Python Projects Series โ€” designed for beginners to those preparing for technical interviews or building real-world projects.

This will be a step-by-step, hands-on journey โ€” where youโ€™ll build useful Python projects with clear code, explanations, and mini-quizzes!

Hereโ€™s what weโ€™ll cover:

๐Ÿ”น Week 1: Python Mini Projects (Daily Practice)
โฆ Calculator
โฆ To-Do List (CLI)
โฆ Number Guessing Game
โฆ Unit Converter
โฆ Digital Clock

๐Ÿ”น Week 2: Data Handling & APIs
โฆ Read/Write CSV & Excel files
โฆ JSON parsing
โฆ API Calls using Requests
โฆ Weather App using OpenWeather API
โฆ Currency Converter using Real-time API

๐Ÿ”น Week 3: Automation with Python
โฆ File Organizer Script
โฆ Email Sender
โฆ WhatsApp Automation
โฆ PDF Merger
โฆ Excel Report Generator

๐Ÿ”น Week 4: Data Analysis with Pandas & Matplotlib
โฆ Load & Clean CSV
โฆ Data Aggregation
โฆ Data Visualization
โฆ Trend Analysis
โฆ Dashboard Basics

๐Ÿ”น Week 5: AI & ML Projects (Beginner Friendly)
โฆ Predict House Prices
โฆ Email Spam Classifier
โฆ Sentiment Analysis
โฆ Image Classification (Intro)
โฆ Basic Chatbot

๐Ÿ“Œ Each project includes: 
โœ… Problem Statement 
โœ… Code with explanation 
โœ… Sample input/output 
โœ… Learning outcome 
โœ… Mini quiz

๐Ÿ’ฌ React โค๏ธ if you're ready to build some projects together!

You can access it for free here
๐Ÿ‘‡๐Ÿ‘‡
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Letโ€™s Build. Letโ€™s Grow. ๐Ÿ’ป๐Ÿ™Œ
โค13๐Ÿ‘2๐Ÿฅฐ2๐Ÿ‘1
๐Ÿ’ ๐๐ž๐ฌ๐ญ ๐๐จ๐ฐ๐ž๐ซ ๐๐ˆ ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ ๐ข๐ง ๐Ÿ๐ŸŽ๐Ÿ๐Ÿ“ ๐ญ๐จ ๐’๐ค๐ฒ๐ซ๐จ๐œ๐ค๐ž๐ญ ๐˜๐จ๐ฎ๐ซ ๐‚๐š๐ซ๐ž๐ž๐ซ๐Ÿ˜

In todayโ€™s data-driven world, Power BI has become one of the most in-demand tools for businessesใ€ฝ๏ธ๐Ÿ“Š

The best part? You donโ€™t need to spend a fortuneโ€”there are free and affordable courses available online to get you started.๐Ÿ’ฅ๐Ÿง‘โ€๐Ÿ’ป

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Start learning today and position yourself for success in 2025!โœ…๏ธ
โค2
Data Science Interview Questions ๐Ÿš€

1. What is Data Science and how does it differ from Data Analytics?
2. How do you handle missing or duplicate data?
3. Explain supervised vs unsupervised learning.
4. What is overfitting and how do you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation and why is it important?
7. What are key evaluation metrics for classification models?
8. What is feature engineering? Give examples.
9. Explain principal component analysis (PCA).
10. Difference between classification and regression algorithms.
11. What is a confusion matrix?
12. Explain bagging vs boosting.
13. Describe decision trees and random forests.
14. What is gradient descent?
15. What are regularization techniques and why use them?
16. How do you handle imbalanced datasets?
17. What is hypothesis testing and p-values?
18. Explain clustering and k-means algorithm.
19. How do you handle unstructured data?
20. What is text mining and sentiment analysis?
21. How do you select important features?
22. What is ensemble learning?
23. Basics of time series analysis.
24. How do you tune hyperparameters?
25. What are activation functions in neural networks?
26. Explain transfer learning.
27. How do you deploy machine learning models?
28. What are common challenges in big data?
29. Define ROC curve and AUC score.
30. What is deep learning?
31. What is reinforcement learning?
32. What tools and libraries do you use?
33. How do you interpret model results for non-technical audiences?
34. What is dimensionality reduction?
35. Handling categorical variables in machine learning.
36. What is exploratory data analysis (EDA)?
37. Explain t-test and chi-square test.
38. How do you ensure fairness and avoid bias in models?
39. Describe a complex data problem you solved.
40. How do you stay updated with new data science trends?

React โค๏ธ for the detailed answers
โค33
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