What does this line do?
from mytools import cleaner
from mytools import cleaner
Anonymous Quiz
5%
A. Creates a new module
14%
B. Imports a class from cleaner.py
74%
C. Imports the cleaner module from the mytools package
8%
D. Installs a module from pip
โค2๐ฅ2
๐ฎ๐ฑ+ ๐ ๐๐๐-๐๐ป๐ผ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ฎ๐ป๐ฑ ๐ฌ๐ผ๐๐ฟ ๐๐ฟ๐ฒ๐ฎ๐บ ๐๐ผ๐ฏ ๐
Breaking into Data Analytics isnโt just about knowing the tools โ itโs about answering the right questions with confidence๐งโ๐ปโจ๏ธ
Whether youโre aiming for your first role or looking to level up your career, these real interview questions will test your skills๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JumloI
Donโt just learn โ prepare smartโ ๏ธ
Breaking into Data Analytics isnโt just about knowing the tools โ itโs about answering the right questions with confidence๐งโ๐ปโจ๏ธ
Whether youโre aiming for your first role or looking to level up your career, these real interview questions will test your skills๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JumloI
Donโt just learn โ prepare smartโ ๏ธ
โค2
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! ๐
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! ๐
โค6๐1
๐ง๐ผ๐ฝ ๐ ๐ก๐๐ ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐๐ ,๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐๐ & ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐๐๐
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Companies Hiring:-
- Goldman Sachs
- Natwest Group
- Siemens
- JP Morgan
- Accenture & Many More
Salary Range :- 5 To 24LPA
Job Location :- PAN India
๐๐ฝ๐ฝ๐น๐ ๐ก๐ผ๐๐:-
https://bit.ly/44qMX2k
Select your experience & Complete The Registration Process
Select the company name & apply for the role that matches you
โค2
SQL Cheatsheet ๐
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether youโre 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
-
-
2. Tables
- Create Table:
- Drop Table:
- Alter Table:
3. Insert Data
-
4. Select Queries
- Basic Select:
- Select Specific Columns:
- Select with Condition:
5. Update Data
-
6. Delete Data
-
7. Joins
- Inner Join:
- Left Join:
- Right Join:
8. Aggregations
- Count:
- Sum:
- Group By:
9. Sorting & Limiting
- Order By:
- Limit Results:
10. Indexes
- Create Index:
- Drop Index:
11. Subqueries
-
12. Views
- Create View:
- Drop View:
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether youโre 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;
โค5๐2
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โกBut hurry โ spots are limited, and the clock is ticking!โ ๏ธ
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๐ Complete Roadmap to Become a Data Scientist in 5 Months
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
โค3
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๐ฐ Data Science Roadmap for Beginners 2025
โโโ ๐ What is Data Science?
โโโ ๐ง Data Science vs Data Analytics vs Machine Learning
โโโ ๐ Tools of the Trade (Python, R, Excel, SQL)
โโโ ๐ Python for Data Science (NumPy, Pandas, Matplotlib)
โโโ ๐ข Statistics & Probability Basics
โโโ ๐ Data Visualization (Matplotlib, Seaborn, Plotly)
โโโ ๐งผ Data Cleaning & Preprocessing
โโโ ๐งฎ Exploratory Data Analysis (EDA)
โโโ ๐ง Introduction to Machine Learning
โโโ ๐ฆ Supervised vs Unsupervised Learning
โโโ ๐ค Popular ML Algorithms (Linear Reg, KNN, Decision Trees)
โโโ ๐งช Model Evaluation (Accuracy, Precision, Recall, F1 Score)
โโโ ๐งฐ Model Tuning (Cross Validation, Grid Search)
โโโ โ๏ธ Feature Engineering
โโโ ๐ Real-world Projects (Kaggle, UCI Datasets)
โโโ ๐ Basic Deployment (Streamlit, Flask, Heroku)
โโโ ๐ Continuous Learning: Blogs, Research Papers, Competitions
Like for more โค๏ธ
โโโ ๐ What is Data Science?
โโโ ๐ง Data Science vs Data Analytics vs Machine Learning
โโโ ๐ Tools of the Trade (Python, R, Excel, SQL)
โโโ ๐ Python for Data Science (NumPy, Pandas, Matplotlib)
โโโ ๐ข Statistics & Probability Basics
โโโ ๐ Data Visualization (Matplotlib, Seaborn, Plotly)
โโโ ๐งผ Data Cleaning & Preprocessing
โโโ ๐งฎ Exploratory Data Analysis (EDA)
โโโ ๐ง Introduction to Machine Learning
โโโ ๐ฆ Supervised vs Unsupervised Learning
โโโ ๐ค Popular ML Algorithms (Linear Reg, KNN, Decision Trees)
โโโ ๐งช Model Evaluation (Accuracy, Precision, Recall, F1 Score)
โโโ ๐งฐ Model Tuning (Cross Validation, Grid Search)
โโโ โ๏ธ Feature Engineering
โโโ ๐ Real-world Projects (Kaggle, UCI Datasets)
โโโ ๐ Basic Deployment (Streamlit, Flask, Heroku)
โโโ ๐ Continuous Learning: Blogs, Research Papers, Competitions
Like for more โค๏ธ
โค6๐1
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
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
โค1
Complete Data Science Roadmap
๐๐
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 ๐๐
๐๐
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 ๐๐
โค7