Data Science & Machine Learning
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from mytools import cleaner
Anonymous Quiz
5%
A. Creates a new module
74%
C. Imports the cleaner module from the mytools package
8%
D. Installs a module from pip
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๐Ÿฎ๐Ÿฑ+ ๐— ๐˜‚๐˜€๐˜-๐—ž๐—ป๐—ผ๐˜„ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฎ๐—ป๐—ฑ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐——๐—ฟ๐—ฒ๐—ฎ๐—บ ๐—๐—ผ๐—ฏ ๐Ÿ˜

Breaking into Data Analytics isnโ€™t just about knowing the tools โ€” itโ€™s about answering the right questions with confidence๐Ÿง‘โ€๐Ÿ’ปโœจ๏ธ

Whether youโ€™re aiming for your first role or looking to level up your career, these real interview questions will test your skills๐Ÿ“Š๐Ÿ“Œ

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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˜‰
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๐—ง๐—ผ๐—ฝ ๐— ๐—ก๐—–๐˜€ ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜๐˜€ ,๐—•๐˜‚๐˜€๐—ถ๐—ป๐—ฒ๐˜€๐˜€ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜๐˜€ & ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐˜๐—ถ๐˜€๐˜๐˜€๐Ÿ˜ 
 
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Select your experience & Complete The Registration Process

 Select the company name & apply for the role that matches you
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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;
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Since many of you were asking me to send Data Science Session

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โšกBut hurry โ€” spots are limited, and the clock is ticking!โœ…๏ธ
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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‘
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