๐ Top 10 Python Interview Questions for Data Science (2025)
1. What makes Python popular for Data Science?
Python offers a rich ecosystem of libraries like NumPy, pandas, scikit-learn, and matplotlib, making data manipulation, analysis, and machine learning efficient and accessible.
2. How do you handle missing values in a dataset with Python?
Using pandas, you can use
3. What is a lambda function in Python, and how is it used in data science?
A lambda is a small anonymous function defined with
4. Explain the difference between a list and a tuple in Python.
Lists are mutable (can be changed), whereas tuples are immutable (cannot be changed); tuples are often used for fixed data, offering slight performance benefits.
5. How can you merge two pandas DataFrames?
Use
6. What is vectorization, and why is it important?
Vectorization uses array operations (e.g., NumPy) instead of loops, accelerating computations significantly by leveraging optimized C code under the hood.
7. How do you calculate summary statistics in pandas?
Functions like
8. What is the difference between
9. Explain how you would build a simple linear regression model in Python.
You can use
10. How do you handle categorical data in Python?
Use pandas for encoding categorical variables via
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1. What makes Python popular for Data Science?
Python offers a rich ecosystem of libraries like NumPy, pandas, scikit-learn, and matplotlib, making data manipulation, analysis, and machine learning efficient and accessible.
2. How do you handle missing values in a dataset with Python?
Using pandas, you can use
.fillna() to replace missing values with a fixed value or statistic (mean, median), or .dropna() to remove rows/columns containing NaNs.3. What is a lambda function in Python, and how is it used in data science?
A lambda is a small anonymous function defined with
lambda keyword, commonly used for quick transformations or within higher-order functions like .apply() in pandas.4. Explain the difference between a list and a tuple in Python.
Lists are mutable (can be changed), whereas tuples are immutable (cannot be changed); tuples are often used for fixed data, offering slight performance benefits.
5. How can you merge two pandas DataFrames?
Use
pd.merge() with keys specifying columns to join on; supports different types of joins like inner, outer, left, and right.6. What is vectorization, and why is it important?
Vectorization uses array operations (e.g., NumPy) instead of loops, accelerating computations significantly by leveraging optimized C code under the hood.
7. How do you calculate summary statistics in pandas?
Functions like
.mean(), .median(), .std(), .describe() provide quick statistical insights over DataFrame columns.8. What is the difference between
.loc[] and .iloc[] in pandas? .loc[] selects data based on labels/index names, while .iloc[] selects using integer position-based indexing.9. Explain how you would build a simple linear regression model in Python.
You can use
scikit-learnโs LinearRegression class to fit a model with .fit(), then predict with .predict() on new data.10. How do you handle categorical data in Python?
Use pandas for encoding categorical variables via
.astype('category'), .get_dummies() for one-hot encoding, or LabelEncoder from scikit-learn for label encoding.๐ฅ React โค๏ธ for more!
โค7๐6
Myths About Data Science:
โ Data Science is Just Coding
Coding is a part of data science. It also involves statistics, domain expertise, communication skills, and business acumen. Soft skills are as important or even more important than technical ones
โ Data Science is a Solo Job
I wish. I wanted to be a data scientist so I could sit quietly in a corner and code. Data scientists often work in teams, collaborating with engineers, product managers, and business analysts
โ Data Science is All About Big Data
Big data is a big buzzword (that was more popular 10 years ago), but not all data science projects involve massive datasets. Itโs about the quality of the data and the questions youโre asking, not just the quantity.
โ You Need to Be a Math Genius
Many data science problems can be solved with basic statistical methods and simple logistic regression. Itโs more about applying the right techniques rather than knowing advanced math theories.
โ Data Science is All About Algorithms
Algorithms are a big part of data science, but understanding the data and the business problem is equally important. Choosing the right algorithm is crucial, but itโs not just about complex models. Sometimes simple models can provide the best results. Logistic regression!
โ Data Science is Just Coding
Coding is a part of data science. It also involves statistics, domain expertise, communication skills, and business acumen. Soft skills are as important or even more important than technical ones
โ Data Science is a Solo Job
I wish. I wanted to be a data scientist so I could sit quietly in a corner and code. Data scientists often work in teams, collaborating with engineers, product managers, and business analysts
โ Data Science is All About Big Data
Big data is a big buzzword (that was more popular 10 years ago), but not all data science projects involve massive datasets. Itโs about the quality of the data and the questions youโre asking, not just the quantity.
โ You Need to Be a Math Genius
Many data science problems can be solved with basic statistical methods and simple logistic regression. Itโs more about applying the right techniques rather than knowing advanced math theories.
โ Data Science is All About Algorithms
Algorithms are a big part of data science, but understanding the data and the business problem is equally important. Choosing the right algorithm is crucial, but itโs not just about complex models. Sometimes simple models can provide the best results. Logistic regression!
โค16๐ฅ2
Hey guys,
Today, letโs talk about SQL conceptual questions that are often asked in data analyst interviews. These questions test not only your technical skills but also your conceptual understanding of SQL and its real-world applications.
1. What is the difference between SQL and NoSQL?
- SQL (Structured Query Language) is a relational database management system, meaning it uses tables (rows and columns) to store data.
- NoSQL databases, on the other hand, handle unstructured data and donโt rely on a schema, making them more flexible in terms of data storage and retrieval.
- Interview Tip: Don't just memorize definitions. Be prepared to explain scenarios where youโd use SQL over NoSQL, and vice versa.
2. What is the difference between INNER JOIN and OUTER JOIN?
- An INNER JOIN returns records that have matching values in both tables.
- An OUTER JOIN returns all records from one table and the matched records from the second table. If there's no match, NULL values are returned.
3. How do you optimize a SQL query for better performance?
- Indexing: Create indexes on columns used frequently in WHERE, JOIN, or GROUP BY clauses.
- Query optimization: Use appropriate WHERE clauses to reduce the data set and avoid unnecessary calculations.
- Avoid SELECT *: Always specify the columns you need to reduce the amount of data retrieved.
- Limit results: If you only need a subset of the data, use the LIMIT clause.
4. What are the different types of SQL constraints?
Constraints are used to enforce rules on data in a table. They ensure the accuracy and reliability of the data. The most common types are:
- PRIMARY KEY: Ensures each record is unique and not null.
- FOREIGN KEY: Enforces a relationship between two tables.
- UNIQUE: Ensures all values in a column are unique.
- NOT NULL: Prevents NULL values from being entered into a column.
- CHECK: Ensures a column's values meet a specific condition.
5. What is normalization? What are the different normal forms?
Normalization is the process of organizing data to reduce redundancy and improve data integrity. Hereโs a quick overview of normal forms:
- 1NF (First Normal Form): Ensures that all values in a table are atomic (indivisible).
- 2NF (Second Normal Form): Ensures that the table is in 1NF and that all non-key columns are fully dependent on the primary key.
- 3NF (Third Normal Form): Ensures that the table is in 2NF and all columns are independent of each other except for the primary key.
6. What is a subquery?
A subquery is a query within another query. It's used to perform operations that need intermediate results before generating the final query.
Example:
In this case, the subquery calculates the average salary, and the outer query selects employees whose salary is greater than the average.
7. What is the difference between a UNION and a UNION ALL?
- UNION combines the result sets of two SELECT statements and removes duplicates.
- UNION ALL combines the result sets and includes duplicates.
8. What is the difference between WHERE and HAVING clause?
- WHERE filters rows before any groupings are made. Itโs used with SELECT, INSERT, UPDATE, or DELETE statements.
- HAVING filters groups after the GROUP BY clause.
9. How would you handle NULL values in SQL?
NULL values can represent missing or unknown data. Hereโs how to manage them:
- Use IS NULL or IS NOT NULL in WHERE clauses to filter null values.
- Use COALESCE() or IFNULL() to replace NULL values with default ones.
Example:
10. What is the purpose of the GROUP BY clause?
The GROUP BY clause groups rows with the same values into summary rows. Itโs often used with aggregate functions like COUNT, SUM, AVG, etc.
Example:
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Today, letโs talk about SQL conceptual questions that are often asked in data analyst interviews. These questions test not only your technical skills but also your conceptual understanding of SQL and its real-world applications.
1. What is the difference between SQL and NoSQL?
- SQL (Structured Query Language) is a relational database management system, meaning it uses tables (rows and columns) to store data.
- NoSQL databases, on the other hand, handle unstructured data and donโt rely on a schema, making them more flexible in terms of data storage and retrieval.
- Interview Tip: Don't just memorize definitions. Be prepared to explain scenarios where youโd use SQL over NoSQL, and vice versa.
2. What is the difference between INNER JOIN and OUTER JOIN?
- An INNER JOIN returns records that have matching values in both tables.
- An OUTER JOIN returns all records from one table and the matched records from the second table. If there's no match, NULL values are returned.
3. How do you optimize a SQL query for better performance?
- Indexing: Create indexes on columns used frequently in WHERE, JOIN, or GROUP BY clauses.
- Query optimization: Use appropriate WHERE clauses to reduce the data set and avoid unnecessary calculations.
- Avoid SELECT *: Always specify the columns you need to reduce the amount of data retrieved.
- Limit results: If you only need a subset of the data, use the LIMIT clause.
4. What are the different types of SQL constraints?
Constraints are used to enforce rules on data in a table. They ensure the accuracy and reliability of the data. The most common types are:
- PRIMARY KEY: Ensures each record is unique and not null.
- FOREIGN KEY: Enforces a relationship between two tables.
- UNIQUE: Ensures all values in a column are unique.
- NOT NULL: Prevents NULL values from being entered into a column.
- CHECK: Ensures a column's values meet a specific condition.
5. What is normalization? What are the different normal forms?
Normalization is the process of organizing data to reduce redundancy and improve data integrity. Hereโs a quick overview of normal forms:
- 1NF (First Normal Form): Ensures that all values in a table are atomic (indivisible).
- 2NF (Second Normal Form): Ensures that the table is in 1NF and that all non-key columns are fully dependent on the primary key.
- 3NF (Third Normal Form): Ensures that the table is in 2NF and all columns are independent of each other except for the primary key.
6. What is a subquery?
A subquery is a query within another query. It's used to perform operations that need intermediate results before generating the final query.
Example:
SELECT employee_id, name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
In this case, the subquery calculates the average salary, and the outer query selects employees whose salary is greater than the average.
7. What is the difference between a UNION and a UNION ALL?
- UNION combines the result sets of two SELECT statements and removes duplicates.
- UNION ALL combines the result sets and includes duplicates.
8. What is the difference between WHERE and HAVING clause?
- WHERE filters rows before any groupings are made. Itโs used with SELECT, INSERT, UPDATE, or DELETE statements.
- HAVING filters groups after the GROUP BY clause.
9. How would you handle NULL values in SQL?
NULL values can represent missing or unknown data. Hereโs how to manage them:
- Use IS NULL or IS NOT NULL in WHERE clauses to filter null values.
- Use COALESCE() or IFNULL() to replace NULL values with default ones.
Example:
SELECT name, COALESCE(age, 0) AS age
FROM employees;
10. What is the purpose of the GROUP BY clause?
The GROUP BY clause groups rows with the same values into summary rows. Itโs often used with aggregate functions like COUNT, SUM, AVG, etc.
Example:
SELECT department, COUNT(*)
FROM employees
GROUP BY department;
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค11
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Difference between linear regression and logistic regression ๐๐
Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.
Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.
Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.
Data Science Interview Resources
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Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.
Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.
Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.
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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
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Python Learning Plan in 2025
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources๐
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Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค10๐1
Where Each Programming Language Shines ๐๐จ๐ปโ๐ป
โฏ C โ OS Development, Embedded Systems, Game Engines
โฏ C++ โ Game Development, High-Performance Applications, Financial Systems
โฏ Java โ Enterprise Software, Android Development, Backend Systems
โฏ C# โ Game Development (Unity), Windows Applications, Enterprise Software
โฏ Python โ AI/ML, Data Science, Web Development, Automation
โฏ JavaScript โ Frontend Web Development, Full-Stack Apps, Game Development
โฏ Golang โ Cloud Services, Networking, High-Performance APIs
โฏ Swift โ iOS/macOS App Development
โฏ Kotlin โ Android Development, Backend Services
โฏ PHP โ Web Development (WordPress, Laravel)
โฏ Ruby โ Web Development (Ruby on Rails), Prototyping
โฏ Rust โ Systems Programming, High-Performance Computing, Blockchain
โฏ Lua โ Game Scripting (Roblox, WoW), Embedded Systems
โฏ R โ Data Science, Statistics, Bioinformatics
โฏ SQL โ Database Management, Data Analytics
โฏ TypeScript โ Scalable Web Applications, Large JavaScript Projects
โฏ Node.js โ Backend Development, Real-Time Applications
โฏ React โ Modern Web Applications, Interactive UIs
โฏ Vue โ Lightweight Frontend Development, SPAs
โฏ Django โ Scalable Web Applications, AI/ML Backend
โฏ Laravel โ Full-Stack PHP Development
โฏ Blazor โ Web Apps with .NET
โฏ Spring Boot โ Enterprise Java Applications, Microservices
โฏ Ruby on Rails โ Startup Web Apps, MVP Development
โฏ HTML/CSS โ Web Design, UI Development
โฏ GIT โ Version Control, Collaboration
โฏ Linux โ Server Management, Security, DevOps
โฏ DevOps โ Infrastructure Automation, CI/CD
โฏ CI/CD โ Continuous Deployment & Testing
โฏ Docker โ Containerization, Cloud Deployments
โฏ Kubernetes โ Scalable Cloud Orchestration
โฏ Microservices โ Distributed Systems, Scalable Backends
โฏ Selenium โ Web Automation Testing
โฏ Playwright โ Modern Browser Automation
React โค๏ธ for more
โฏ C โ OS Development, Embedded Systems, Game Engines
โฏ C++ โ Game Development, High-Performance Applications, Financial Systems
โฏ Java โ Enterprise Software, Android Development, Backend Systems
โฏ C# โ Game Development (Unity), Windows Applications, Enterprise Software
โฏ Python โ AI/ML, Data Science, Web Development, Automation
โฏ JavaScript โ Frontend Web Development, Full-Stack Apps, Game Development
โฏ Golang โ Cloud Services, Networking, High-Performance APIs
โฏ Swift โ iOS/macOS App Development
โฏ Kotlin โ Android Development, Backend Services
โฏ PHP โ Web Development (WordPress, Laravel)
โฏ Ruby โ Web Development (Ruby on Rails), Prototyping
โฏ Rust โ Systems Programming, High-Performance Computing, Blockchain
โฏ Lua โ Game Scripting (Roblox, WoW), Embedded Systems
โฏ R โ Data Science, Statistics, Bioinformatics
โฏ SQL โ Database Management, Data Analytics
โฏ TypeScript โ Scalable Web Applications, Large JavaScript Projects
โฏ Node.js โ Backend Development, Real-Time Applications
โฏ React โ Modern Web Applications, Interactive UIs
โฏ Vue โ Lightweight Frontend Development, SPAs
โฏ Django โ Scalable Web Applications, AI/ML Backend
โฏ Laravel โ Full-Stack PHP Development
โฏ Blazor โ Web Apps with .NET
โฏ Spring Boot โ Enterprise Java Applications, Microservices
โฏ Ruby on Rails โ Startup Web Apps, MVP Development
โฏ HTML/CSS โ Web Design, UI Development
โฏ GIT โ Version Control, Collaboration
โฏ Linux โ Server Management, Security, DevOps
โฏ DevOps โ Infrastructure Automation, CI/CD
โฏ CI/CD โ Continuous Deployment & Testing
โฏ Docker โ Containerization, Cloud Deployments
โฏ Kubernetes โ Scalable Cloud Orchestration
โฏ Microservices โ Distributed Systems, Scalable Backends
โฏ Selenium โ Web Automation Testing
โฏ Playwright โ Modern Browser Automation
React โค๏ธ for more
โค3