Python Interview Books
Best Resource to learn Python Programming & DSA (Data Structure and Algorithms) ๐๐ For collaborations: @coderfun
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Lists ๐ Tuples ๐ Dictionaries
What's the difference?
Lists are mutable.
Tuples are immutable.
Dictionaries are associative.
When should you use each?
Lists:
โถ When you want to add or remove elements
โถ When you want to sort elements
โถ When you want to slice elements
Tuples:
โถ When you want a constant object
โถ When you want to send multiple in a function
โถ When you want to return multiple from a function
Dictionaries:
โถ When you want to map keys to values
โถ When you want to loop over the keys
โถ When you want to validate if key exists
Now, pick your weapon of mass data analysis and become a Python pro!
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
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๐ 4
Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://t.me/sqlproject
ENJOY LEARNING ๐๐
๐ 14โค 2
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โค 22๐ 5
Lists ๐ Tuples ๐ Dictionaries
What's the difference?
Lists are mutable.
Tuples are immutable.
Dictionaries are associative.
When should you use each?
Lists:
โถ When you want to add or remove elements
โถ When you want to sort elements
โถ When you want to slice elements
Tuples:
โถ When you want a constant object
โถ When you want to send multiple in a function
โถ When you want to return multiple from a function
Dictionaries:
โถ When you want to map keys to values
โถ When you want to loop over the keys
โถ When you want to validate if key exists
Now, pick your weapon of mass data analysis and become a Python pro!
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
๐ 13โค 2๐ 2
Here are 5 key Python libraries/ concepts that are particularly important for data analysts:
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
๐ 13โค 4
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Data Analytics on LinkedIn: Many people charge too much to teach Python, but my mission is to breakโฆ
Many people charge too much to teach Python, but my mission is to break down barriers. I have shared complete learning series to learn Python fromโฆ
๐ 4
๐ Python resources to boost your resume
๐ญ. ๐๐ป๐๐ฟ๐ผ ๐๐ผ ๐ฃ๐๐๐ต๐ผ๐ป
This a great course to get started with learning Python, if you have no coding experience.
๐ https://kaggle.com/learn/intro-to-programming
๐ฎ. ๐ฃ๐๐๐ต๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐ฐ๐ผ๐๐ฟ๐๐ฒ
Learn the fundamentals like functions, loops, conditional statements, etc of the most important language for data science.
๐ https://kaggle.com/learn/python
๐ฏ. ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น๐ ๐ถ๐ป ๐ฃ๐๐๐ต๐ผ๐ป
Part 1 prepares you for PCEP โ Certified Entry-Level Python Programmer Certification.
Part 2 prepares you for PCAP โ Certified Associate in Python Programming Certification.
๐ https://netacad.com/courses/programming/pcap-programming-essentials-python
๐ฐ. Python Data Structure and Algorithms
๐ https://t.me/programming_guide/76
๐ฑ. ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐ณ๐ถ๐ฐ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
You'll learn Python fundamentals like variables, loops, conditionals, and functions.
Then you'll quickly ramp up to complex data structures, networking, relational databases, and data visualization.
๐ https://freecodecamp.org/learn/scientific-computing-with-python/
๐ฒ. ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
You'll learn how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.
๐ https://freecodecamp.org/learn/data-analysis-with-python/
๐ณ. ๐๐ฎ๐๐ฎ ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
You will learn how to implement the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general.
๐ https://cognitiveclass.ai/courses/data-visualization-python#about-course
๐ด. ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
You will build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning.
๐ https://freecodecamp.org/learn/machine-learning-with-python/
9. Practice Python
๐ https://learnpython.org/
10. Free Python course by datacamp
๐ https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=1
ENJOY LEARNING ๐๐
๐ซก 10๐ 9โค 1๐ 1
Master Python programming in 15 days with Free Resources ๐๐
Days 1-3: Introduction to Python
- Day 1: Start by installing Python on your computer.
- Day 2: Learn the basic syntax and data types in Python (variables, numbers, strings).
- Day 3: Explore Python's built-in functions and operators.
Days 4-6: Control Structures
- Day 4: Understand conditional statements (if, elif, else).
- Day 5: Learn about loops (for and while) and iterators.
- Day 6: Work on small projects to practice using conditionals and loops.
Days 7-9: Data Structures
- Day 7: Learn about lists and how to manipulate them.
- Day 8: Explore dictionaries and sets.
- Day 9: Understand tuples and lists comprehensions.
Days 10-12: Functions and Modules
- Day 10: Learn how to define functions in Python.
- Day 11: Understand scope and global vs. local variables.
- Day 12: Explore Python's module system and create your own modules.
Days 13-15: Intermediate Concepts
- Day 13: Work with file handling and I/O operations.
- Day 14: Learn about exceptions and error handling.
- Day 15: Explore more advanced topics like object-oriented programming and libraries such as NumPy, pandas, and Matplotlib.
FREE RESOURCES TO LEARN PYTHON ๐
Microsoft course for Python: https://learn.microsoft.com/en-us/training/paths/beginner-python/
Python for data Science and Machine Learning: https://t.me/datasciencefree/69
Python Interview Questions & Answers: https://t.me/dsabooks/96
Harvard course for Python: http://cs50.harvard.edu/python/2022/
Freecodecamp Python course with certificate: https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐ 11โค 2
https://topmate.io/coding/898340
If you're a job seeker, these well structured resources will help you to know and learn all the real time Python Interview questions with their exact answer. Folks who are having 0-4 years of experience have cracked the interview using this guide!
Please use the above link to avail them!๐
NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.
Hope this helps in your job search journey... All the best!๐โ๏ธ
๐ 6๐ 1
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