30-day roadmap to learn Python up to an intermediate level
Week 1: Python Basics
*Day 1-2:*
- Learn about Python, its syntax, and how to install Python on your computer.
- Write your first "Hello, World!" program.
- Understand variables and data types (integers, floats, strings).
*Day 3-4:*
- Explore basic operations (arithmetic, string concatenation).
- Learn about user input and how to use the
- Practice creating and using variables.
*Day 5-7:*
- Dive into control flow with if statements, else statements, and loops (for and while).
- Work on simple programs that involve conditions and loops.
Week 2: Functions and Modules
*Day 8-9:*
- Study functions and how to define your own functions using
- Learn about function arguments and return values.
*Day 10-12:*
- Explore built-in functions and libraries (e.g.,
- Understand how to import modules and use their functions.
*Day 13-14:*
- Practice writing functions for common tasks.
- Create a small project that utilizes functions and modules.
Week 3: Data Structures
*Day 15-17:*
- Learn about lists and their operations (slicing, appending, removing).
- Understand how to work with lists of different data types.
*Day 18-19:*
- Study dictionaries and their key-value pairs.
- Practice manipulating dictionary data.
*Day 20-21:*
- Explore tuples and sets.
- Understand when and how to use each data structure.
Week 4: Intermediate Topics
*Day 22-23:*
- Study file handling and how to read/write files in Python.
- Work on projects involving file operations.
*Day 24-26:*
- Learn about exceptions and error handling.
- Explore object-oriented programming (classes and objects).
*Day 27-28:*
- Dive into more advanced topics like list comprehensions and generators.
- Study Python's built-in libraries for web development (e.g., requests).
*Day 29-30:*
- Explore additional libraries and frameworks relevant to your interests (e.g., NumPy for data analysis, Flask for web development, or Pygame for game development).
- Work on a more complex project that combines your knowledge from the past weeks.
Throughout the 30 days, practice coding daily, and don't hesitate to explore Python's documentation and online resources for additional help. Learning Python is a dynamic process, so adapt the roadmap based on your progress and interests.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING 👍👍
Week 1: Python Basics
*Day 1-2:*
- Learn about Python, its syntax, and how to install Python on your computer.
- Write your first "Hello, World!" program.
- Understand variables and data types (integers, floats, strings).
*Day 3-4:*
- Explore basic operations (arithmetic, string concatenation).
- Learn about user input and how to use the
input() function.- Practice creating and using variables.
*Day 5-7:*
- Dive into control flow with if statements, else statements, and loops (for and while).
- Work on simple programs that involve conditions and loops.
Week 2: Functions and Modules
*Day 8-9:*
- Study functions and how to define your own functions using
def.- Learn about function arguments and return values.
*Day 10-12:*
- Explore built-in functions and libraries (e.g.,
len(), random, math).- Understand how to import modules and use their functions.
*Day 13-14:*
- Practice writing functions for common tasks.
- Create a small project that utilizes functions and modules.
Week 3: Data Structures
*Day 15-17:*
- Learn about lists and their operations (slicing, appending, removing).
- Understand how to work with lists of different data types.
*Day 18-19:*
- Study dictionaries and their key-value pairs.
- Practice manipulating dictionary data.
*Day 20-21:*
- Explore tuples and sets.
- Understand when and how to use each data structure.
Week 4: Intermediate Topics
*Day 22-23:*
- Study file handling and how to read/write files in Python.
- Work on projects involving file operations.
*Day 24-26:*
- Learn about exceptions and error handling.
- Explore object-oriented programming (classes and objects).
*Day 27-28:*
- Dive into more advanced topics like list comprehensions and generators.
- Study Python's built-in libraries for web development (e.g., requests).
*Day 29-30:*
- Explore additional libraries and frameworks relevant to your interests (e.g., NumPy for data analysis, Flask for web development, or Pygame for game development).
- Work on a more complex project that combines your knowledge from the past weeks.
Throughout the 30 days, practice coding daily, and don't hesitate to explore Python's documentation and online resources for additional help. Learning Python is a dynamic process, so adapt the roadmap based on your progress and interests.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING 👍👍
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Websites and platforms where you can practice Python projects, do hands-on coding, and gain valuable experience
1. w3schools
(https://www.w3schools.com/python): Offers interactive Python courses and coding exercises. Great for beginners.
2. Learn Python (https://learnpython.org/): Good resource for beginners
3. Freecodecamp (https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course): Offers Python courses, including those from top universities. You can earn certificates upon completion.
4. Hackerrank (https://www.hackerrank.com/domains/tutorials/10-days-of-python): Offers Python tutorials and coding challenges to practice your skills.
5. Google (https://developers.google.com/edu/python): Free resource to learn python from Google.
6. Project Euler (https://projecteuler.net/): Provides mathematical and computational problems that can be solved with Python. Great for honing your programming skills.
7. Python.org (https://docs.python.org/3/tutorial/index.html): The official Python website has a tutorial section that includes exercises and examples to practice Python concepts.
8. GitHub (https://github.com/): Explore Python repositories, contribute to open-source projects, or start your own Python project to build a portfolio.
9. Kaggle (https://www.kaggle.com/): Offers Python datasets, competitions, and notebooks for data science and machine learning projects.
10. Udemy (https://bit.ly/45q7pxh): Amazing course to master Python in 100 days with coding challenges with certificate. Learn data science, automation, build websites, games and apps!
Remember that practice is key to mastering Python. Choose projects and exercises that align with your interests and goals, and don't hesitate to explore multiple platforms to diversify your learning experience.
ENJOY LEARNING 👍👍
1. w3schools
(https://www.w3schools.com/python): Offers interactive Python courses and coding exercises. Great for beginners.
2. Learn Python (https://learnpython.org/): Good resource for beginners
3. Freecodecamp (https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course): Offers Python courses, including those from top universities. You can earn certificates upon completion.
4. Hackerrank (https://www.hackerrank.com/domains/tutorials/10-days-of-python): Offers Python tutorials and coding challenges to practice your skills.
5. Google (https://developers.google.com/edu/python): Free resource to learn python from Google.
6. Project Euler (https://projecteuler.net/): Provides mathematical and computational problems that can be solved with Python. Great for honing your programming skills.
7. Python.org (https://docs.python.org/3/tutorial/index.html): The official Python website has a tutorial section that includes exercises and examples to practice Python concepts.
8. GitHub (https://github.com/): Explore Python repositories, contribute to open-source projects, or start your own Python project to build a portfolio.
9. Kaggle (https://www.kaggle.com/): Offers Python datasets, competitions, and notebooks for data science and machine learning projects.
10. Udemy (https://bit.ly/45q7pxh): Amazing course to master Python in 100 days with coding challenges with certificate. Learn data science, automation, build websites, games and apps!
Remember that practice is key to mastering Python. Choose projects and exercises that align with your interests and goals, and don't hesitate to explore multiple platforms to diversify your learning experience.
ENJOY LEARNING 👍👍
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Build 5 Python Projects in just 10 minutes 👇
https://youtu.be/kjemNqhAQVA?si=10jSVUKzSSfD1Ubl
https://youtu.be/kjemNqhAQVA?si=10jSVUKzSSfD1Ubl
YouTube
Build 5 Easy Python Projects in 10 Minutes [For Beginners]
🚀 Learn Python FAST with these 5 simple projects you can code in under 10 lines! Perfect for beginners looking to build practical skills.
📌 Projects Covered:
1. Link Shortener 🔗
2. Voice Recorder 🎤
3. Password Generator 🔒
4. Guess the Number Game…
📌 Projects Covered:
1. Link Shortener 🔗
2. Voice Recorder 🎤
3. Password Generator 🔒
4. Guess the Number Game…
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Scrap Image from google using BeautifulSoup
import requests
from bs4 import BeautifulSoup as BSP
def get_image_urls(search_query):
url = f"https://www.google.com/search?q={search_query}&tbm=isch"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
}
rss = requests.get(url, headers=headers)
soup = BSP(rss.content, "html.parser")
all_img = []
for img in soup.find_all('img'):
src = img['src']
if not src.endswith("gif"):
all_img.append(src)
return all_img
print(get_image_urls("boy"))
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Tip : Use the zip() function to iterate over multiple iterables simultaneously
Example:
Benefits:
* Simplifies the process of iterating over multiple lists or tuples
* Ensures that the elements from corresponding lists are aligned
Language:
Example:
# Create two lists
names = ["John", "Mary", "Bob"]
ages = [30, 25, 40]
# Iterate over both lists using zip()
for name, age in zip(names, ages):
print(f"{name} is {age} years old.")
Benefits:
* Simplifies the process of iterating over multiple lists or tuples
* Ensures that the elements from corresponding lists are aligned
Language:
Python👍6
🔟 Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://t.me/sqlspecialist/379
ENJOY LEARNING 👍👍
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://t.me/sqlspecialist/379
ENJOY LEARNING 👍👍
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Coding and Aptitude Round before interview
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
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