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. You can refer this guide to help you with interview preparation.
Good luck with your Python journey ๐๐
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. You can refer this guide to help you with interview preparation.
Good luck with your Python journey ๐๐
๐10
๐ Data Analyst vs Business Analyst: Quick comparison ๐
1. Data Analyst: Dives into data, cleans it up, and finds hidden insights like Sherlock Holmes. ๐ต๏ธโโ๏ธ
Business Analyst: Talks to stakeholders, defines requirements, and ensures everyoneโs on the same page. The diplomat. ๐ค
2. Data Analyst: Master of Excel, SQL, Python, and dashboards. Their life is rows, columns, and code. ๐
Business Analyst: Fluent in meetings, presentations, and documentation. Their life is all about people and processes. ๐๏ธ
3. Data Analyst: Focuses on numbers, patterns, and trends to tell a story with data. ๐
Business Analyst: Focuses on the "why" behind the numbers to help the business make decisions. ๐ก
4. Data Analyst: Creates beautiful Power BI or Tableau dashboards that wow stakeholders. ๐จ
Business Analyst: Uses those dashboards to present actionable insights to the C-suite. ๐ค
5. Data Analyst: SQL queries, Python scripts, and statistical models are their weapons. ๐ ๏ธ
Business Analyst: Process diagrams, requirement docs, and communication are their superpowers. ๐ฆธโโ๏ธ
6. Data Analyst: โWhy is revenue declining? Let me analyze the sales data.โ
Business Analyst: โWhy is revenue declining? Letโs talk to the sales team and fix the process.โ
7. Data Analyst: Works behind the scenes, crunching data and making sense of numbers. ๐ข
Business Analyst: Works with teams to ensure that processes, strategies, and technologies align with business goals. ๐ฏ
8. Data Analyst: Uses data to make decisionsโraw data is their best friend. ๐
Business Analyst: Uses data to support business decisions and recommends solutions to improve processes. ๐
9. Data Analyst: Aims for accuracy, precision, and statistical significance in every analysis. ๐งฎ
Business Analyst: Aims to understand business needs, optimize workflows, and align solutions with business objectives. ๐ข
10. Data Analyst: Focuses on extracting insights from data for current or historical analysis. ๐
Business Analyst: Looks forward, aligning business strategies with long-term goals and improvements. ๐ฑ
Both roles are vital, but they approach the data world in their unique ways.
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Data Analyst: Dives into data, cleans it up, and finds hidden insights like Sherlock Holmes. ๐ต๏ธโโ๏ธ
Business Analyst: Talks to stakeholders, defines requirements, and ensures everyoneโs on the same page. The diplomat. ๐ค
2. Data Analyst: Master of Excel, SQL, Python, and dashboards. Their life is rows, columns, and code. ๐
Business Analyst: Fluent in meetings, presentations, and documentation. Their life is all about people and processes. ๐๏ธ
3. Data Analyst: Focuses on numbers, patterns, and trends to tell a story with data. ๐
Business Analyst: Focuses on the "why" behind the numbers to help the business make decisions. ๐ก
4. Data Analyst: Creates beautiful Power BI or Tableau dashboards that wow stakeholders. ๐จ
Business Analyst: Uses those dashboards to present actionable insights to the C-suite. ๐ค
5. Data Analyst: SQL queries, Python scripts, and statistical models are their weapons. ๐ ๏ธ
Business Analyst: Process diagrams, requirement docs, and communication are their superpowers. ๐ฆธโโ๏ธ
6. Data Analyst: โWhy is revenue declining? Let me analyze the sales data.โ
Business Analyst: โWhy is revenue declining? Letโs talk to the sales team and fix the process.โ
7. Data Analyst: Works behind the scenes, crunching data and making sense of numbers. ๐ข
Business Analyst: Works with teams to ensure that processes, strategies, and technologies align with business goals. ๐ฏ
8. Data Analyst: Uses data to make decisionsโraw data is their best friend. ๐
Business Analyst: Uses data to support business decisions and recommends solutions to improve processes. ๐
9. Data Analyst: Aims for accuracy, precision, and statistical significance in every analysis. ๐งฎ
Business Analyst: Aims to understand business needs, optimize workflows, and align solutions with business objectives. ๐ข
10. Data Analyst: Focuses on extracting insights from data for current or historical analysis. ๐
Business Analyst: Looks forward, aligning business strategies with long-term goals and improvements. ๐ฑ
Both roles are vital, but they approach the data world in their unique ways.
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐6๐2
Python Interview Questions for data analyst interview
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
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#Python
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
#Python
๐3โค1
Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. 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 and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. 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 and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
๐10โค2
๐ Master Python for Data Analytics!
Python is a powerful tool for data analysis, automation, and visualization. Hereโs the ultimate roadmap:
๐น Basic Concepts:
โก๏ธ Syntax, variables, and data types (integers, floats, strings, booleans)
โก๏ธ Control structures (if-else, for and while loops)
โก๏ธ Basic data structures (lists, dictionaries, sets, tuples)
โก๏ธ Functions, lambda functions, and error handling (try-except)
โก๏ธ Working with modules and packages
๐น Pandas & NumPy:
โก๏ธ Creating and manipulating DataFrames and arrays
โก๏ธ Data filtering, aggregation, and reshaping
โก๏ธ Handling missing values
โก๏ธ Efficient data operations with NumPy
๐น Data Visualization:
โก๏ธ Creating visualizations using Matplotlib and Seaborn
โก๏ธ Plotting line, bar, scatter, and heatmaps
#Python
Python is a powerful tool for data analysis, automation, and visualization. Hereโs the ultimate roadmap:
๐น Basic Concepts:
โก๏ธ Syntax, variables, and data types (integers, floats, strings, booleans)
โก๏ธ Control structures (if-else, for and while loops)
โก๏ธ Basic data structures (lists, dictionaries, sets, tuples)
โก๏ธ Functions, lambda functions, and error handling (try-except)
โก๏ธ Working with modules and packages
๐น Pandas & NumPy:
โก๏ธ Creating and manipulating DataFrames and arrays
โก๏ธ Data filtering, aggregation, and reshaping
โก๏ธ Handling missing values
โก๏ธ Efficient data operations with NumPy
๐น Data Visualization:
โก๏ธ Creating visualizations using Matplotlib and Seaborn
โก๏ธ Plotting line, bar, scatter, and heatmaps
#Python
๐5
Iterating over Pandas DataFrames can cost you much performance.
Comparing iterrows() and itertuples() can help in some cases:
1. ๐ถ๐๐ฒ๐ฟ๐ฟ๐ผ๐๐():
Generates index and Series pairs for each row.
๐ฃ๐ฟ๐ผ๐: Easy to use and intuitive. Suitable for small datasets.
๐๐ผ๐ป๐: Slow for large datasets. Series conversion incurs additional overhead.
๐จ๐๐ฒ ๐๐ฎ๐๐ฒ: Quick data inspection and small-scale transformations.
2. ๐ถ๐๐ฒ๐ฟ๐๐๐ฝ๐น๐ฒ๐():
Returns namedtuples of the DataFrame rows.
๐ฃ๐ฟ๐ผ๐: Much faster than iterrows(). More efficient for large datasets.
๐๐ผ๐ป๐: Slightly less intuitive syntax. Avoid using when mutating DataFrames.
๐จ๐๐ฒ ๐๐ฎ๐๐ฒ: Large-scale data processing and read-only operations.
For optimal performance, use vectorized operations whenever possible! Iteration methods should be your last resort!
Comparing iterrows() and itertuples() can help in some cases:
1. ๐ถ๐๐ฒ๐ฟ๐ฟ๐ผ๐๐():
Generates index and Series pairs for each row.
๐ฃ๐ฟ๐ผ๐: Easy to use and intuitive. Suitable for small datasets.
๐๐ผ๐ป๐: Slow for large datasets. Series conversion incurs additional overhead.
๐จ๐๐ฒ ๐๐ฎ๐๐ฒ: Quick data inspection and small-scale transformations.
2. ๐ถ๐๐ฒ๐ฟ๐๐๐ฝ๐น๐ฒ๐():
Returns namedtuples of the DataFrame rows.
๐ฃ๐ฟ๐ผ๐: Much faster than iterrows(). More efficient for large datasets.
๐๐ผ๐ป๐: Slightly less intuitive syntax. Avoid using when mutating DataFrames.
๐จ๐๐ฒ ๐๐ฎ๐๐ฒ: Large-scale data processing and read-only operations.
For optimal performance, use vectorized operations whenever possible! Iteration methods should be your last resort!
๐๐ญ๐ซ๐ข๐ง๐ ๐๐๐ง๐ข๐ฉ๐ฎ๐ฅ๐๐ญ๐ข๐จ๐ง ๐ข๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง:
Strings in Python are immutable sequences of characters.
๐- ๐ฅ๐๐ง(): ๐๐๐ญ๐ฎ๐ซ๐ง๐ฌ ๐ญ๐ก๐ ๐ฅ๐๐ง๐ ๐ญ๐ก ๐จ๐ ๐ญ๐ก๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "Hello"
length = len(my_string) # length will be 5
๐- ๐ฌ๐ญ๐ซ(): ๐๐จ๐ง๐ฏ๐๐ซ๐ญ๐ฌ ๐ง๐จ๐ง-๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐๐๐ญ๐ ๐ญ๐ฒ๐ฉ๐๐ฌ ๐ข๐ง๐ญ๐จ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ.
num = 123
str_num = str(num) # str_num will be "123"
๐- ๐ฅ๐จ๐ฐ๐๐ซ() ๐๐ง๐ ๐ฎ๐ฉ๐ฉ๐๐ซ(): ๐๐จ๐ง๐ฏ๐๐ซ๐ญ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ญ๐จ ๐ฅ๐จ๐ฐ๐๐ซ๐๐๐ฌ๐ ๐จ๐ซ ๐ฎ๐ฉ๐ฉ๐๐ซ๐๐๐ฌ๐.
my_string = "Hello"
lower_case = my_string.lower() # lower_case will be "hello"
upper_case = my_string.upper() # upper_case will be "HELLO"
๐- ๐ฌ๐ญ๐ซ๐ข๐ฉ(): ๐๐๐ฆ๐จ๐ฏ๐๐ฌ ๐ฅ๐๐๐๐ข๐ง๐ ๐๐ง๐ ๐ญ๐ซ๐๐ข๐ฅ๐ข๐ง๐ ๐ฐ๐ก๐ข๐ญ๐๐ฌ๐ฉ๐๐๐ ๐๐ซ๐จ๐ฆ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = " Hello "
stripped_string = my_string.strip() # stripped_string will be "Hello"
๐- ๐ฌ๐ฉ๐ฅ๐ข๐ญ(): ๐๐ฉ๐ฅ๐ข๐ญ๐ฌ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ข๐ง๐ญ๐จ ๐ ๐ฅ๐ข๐ฌ๐ญ ๐จ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ ๐๐๐ฌ๐๐ ๐จ๐ง ๐ ๐๐๐ฅ๐ข๐ฆ๐ข๐ญ๐๐ซ.
my_string = "apple,banana,orange"
fruits = my_string.split(",") # fruits will be ["apple", "banana", "orange"]
๐- ๐ฃ๐จ๐ข๐ง(): ๐๐จ๐ข๐ง๐ฌ ๐ญ๐ก๐ ๐๐ฅ๐๐ฆ๐๐ง๐ญ๐ฌ ๐จ๐ ๐ ๐ฅ๐ข๐ฌ๐ญ ๐ข๐ง๐ญ๐จ ๐ ๐ฌ๐ข๐ง๐ ๐ฅ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฎ๐ฌ๐ข๐ง๐ ๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐๐ ๐ฌ๐๐ฉ๐๐ซ๐๐ญ๐จ๐ซ.
fruits = ["apple", "banana", "orange"]
my_string = ",".join(fruits) # my_string will be "apple,banana,orange"
๐- ๐๐ข๐ง๐() ๐๐ง๐ ๐ข๐ง๐๐๐ฑ(): ๐๐๐๐ซ๐๐ก ๐๐จ๐ซ ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฐ๐ข๐ญ๐ก๐ข๐ง ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐๐ง๐ ๐ซ๐๐ญ๐ฎ๐ซ๐ง ๐ข๐ญ๐ฌ ๐ข๐ง๐๐๐ฑ.
my_string = "Hello, world!"
index1 = my_string.find("world") # index1 will be 7
index2 = my_string.index("world") # index2 will also be 7
๐- ๐ซ๐๐ฉ๐ฅ๐๐๐(): ๐๐๐ฉ๐ฅ๐๐๐๐ฌ ๐จ๐๐๐ฎ๐ซ๐ซ๐๐ง๐๐๐ฌ ๐จ๐ ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฐ๐ข๐ญ๐ก ๐๐ง๐จ๐ญ๐ก๐๐ซ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "Hello, world!"
new_string = my_string.replace("world", "Python") # new_string will be "Hello, Python!"
๐- ๐ฌ๐ญ๐๐ซ๐ญ๐ฌ๐ฐ๐ข๐ญ๐ก() ๐๐ง๐ ๐๐ง๐๐ฌ๐ฐ๐ข๐ญ๐ก(): ๐๐ก๐๐๐ค๐ฌ ๐ข๐ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ๐ญ๐๐ซ๐ญ๐ฌ ๐จ๐ซ ๐๐ง๐๐ฌ ๐ฐ๐ข๐ญ๐ก ๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "Hello, world!"
starts_with_hello = my_string.startswith("Hello") # True
ends_with_world = my_string.endswith("world") # False
๐๐- ๐๐จ๐ฎ๐ง๐ญ(): ๐๐จ๐ฎ๐ง๐ญ๐ฌ ๐ญ๐ก๐ ๐จ๐๐๐ฎ๐ซ๐ซ๐๐ง๐๐๐ฌ ๐จ๐ ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ข๐ง ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "apple, banana, orange, banana"
count = my_string.count("banana") # count will be 2
Python Free Resources
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Strings in Python are immutable sequences of characters.
๐- ๐ฅ๐๐ง(): ๐๐๐ญ๐ฎ๐ซ๐ง๐ฌ ๐ญ๐ก๐ ๐ฅ๐๐ง๐ ๐ญ๐ก ๐จ๐ ๐ญ๐ก๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "Hello"
length = len(my_string) # length will be 5
๐- ๐ฌ๐ญ๐ซ(): ๐๐จ๐ง๐ฏ๐๐ซ๐ญ๐ฌ ๐ง๐จ๐ง-๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐๐๐ญ๐ ๐ญ๐ฒ๐ฉ๐๐ฌ ๐ข๐ง๐ญ๐จ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ.
num = 123
str_num = str(num) # str_num will be "123"
๐- ๐ฅ๐จ๐ฐ๐๐ซ() ๐๐ง๐ ๐ฎ๐ฉ๐ฉ๐๐ซ(): ๐๐จ๐ง๐ฏ๐๐ซ๐ญ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ญ๐จ ๐ฅ๐จ๐ฐ๐๐ซ๐๐๐ฌ๐ ๐จ๐ซ ๐ฎ๐ฉ๐ฉ๐๐ซ๐๐๐ฌ๐.
my_string = "Hello"
lower_case = my_string.lower() # lower_case will be "hello"
upper_case = my_string.upper() # upper_case will be "HELLO"
๐- ๐ฌ๐ญ๐ซ๐ข๐ฉ(): ๐๐๐ฆ๐จ๐ฏ๐๐ฌ ๐ฅ๐๐๐๐ข๐ง๐ ๐๐ง๐ ๐ญ๐ซ๐๐ข๐ฅ๐ข๐ง๐ ๐ฐ๐ก๐ข๐ญ๐๐ฌ๐ฉ๐๐๐ ๐๐ซ๐จ๐ฆ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = " Hello "
stripped_string = my_string.strip() # stripped_string will be "Hello"
๐- ๐ฌ๐ฉ๐ฅ๐ข๐ญ(): ๐๐ฉ๐ฅ๐ข๐ญ๐ฌ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ข๐ง๐ญ๐จ ๐ ๐ฅ๐ข๐ฌ๐ญ ๐จ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ ๐๐๐ฌ๐๐ ๐จ๐ง ๐ ๐๐๐ฅ๐ข๐ฆ๐ข๐ญ๐๐ซ.
my_string = "apple,banana,orange"
fruits = my_string.split(",") # fruits will be ["apple", "banana", "orange"]
๐- ๐ฃ๐จ๐ข๐ง(): ๐๐จ๐ข๐ง๐ฌ ๐ญ๐ก๐ ๐๐ฅ๐๐ฆ๐๐ง๐ญ๐ฌ ๐จ๐ ๐ ๐ฅ๐ข๐ฌ๐ญ ๐ข๐ง๐ญ๐จ ๐ ๐ฌ๐ข๐ง๐ ๐ฅ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฎ๐ฌ๐ข๐ง๐ ๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐๐ ๐ฌ๐๐ฉ๐๐ซ๐๐ญ๐จ๐ซ.
fruits = ["apple", "banana", "orange"]
my_string = ",".join(fruits) # my_string will be "apple,banana,orange"
๐- ๐๐ข๐ง๐() ๐๐ง๐ ๐ข๐ง๐๐๐ฑ(): ๐๐๐๐ซ๐๐ก ๐๐จ๐ซ ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฐ๐ข๐ญ๐ก๐ข๐ง ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐๐ง๐ ๐ซ๐๐ญ๐ฎ๐ซ๐ง ๐ข๐ญ๐ฌ ๐ข๐ง๐๐๐ฑ.
my_string = "Hello, world!"
index1 = my_string.find("world") # index1 will be 7
index2 = my_string.index("world") # index2 will also be 7
๐- ๐ซ๐๐ฉ๐ฅ๐๐๐(): ๐๐๐ฉ๐ฅ๐๐๐๐ฌ ๐จ๐๐๐ฎ๐ซ๐ซ๐๐ง๐๐๐ฌ ๐จ๐ ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฐ๐ข๐ญ๐ก ๐๐ง๐จ๐ญ๐ก๐๐ซ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "Hello, world!"
new_string = my_string.replace("world", "Python") # new_string will be "Hello, Python!"
๐- ๐ฌ๐ญ๐๐ซ๐ญ๐ฌ๐ฐ๐ข๐ญ๐ก() ๐๐ง๐ ๐๐ง๐๐ฌ๐ฐ๐ข๐ญ๐ก(): ๐๐ก๐๐๐ค๐ฌ ๐ข๐ ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ๐ญ๐๐ซ๐ญ๐ฌ ๐จ๐ซ ๐๐ง๐๐ฌ ๐ฐ๐ข๐ญ๐ก ๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "Hello, world!"
starts_with_hello = my_string.startswith("Hello") # True
ends_with_world = my_string.endswith("world") # False
๐๐- ๐๐จ๐ฎ๐ง๐ญ(): ๐๐จ๐ฎ๐ง๐ญ๐ฌ ๐ญ๐ก๐ ๐จ๐๐๐ฎ๐ซ๐ซ๐๐ง๐๐๐ฌ ๐จ๐ ๐ ๐ฌ๐ฎ๐๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ข๐ง ๐ ๐ฌ๐ญ๐ซ๐ข๐ง๐ .
my_string = "apple, banana, orange, banana"
count = my_string.count("banana") # count will be 2
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Python Interview Questions for data analyst interview
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
๐7
Artificial Intelligence (AI) Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
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ENJOY LEARNING๐๐
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
โค4๐3
Top 21 skills to learn this year ๐
1. Artificial Intelligence and Machine Learning: Understanding AI algorithms and applications.
2. Data Science: Proficiency in tools like Python/ R, Jupyter Notebook, and GitHub, with the ability to apply data science algorithms to solve real-world problems.
3. Cybersecurity: Protecting data and systems from cyber threats.
4. Cloud Computing: Proficiency in platforms like AWS, Azure, and Google Cloud.
5. Blockchain Technology: Understanding blockchain architecture and applications beyond cryptocurrencies.
6. Digital Marketing: Expertise in SEO, social media, and online advertising.
7. Programming: Skills in languages such as Python, JavaScript, and Go.
8. UX/UI Design: Creating intuitive and effective user interfaces and experiences.
9. Consulting: Expertise in providing strategic advice, improving business processes, and implementing solutions to drive business growth.
10. Data Analysis and Visualization: Proficiency in tools like Excel, SQL, Tableau, and Power BI to analyze and present data effectively.
11. Business Analysis & Project Management: Using tools and methodologies like Agile and Scrum.
12. Remote Work Tools: Proficiency in tools for remote collaboration and productivity.
13. Financial Literacy: Understanding personal finance, investment, and cryptocurrencies.
14. Emotional Intelligence: Skills in empathy, communication, and relationship management.
15. Business Acumen: A deep understanding of how businesses operate, including strategic thinking, market analysis, and financial literacy.
16. Investment Banking: Knowledge of financial markets, valuation methods, mergers and acquisitions, and financial modeling.
17. Mobile App Development: Skills in developing apps for iOS and Android using Swift, Kotlin, or React Native.
18. Financial Management: Proficiency in financial planning, analysis, and tools like QuickBooks and SAP.
19. Web Development: Proficiency in front-end and back-end development using HTML, CSS, JavaScript, and frameworks like React, Angular, and Node.js.
20. Data Engineering: Skills in designing, building, and maintaining data pipelines and architectures using tools like Hadoop, Spark, and Kafka.
21. Soft Skills: Improving leadership, teamwork, and adaptability skills.
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1. Artificial Intelligence and Machine Learning: Understanding AI algorithms and applications.
2. Data Science: Proficiency in tools like Python/ R, Jupyter Notebook, and GitHub, with the ability to apply data science algorithms to solve real-world problems.
3. Cybersecurity: Protecting data and systems from cyber threats.
4. Cloud Computing: Proficiency in platforms like AWS, Azure, and Google Cloud.
5. Blockchain Technology: Understanding blockchain architecture and applications beyond cryptocurrencies.
6. Digital Marketing: Expertise in SEO, social media, and online advertising.
7. Programming: Skills in languages such as Python, JavaScript, and Go.
8. UX/UI Design: Creating intuitive and effective user interfaces and experiences.
9. Consulting: Expertise in providing strategic advice, improving business processes, and implementing solutions to drive business growth.
10. Data Analysis and Visualization: Proficiency in tools like Excel, SQL, Tableau, and Power BI to analyze and present data effectively.
11. Business Analysis & Project Management: Using tools and methodologies like Agile and Scrum.
12. Remote Work Tools: Proficiency in tools for remote collaboration and productivity.
13. Financial Literacy: Understanding personal finance, investment, and cryptocurrencies.
14. Emotional Intelligence: Skills in empathy, communication, and relationship management.
15. Business Acumen: A deep understanding of how businesses operate, including strategic thinking, market analysis, and financial literacy.
16. Investment Banking: Knowledge of financial markets, valuation methods, mergers and acquisitions, and financial modeling.
17. Mobile App Development: Skills in developing apps for iOS and Android using Swift, Kotlin, or React Native.
18. Financial Management: Proficiency in financial planning, analysis, and tools like QuickBooks and SAP.
19. Web Development: Proficiency in front-end and back-end development using HTML, CSS, JavaScript, and frameworks like React, Angular, and Node.js.
20. Data Engineering: Skills in designing, building, and maintaining data pipelines and architectures using tools like Hadoop, Spark, and Kafka.
21. Soft Skills: Improving leadership, teamwork, and adaptability skills.
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7 level of writing Python Dictionary
Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
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Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
I have curated the best interview resources to crack Python Interviews ๐๐
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Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ