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
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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
Python Free Resources
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Like this post if you need more resources like this ๐โค๏ธ
๐4โค1
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
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
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.
Join for more: ๐
https://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
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.
Join for more: ๐
https://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
๐7โค2
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
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
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
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
Top 10 Python functions that are commonly used in data analysis
import pandas as pd: This function is used to import the Pandas library, which is essential for data manipulation and analysis.
read_csv(): This function from Pandas is used to read data from CSV files into a DataFrame, a primary data structure for data analysis.
head(): It allows you to quickly preview the first few rows of a DataFrame to understand its structure.
describe(): This function provides summary statistics of the numeric columns in a DataFrame, such as mean, standard deviation, and percentiles.
groupby(): It's used to group data by one or more columns, enabling aggregation and analysis within those groups.
pivot_table(): This function helps in creating pivot tables, allowing you to summarize and reshape data for analysis.
fillna(): Useful for filling missing values in a DataFrame with a specified value or a calculated one (e.g., mean or median).
apply(): This function is used to apply custom functions to DataFrame columns or rows, which is handy for data transformation.
plot(): It's part of the Matplotlib library and is used for creating various data visualizations, such as line plots, bar charts, and scatter plots.
merge(): This function is used for combining two or more DataFrames based on a common column or index, which is crucial for joining datasets during analysis.
These functions are essential tools for any data analyst working with Python for data analysis tasks.
Hope it helps :)
import pandas as pd: This function is used to import the Pandas library, which is essential for data manipulation and analysis.
read_csv(): This function from Pandas is used to read data from CSV files into a DataFrame, a primary data structure for data analysis.
head(): It allows you to quickly preview the first few rows of a DataFrame to understand its structure.
describe(): This function provides summary statistics of the numeric columns in a DataFrame, such as mean, standard deviation, and percentiles.
groupby(): It's used to group data by one or more columns, enabling aggregation and analysis within those groups.
pivot_table(): This function helps in creating pivot tables, allowing you to summarize and reshape data for analysis.
fillna(): Useful for filling missing values in a DataFrame with a specified value or a calculated one (e.g., mean or median).
apply(): This function is used to apply custom functions to DataFrame columns or rows, which is handy for data transformation.
plot(): It's part of the Matplotlib library and is used for creating various data visualizations, such as line plots, bar charts, and scatter plots.
merge(): This function is used for combining two or more DataFrames based on a common column or index, which is crucial for joining datasets during analysis.
These functions are essential tools for any data analyst working with Python for data analysis tasks.
Hope it helps :)
๐5โค4
Forwarded from SQL For Data Analytics
Essentials for Acing any Data Analytics Interviews-
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
โค8๐3
Python Programming Interview Questions for Entry Level Data Analyst
1. What is Python, and why is it popular in data analysis?
2. Differentiate between Python 2 and Python 3.
3. Explain the importance of libraries like NumPy and Pandas in data analysis.
4. How do you read and write data from/to files using Python?
5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.
6. What are list comprehensions, and how do you use them in Python?
7. Explain the concept of object-oriented programming (OOP) in Python.
8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.
9. How do you handle missing or NaN values in a DataFrame using Pandas?
10. Explain the difference between loc and iloc in Pandas DataFrame indexing.
11. Discuss the purpose and usage of lambda functions in Python.
12. What are Python decorators, and how do they work?
13. How do you handle categorical data in Python using the Pandas library?
14. Explain the concept of data normalization and its importance in data preprocessing.
15. Discuss the role of regular expressions (regex) in data cleaning with Python.
16. What are Python virtual environments, and why are they useful?
17. How do you handle outliers in a dataset using Python?
18. Explain the usage of the map and filter functions in Python.
19. Discuss the concept of recursion in Python programming.
20. How do you perform data analysis and visualization using Jupyter Notebooks?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
1. What is Python, and why is it popular in data analysis?
2. Differentiate between Python 2 and Python 3.
3. Explain the importance of libraries like NumPy and Pandas in data analysis.
4. How do you read and write data from/to files using Python?
5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.
6. What are list comprehensions, and how do you use them in Python?
7. Explain the concept of object-oriented programming (OOP) in Python.
8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.
9. How do you handle missing or NaN values in a DataFrame using Pandas?
10. Explain the difference between loc and iloc in Pandas DataFrame indexing.
11. Discuss the purpose and usage of lambda functions in Python.
12. What are Python decorators, and how do they work?
13. How do you handle categorical data in Python using the Pandas library?
14. Explain the concept of data normalization and its importance in data preprocessing.
15. Discuss the role of regular expressions (regex) in data cleaning with Python.
16. What are Python virtual environments, and why are they useful?
17. How do you handle outliers in a dataset using Python?
18. Explain the usage of the map and filter functions in Python.
19. Discuss the concept of recursion in Python programming.
20. How do you perform data analysis and visualization using Jupyter Notebooks?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
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