Python from scratch
by University of Waterloo

0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion

https://open.cs.uwaterloo.ca/python-from-scratch/

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If I were to learn Python for Data Analysis again I'd focus on:

- Python Programming fundamentals.

- Pandas, Numpy, and Matplotlib for data handling/visualisation.

- Seaborn for enhanced visualisation.

- Build projects with data from Kaggle/Google Datasets.

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If you want to learn Python for data analysis, focus on these essentials

Don't aim for this:

NumPy - 100%
Pandas - 0%
Matplotlib - 0%
Seaborn - 0%
OS - 0%

Aim for this:

NumPy - 25%
Pandas - 25%
Matplotlib - 25%
Seaborn - 25%
OS - 25%

You don't need to master everything at once.

Focus on the essentials to build a strong foundation.

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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

Hope you'll like it

Like this post if you need more resources like this 👍❤️

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