Fundamentals

Learn how long it takes to learn Python for Data Science in 2021. What does it take to be a Data Scientist in 2021?

Learn how to add logic to your Python code with control statements. Use if, else, and elif to make decisions based on conditions, and for and while loops to repeat actions. Master these programming concepts to write efficient and flexible code in Python.

Data visualization helps to turn complex data into actionable insights. To make effective and self-explanatory graphs, keep it simple, write labels and legends according to your reader, and choose the right library. Clear data visualization is a valuable tool for data analysis and communication.

Handle errors and exceptions in Python using 'try' and 'except' statement, with optional 'else', 'finally' blocks, use 'raise' to throw exceptions, assign exception to variable with 'as' keyword, handle multiple types of exceptions.

Automate tasks in Python by scheduling, web scraping, GUI automation, file operations using libraries like 'schedule','beautifulsoup4','requests','pyautogui','os','shutil'

Learn how to assign default value to function parameters by using '=' in function signature, default parameters must be at end of the parameter list

In Python, the yield keyword allows functions to produce a series of values over time, rather than computing them all at once, making it efficient for large data sets or for producing a series of values that can be used in a for loop.

Learn how to perform various statistics on a Pandas DataFrame such as mean, median, mode, standard deviation, variance, minimum and maximum, sum, count and more using built-in methods like .mean(), .median(), .std(), .min(), .max(), .sum(), .count(), .describe() etc.

Learn various ways to slice a Pandas DataFrame, including using the .loc[], .iloc[], and [] accessors, boolean indexing and chaining multiple conditions to select specific rows and columns.