Python for Data Analysis & Visualization

Explore Python objects and methods, differentiating integers and strings, and learn how functions differ from object methods with dot notation. See how to create variables and follow pep8 naming conventions.

Explore Python string methods such as upper, lower, find, replace, and split, and learn about immutability, reassignment, case sensitivity, and start and stop indexes.

Learn to concatenate strings with the plus operator and manage whitespace. Use f-strings to embed variables and format dynamic text, such as a name.

Explore booleans, the true or false data type, with case-sensitive values. See how conditional statements yield true or false, such as five greater than seven returning false.

Explore how break, continue, and pass control flow statements manage loops in Python, using examples that skip items, exit loops, and leave placeholders.

Master array manipulation in Python for data analysis using Nampai, covering reshape, flatten, ravel, concatenate, transpose, and sort with axis guidance and not in place transformations.

Learn to input and output data with numpy using load text and options, including delimiter, skip rows, and usecols, then perform statistics like max, min, mean, and standard deviation.

Launch Jupyter notebook, locate the course resources folder, and work through challenge questions on creating and slicing arrays, filtering values, and saving and combining csv files for data analysis.

Learn to create and use a series object with indexes in pandas, specify data and index, define data types, auto generate indexes, and access values by index.

Learn how to select and filter data in pandas dataframes by index and columns, including single or multiple column selection, conditional filters, and previewing with head and tail.

Combine multiple dataframes with pandas using concat, join, and merge. Understand axis handling, key-based joins, and inner, left, right, and outer joins with example employee and department data.

Tackle the step-by-step challenge questions on the employees dataset for data analysis in python, using three csv files and two notebooks, one containing the solutions.