Using Python to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, and predict future trends from data!
- Importing Datasets
- Cleaning the Data
- Dataframe manipulation
- Summarizing the Data
- Building machine learning Regression models
- Building data pipelines with Python
Data Analysis libraries: Pandas, Numpy and Scipy libraries to work with a sample dataset. Use pandas, an open-source library, to load, manipulate, analyze, and visualize cool datasets. Use scikit-learn's machine learning algorithms to build smart models and make cool predictions.
Python Indentation Indentation refers to the spaces at the beginning of a code line.
Where in other programming languages the indentation in code is for readability only, the indentation in Python is very important.
Python uses indentation to indicate a block of code.
Example if 5 > 2: print("Five is greater than two!")
l = ["apple", "banana", "cherry"] #list t = ("apple", "banana", "cherry") #tuple x = range(6) #range x = {"name" : "John", "age" : 36} #dict x = {"apple", "banana", "cherry"} #set x = frozenset({"apple", "banana", "cherry"}) #frozenset x = True #bool x = b"Hello" bytes x = bytearray(5) bytearray x = memoryview(bytes(5))