Python Landscape
EDITION MODE. REFERENCES ARE MISSING. THERE ARE TYPOS.
Contents
- 1 Welcome to the Python Wiki for Data Analysis
- 2 What can you do with Python?
- 3 Setting up your environment
- 4 Starting with Python basics
- 5 Doing some math with Python
- 6 Handling data in Python
- 7 Statistics with Python
- 8 Data Visualization
- 9 Machine Learning
- 10 Data Engineering
- 11 Data Storytelling
- 12 Project Management in Python
Welcome to the Python Wiki for Data Analysis
Python has become one of the most popular programming languages because of its simplicity, readability and versatility. It is a high-level, interpreted programming language created by Guido van Rossum and first released in 1991. Python emphasizes code readability and syntax that allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java, therefore, for aspiring data analysts or scientists, Python represents a convenient entry into the programming world because it can be learned and mastered in a relative short period of time.
What can you do with Python?
With Python you can conduct simple and complex data analysis, design and launch websites, and even design artificial intelligence algorithms. This diagram is just a glimpse of the areas of application when working with python.
Python is a high-level, interpreted programming language known for its simplicity and readability. It's widely used in various fields, from web development to data science and artificial intelligence.
Setting up your environment
- Install and setup Anaconda
- Setting up Jupyter Lab
Starting with Python basics
- Conditions
- Loops
- Lists, Tuples, Sets, and Dictionaries
- Exceptions
- Functions
- Classes
- Objects
- Types, Expressions, and Variables
Doing some math with Python
Handling data in Python
Statistics with Python
- Binomial Distribution
- Bootstrapping
- Correlations
- Decision Trees
- Factor Analysis
- Linear Regression
- Multiple Regression
- Outlier Detection
- Poisson Distribution
- Regression, Correlation, and Ordinary Least Squares Estimator
Data Visualization
Data visualization is very important because...
Machine Learning
Data Engineering
Use of API Cloud computing
