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Datacamp

My notes on Datacamp courses. They are markdown duplicates (on jupyter notebooks) of the lecutres along with my solution to each problem.

Getting Started

They can be viewed on github or download them.

Prerequisites

Only if are downloading them to your system.

• Python enviroment
• Jupyter notebook

1. change directory

cd datacamp

2. Create a conda enviroment with environment.yml

conda env create -f environment.yml -n $ENV_NAME

Courses

These are the courses Im currently working on.

Data Types for Data Science

• Fundamental data types
• Dictionaries - the root of Python
• Meet the collections module
• Handling Dates and Times
• Answering Data Science Questions

Introduction to Data Visualization with Python

• 01 Customizing plots
• 02 Plotting 2D arrays
• 03 Statistical plots with Seaborn
• 04 Analyzing time series and images

Pandas Foundations

• Data ingestion & instpection
• Exploratory data analysis
• Time series in pandas
• Case Study - Sunlight in Austin

Manipulating DataFrames with pandas

• Extracting and transforming data
• Advanced indexing
• Rearranging and reshaping data
• Grouping data
• Bringing it all together

Merging DataFrames with pandas

• Preparing data
• Concatenating data
• Merging data
• Case Study - Summer Olympics

Cleaning Data in Python

• Exploring your data
• Tidying data for analysis
• Combining data for analysis
• Cleaning data for analysis
• Case study

Python Data Science Toolbox (Part 2)

• Using iterators in PythonLand
• List comprehensions and generators
• Bringing it all together!

Importing Data in Python (Part 1)

• Introduction and flat files
• Importing data from other file types
• Working with relational databases in Python

Importing Data in Python (Part 2)

• Importing data from the Internet
• Interacting with APIs to import data from the web
• Diving deep into the Twitter API

Interactive Data Visualization with Bokeh

• Basic plotting with Bokeh
• Layouts, Interactions, and Annotations
• Building interactive apps with Bokeh
• Putting It All Together! A Case Study

Introduction to Databases in Python

• Basics of Relational Databases
• Applying Filtering, Ordering and Grouping to Queries
• Advanced SQLAlchemy Queries
• Creating and Manipulating your own Databases
• Putting it all together!

Statistical Thinking in Python (Part 1)

• Graphical exploratory data analysis
• Quantitative exploratory data analysis
• Thinking probabilistically-- Discrete variables
• Work with Multiple Time Series
• Thinking probabilistically-- Continuous variables

Statistical Thinking in Python (Part 1)

• Parameter estimation by optimization
• Bootstrap confidence intervals
• Introduction to hypothesis testing
• Hypothesis test examples
• Putting it all together: a case study

Supervised Learning with scikit-learn

• Classification
• Regression
• Fine-tuning your model
• Preprocessing and pipelines

Visualizing Time Series Data in Python

• Introduction
• Summary Statistics and Diagnostics
• Seasonality, Trend and Noise
• Work with Multiple Time Series
• Case Study

Intro to SQL for Data Science

• Selecting Columns
• Filtering rows
• Aggregate Functions
• Sortin, grouping and joins

Joining Data in SQL

• Introduction to joins
• Outer joins and cross joins
• Set theory clauses
• Subqueries