My notes on Datacamp courses. They are markdown duplicates (on jupyter notebooks) of the lecutres along with my solution to each problem.
They can be viewed on github or download them.
Only if are downloading them to your system.
- Python enviroment
- Jupyter notebook
- change directory
cd datacamp- Create a conda enviroment with
environment.yml
conda env create -f environment.yml -n $ENV_NAME
These are the courses Im currently working on.
Start/finish: 1/30/18 - 2/1/18
- Fundamental data types
- Dictionaries - the root of Python
- Meet the collections module
- Handling Dates and Times
- Answering Data Science Questions
Finish 2/14/18
- 01 Customizing plots
- 02 Plotting 2D arrays
- 03 Statistical plots with Seaborn
- 04 Analyzing time series and images
Finish 3/6/18
- Data ingestion & instpection
- Exploratory data analysis
- Time series in pandas
- Case Study - Sunlight in Austin
Finish 03/08/18
- Extracting and transforming data
- Advanced indexing
- Rearranging and reshaping data
- Grouping data
- Bringing it all together
Finish 03/13/18
- Preparing data
- Concatenating data
- Merging data
- Case Study - Summer Olympics
finish 03/16/18
- Exploring your data
- Tidying data for analysis
- Combining data for analysis
- Cleaning data for analysis
- Case study
finish 03/17/18
- Using iterators in PythonLand
- List comprehensions and generators
- Bringing it all together!
finish 03/17/18
- Introduction and flat files
- Importing data from other file types
- Working with relational databases in Python
finish 03/18/18
- Importing data from the Internet
- Interacting with APIs to import data from the web
- Diving deep into the Twitter API
- Basic plotting with Bokeh
- Layouts, Interactions, and Annotations
- Building interactive apps with Bokeh
- Putting It All Together! A Case Study
finish 03/23/18
- Basics of Relational Databases
- Applying Filtering, Ordering and Grouping to Queries
- Advanced SQLAlchemy Queries
- Creating and Manipulating your own Databases
- Putting it all together!
- Graphical exploratory data analysis
- Quantitative exploratory data analysis
- Thinking probabilistically-- Discrete variables
- Work with Multiple Time Series
- Thinking probabilistically-- Continuous variables
- Parameter estimation by optimization
- Bootstrap confidence intervals
- Introduction to hypothesis testing
- Hypothesis test examples
- Putting it all together: a case study
- Classification
- Regression
- Fine-tuning your model
- Preprocessing and pipelines
- Introduction
- Summary Statistics and Diagnostics
- Seasonality, Trend and Noise
- Work with Multiple Time Series
- Case Study