Description

Who should attend

This course was written for professionals interested in Python and Data Sciences. This includes: Engineers, Mathematicians, Actuaries, Network Specialists, System Admins, and developers.

Prerequisites

Keyboard proficiency, and some previous python coding experience is the only hard requirement. Students with some previous exposure to Python, or any another scripting experience, will take the most from the course. In lieu of previous experience, Alta3 Research’s Python Basics course is recommended.

Recommended Prerequisite: Python Basics (5 days)

Follow On Courses

Python 201 – APIs and API Design with Python (AADP)

Outline: Python 203 – Python for Data Sciences (PDS)

Introduction to Python Libraries for Data Sciences

Python with Jupyter Notebook overview
- Live code
- Equations
- Data cleaning
- Transformation
- Numerical simulation
- Statistical modeling
- Data visualization
- Machine Learning
Pandas
- Filter DataFrames
- Dictionaries to DataFrames
- CSV to DataFrames
- Excel to DataFrames
Numpy
- Work across arrays
Requests
- Pull from RESTful APIs
- JSON

Sort, Analyze, and Visualize Data with Python

Matplotlib
- Line Plots
- Area Plots
- Histograms
- Bar Charts
- Pie Charts
- Box Plots
- Scatter Plots
- Bubble Plots
- Waffle Charts
- Word Clouds
Seaborn
- visualization techniques
  - Relational
  - Categorical
  - Distributions
  - Regressions
Folium
- interactive leaflet maps
- choropleth visualizations
- rich vector/raster/HTML visual markers
Saving visualizations output in various formats

Python and Databases

Creating a database engine in Python
sqlite3
Looking at tables in a database
Querying relational databases
MySQL and Python
SQL Queries
- Filtering with SQL WHERE
- Ordering with SQL ORDER BY
- Querying with pandas
- Table relationships with INNER JOIN
MongoDB
- Understanding noSQL
- Python and MongoDB
- Pymongo
  - Query
  - Find
  - Delete
  - Update
  - Limit

Introduction to Machine Learning with Python

scipy open ecosystem
- numerical integration
- Interpolation
- Optimization
- linear algebra
- statistics
Scikit–learn
- Applications of Machine Learning
- Training vs Testing sets
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
- Loading an example dataset
- Learning and predicting

Introduction to Machine Learning with Python (continued)

Scikit–learn
- Model persistence
- Conventions
- Refitting and updating parameters
- Multiclass vs. multilabel fitting
Moving output to remote systems
- Streaming (push) to real–time dashboard APIs
- Move data with SFTP
- Email attachments

Python 203 – Python for Data Sciences