Python 203 - Data Sciences

$2795

5 days

2024-08-26

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Essential Skills Gained

Intro to data science using Python libraries, including:

Use `pandas` and `numpy` to identify trends within datasets

Create rich visualizations with `matplotlib` and visual applications with `folium`

Use open source toolset `scipy` for mathematics, science, and engineering applications

Format

5 day course with lecture and hands-on labs.

Audience

Description

In this course you will learn to use Python, the most popular programming language for data sciences, for data analysis and data visualization. Explore Python libraries to more easily sort and analyze data sets for emerging trends. Quickly produce Excel quality visualizations appropriate for displaying data in real time monitoring systems.

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Summary

  • 💻 Welcome to the Alta3 Research Lab Environment

  • 💻 Register for Poll

Up and Running

  • 💻 Using Vi and Vim

  • 💻 Using VSCode

  • 💻 Python IDEs

Certification

  • 💻 Data Sciences with Python - Alta3 Research Certification Project

AI LLM Toolkit

  • 💻 Large Language Model toolkit for AI Solution Assistance

Software Control Management

  • 💻 SCM Option #1 - GitHub

  • 💻 SCM Option #2 - GitLab

Jupyter Notebook

  • 💻 Introduction to Jupyter Notebook

Common Data Types

  • 💬 Converting JSON to Python Data Types

  • 💻 Python, APIs, and JSON

Regular Expression

  • 💬 Introduction to Regular Expression (RegEx)

  • 💻 Use RegEx to Search Text

  • 💻 Search and Replace Data

  • 💻 Compiling RegEx Search Objects

  • 💻 Testing if a Match Exists

Pandas

  • 💻 Intro to Pandas

  • 💻 Examining Cashflow with Pandas

  • 💬 Cleaning Data with Pandas

  • 💻 Cleaning Data with Pandas

  • 🏆 Modifying DataFrames

  • 💻 Boolean Masks for DataFrames

YAML

  • 💻 Python Data to YAML file

Python and Databases

  • 💬 Interacting with Databases

SQL

  • 💻 Learning sqlite3

  • 💻 postgreSQL with Python

noSQL

  • 💻 Python and MongoDB

Matplotlib

  • 💬 Matplotlib

  • 💻 Creating Plots with Matplotlib

  • 💻 Matplotlib - Histograms and Bar Graphs

  • 💻 Annotating Graphs on Matplotlib

  • 💻 Making Subplots with Matplotlib

  • 💻 Customizing Matplotlib Graphs

  • 🏆 Create a Graph with Matplotlib

Time Series

  • 💻 Time Series Data

Numpy

  • 💻 Using Numpy Arrays

  • 💻 Generating Data with Numpy's arange and linspace

  • 💻 Indexing, Slicing, and Reshaping Multi-Dimensional Arrays

Interactive Maps

  • 💻 Building Interactive Maps

  • 💻 folium and Flask - Returning Maps from Custom API Endpoints

  • 🏆 Map the Location of the ISS

Seaborn

  • 💻 Pandas groupby and Graphing Relational Data with Seaborn

  • 💻 Visualizing Multi-Dimensional Data Using Seaborn

Scipy

  • 💻 Introducing SciPy

Machine Learning Metrics

  • 💬 Accuracy, Precision, Recall and F-scores

  • 💻 Accuracy, Precision, Recall, and F-scores

Classifications

  • 💻 Identifying Images with ImageAI and PyTorch

  • 💻 Iris Data and Scikit-learn

  • 💻 Natural Language Processing

Appendix

  • 💬 Glossary

  • 💬 Curriculum Path: Developer

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