https://github.com/jesussantana/ibm-data-analysis-with-python-da0101en

This course will take you from the basics of Python to exploring many different types of data.
https://github.com/jesussantana/ibm-data-analysis-with-python-da0101en

anova correlation data-analysis model-evaluation numpy pandas prepare-data python regression-models statistics

Last synced: 8 months ago
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This course will take you from the basics of Python to exploring many different types of data.

• Host: GitHub
• URL: https://github.com/jesussantana/ibm-data-analysis-with-python-da0101en
• Owner: jesussantana
• License: mit
• Created: 2021-05-02T09:44:25.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2021-05-03T08:26:07.000Z (almost 5 years ago)
• Last Synced: 2025-04-06T18:15:12.265Z (11 months ago)
• Topics: anova, correlation, data-analysis, model-evaluation, numpy, pandas, prepare-data, python, regression-models, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 508 KB
• Stars: 5
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# IBM Data Analysis with Python

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  

[![Made withJupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?style=for-the-badge&logo=Jupyter)](https://jupyter.org/try)  

## Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!.  

## You will learn how to:

- Import data sets

- Clean and prepare data for analysis

- Manipulate pandas DataFrame

- Summarize data

- Build machine learning models using scikit-learn

- Build data pipeline

## COURSE SYLLABUS: 

### Module 1 - Importing Datasets

- Learning Objectives

- Understanding the Domain

- Understanding the Dataset

- Python package for data science

- Importing and Exporting Data in Python

- Basic Insights from Datasets 

### Module 2 - Cleaning and Preparing the Data

- Identify and Handle Missing Values

- Data Formatting

- Data Normalization Sets

- Binning

- Indicator variables

### Module 3 - Summarizing the Data Frame

- Descriptive Statistics

- Basic of Grouping

- ANOVA

- Correlation

- More on Correlation

### Module 4 - Model Development

- Simple and Multiple Linear Regression

- Model Evaluation Using Visualization

- Polynomial Regression and Pipelines

- R-squared and MSE for In-Sample Evaluation

- Prediction and Decision Making  

### Module 5 - Model Evaluation

- Model  Evaluation

- Over-fitting, Under-fitting and Model Selection

- Ridge Regression

- Grid Search

- Model Refinement

## Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:

- Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

https://cognitiveclass.ai/courses/data-analysis-python