About This CourseLearn 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! Topics covered:
- Importing Data sets
- Cleaning the Data
- Data frame manipulation
- Summarizing the Data
- Building machine learning models
- Building data pipelines
- Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. 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.
Recommended skills prior to taking this course
- Learning Objectives
- Understanding the Domain
- Understanding the Dataset
- Python package for data science
- Importing and Exporting Data in Python
- Basic Insights from Datasets
- Identify and Handle Missing Values
- Data Formatting
- Data NormalizationSets
- Indicator variables
- Descriptive Statistics
- Basic of Grouping
- Correlation 2
- 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
- Model Evaluation
- Over Fitting, Under fitting and Model Selection
- Ridge Regression
- Grid Search
- Model Refinement
- This course is free.
- It is self-paced.
- It can be taken at any time.
- It can be audited as many times as you wish.
- There is only ONE chance to pass the course, but multiple attempts per question
- Python programming, Statistics
- Python programming
Mahdi Noorian Ph.D.
Mahdi Noorian is a Postdoctoral Fellow at the Laboratory for Systems, Software and Semantics (LS3) of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick. As a Data Scientist, he is interested in application of machine learning, data mining, optimization, and semantic data analysis for big data to solve the real-world problems.