Data Analysis with Python
- Offered byCognitive Class
Data Analysis with Python at Cognitive Class Overview
Duration | 3 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Beginner |
Credential | Certificate |
Data Analysis with Python at Cognitive Class Highlights
- Gain expertise on skills like Python, Data Science, Data Analysis
Data Analysis with Python at Cognitive Class Course details
- Import data sets
- Clean and prepare data for analysis
- Manipulate pandas DataFrame
- Summarize data
- Build machine learning models using scikit-learn
- Build data pipelines
- In this course you will learn about: Data Acquisition How to Obtain Basic Insight From a Dataset Data
- 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
- 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
Data Analysis with Python at Cognitive Class Curriculum
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