How To Replace A Data Value With Another In Python

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Analyzing Data with Python

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LEARN TO ANALYZE Information WITH PYTHON

Acquire how to analyze information using Python in this introductory course. You will go from understanding the basics of Python to exploring many different types of data through lecture, hands-on labs, and assignments. You lot will learn how to prepare data for assay, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

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Analyzing Data with Python

At a glance

Institution: IBM
Subject: Information Analysis & Statistics
Level: Introductory
Prerequisites:
Some Python Feel
Language: English

Video Transcript: English language
Associated programs:
- Professional Certificate in Python Data Scientific discipline
- Professional person Certificate in IBM Data Science
- Professional Document in Data Annotator

What you'll larn

Skip What you'll learn

How to import information sets, clean and prepare data for analysis, summarize data, and build data pipelines
Use Pandas DataFrames, Numpy multidimensional arrays, and SciPy libraries to work with various datasets
Load, manipulate, clarify, and visualize datasets with pandas, an open-source library
Build machine-learning models and make predictions with scikit-learn, another open-source library

It includes following parts:

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

Module i - 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 Information Frame

Descriptive Statistics
Basic of Group
ANOVA
Correlation
More than on Correlation

Module iv - Model Evolution

Elementary and Multiple Linear Regression
Model EvaluationUsingVisualization
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-plumbing fixtures and Model Selection
Ridge Regression
Filigree Search
Model Refinement

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Ways to take this course

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Shareable certificate upon completion
Graded assignments and exams

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