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IBM: Analyzing Data with Python

Course from IBM (IBM)

IBM: Analyzing Data with Python

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In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!

159,410 learners enrolled

What you'll learn

  • Import data 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, analyze, and visualize dataset
  • Build machine-learning models and make predictions with scikit-learn

About this course

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

LEARN TO ANALYZE DATA WITH PYTHON

Learn how to analyze data 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 will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

Curriculum

5 Weeks, 2-4 hours per week

Language

English

Transcripts

اَلْعَرَبِيَّةُ

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English

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हिन्दी

Bahasa Indonesia

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Kiswahili

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中文

Module 1 – Importing Data Sets

  • The Problem
  • Understanding the Data
  • Python Packages for Data Science
  • Importing and Exporting Data in Python
  • Getting Started Analyzing Data in Python
  • Accessing Databases with Python
  • Module Summary
  • Practice Quiz: Importing Data sets
  • Hands-on Lab: Importing Data sets
  • Graded Quiz: Importing Data sets

Module 2 – Data Wrangling

  • Pre-processing Data in Python
  • Dealing with Missing Values in Python
  • Data Formatting in Python
  • Data Normalization in Python
  • Binning in Python
  • Turning Categorical Variables into Quantitative Variables in Python
  • Hands-on Lab: Data Wrangling - Used Cars Pricing
  • Hands-on Lab: Data Wrangling - Laptop Pricing
  • Module Summary
  • Practice Quiz: Data Wrangling
  • Graded Quiz: Data Wrangling

Module 3 - Exploratory Data Analysis

  • Exploratory Data Analysis
  • Descriptive Statistics
  • GroupBy in Python
  • Correlation
  • Correlation - Statistics
  • Hands-on Lab: Exploratory Data Analysis - Laptop Pricing
  • Hands-on Lab: Exploratory Data Analysis - Used Car Pricing
  • Module Summary
  • Practice Quiz: Exploratory Data Analysis
  • Graded Quiz: Exploratory Data Analysis

Module 4 – Model Development

  • Model Development
  • Linear Regression and Multiple Linear Regression
  • Model Evaluation using Visualization
  • Polynomial Regression and Pipelines
  • Measures for In-Sample Evaluation
  • Prediction and Decision Making
  • Practice Quiz: Model Development
  • Hands-on Lab: Model Development - Used Car Pricing
  • Hands-on Lab: Model Development - Laptop Pricing
  • Module Summary
  • Graded Quiz: Model Development

Module 5 - Model Evaluation

  • Model Evaluation and Refinement
  • Overfitting, Underfitting, and Model Selection
  • Ridge Regression Introduction
  • Ridge Regression
  • Grid Search
  • Practice Quiz: Model Evaluation and Refinement
  • Hands-on Lab: Model Evaluation and Refinement - Used Cars Pricing
  • Hands-on Lab: Model Evaluation and Refinement - Laptop Pricing
  • Module Summary
  • Graded Quiz: Model Evaluation and Refinement

Module 6 - Final Assignment

  • Project Scenario
  • Hands-on Lab for Final Project - Data Analytics for House Pricing Data Set
  • Peer Review
  • Cheat Sheet: Data Analysis for Python
  • Final Exam Instructions
  • Final Exam
  • Course Rating and Feedback
  • Course Rating
  • Badge
  • Claim your badge here
  • Acknowledgments
  • Congrats and Next Steps
  • Thanks from the Course Team

Awards

Analyzing Data with Python

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