Skip to main contentSkip to Xpert Chatbot

IBM: Analyzing Data with Python

4.6 stars
100 ratings

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!

Analyzing Data with Python
5 weeks
2–4 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

158,342 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Nov 28

About this course

Skip 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!

Awards

Analyzing Data with Python

At a glance

  • Institution: IBM
  • Subject: Data Analysis & Statistics
  • Level: Intermediate
  • Prerequisites:

    Some Python Experience

  • Language: English
  • Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
  • Associated programs:
  • Associated skills:Scikit-learn (Machine Learning Library), Machine Learning, NumPy, Data Visualization, Pandas (Python Package), SciPy, Python (Programming Language), Data Analysis, Basic Math

What you'll learn

Skip 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

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

This course is part of IBM Data Science Professional Certificate Program

Learn more 
Expert instruction
10 skill-building courses
Self-paced
Progress at your own speed
1 year
3 - 6 hours per week

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX For Business.