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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!

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:

151,340 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Feb 27
Ends Jun 30

About this course

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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: Introductory
  • 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:Python (Programming Language), Basic Math, Data Analysis, Scikit-learn (Machine Learning Library), Pandas (Python Package), SciPy, NumPy, Data Visualization, Machine Learning

What you'll learn

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  • 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 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 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-fitting and Model Selection
  • Ridge Regression
  • Grid Search
  • Model Refinement

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

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