• Length:
    5 Weeks
  • Effort:
    2–4 hours per week
  • Price:

    FREE
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  • Institution
    IBM
  • Subject:
    Data Analysis & Statistics
  • Level:
    Introductory
  • Language:
    English
  • Video Transcript:
    English

Associated Programs:

Prerequisites

Some Python Experience

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About this course

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LEARN TO ANALYZE DATA WITH PYTHON

Learn how to analyze data using Python. 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!

What you'll learn

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You will learn how to:

  • Import data sets
  • Clean and prepare data for analysis
  • Manipulate pandas DataFrame
  • Summarize data
  • Build machine learning models using scikit-learn
  • Build data pipelines
  • Data Analysis with Python is delivered through lecture, hands-on labs, and assignment.

It includes following parts:

Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. 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.

Syllabus

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COURSE SYLLABUS

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

Meet your instructors

IBM
Joseph Santarcangelo
PhD., Data Scientist
IBM

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