Data Science with Scala by IBM

Scala for Data Science

• Lesson 1 Learning Objectives

  • Learning Objectives

• Lesson 2 Basic Statistics and Data Types

  37:22Preview

  • 2.1 Learning Objectives

  • 2.2 Vectors and Labelled Points

    07:06

  • 2.3 Local and Distributed Matrices

    08:48

  • 2.4 Summary Statistics, Correlation, and Random Data

    05:30

  • 2.5 Sampling

    06:49

  • 2.6 Hypothesis Testing

    09:09

• Lesson 3 Preparing Data

  44:34Preview

  • 3.1 Learning Objectives

  • 3.2 Statistics, Random Data and Sampling on Data Frames

    12:16

  • 3.3 Handling Missing Data and Imputing Values

    11:07

  • 3.4 Transformers and Estimators

    08:39

  • 3.5 Data Normalization

    05:00

  • 3.6 Identifying Outliers

    07:32

• Lesson 4 Feature Engineering

  28:19Preview

  • 4.1 Learning Objectives

  • 4.2 Feature Vectors

    03:28

  • 4.3 Categorical Features

    06:53

  • 4.4 Using Explode, User Defined Functions, and Pivot

    04:18

  • 4.5 Principal Component Analysis(PCA) in Feature Engineering

    09:12

  • 4.6 RFormulas

    04:28

• Lesson 5 Fitting a Model

  40:29

  • 5.1 Learning Objectives

  • 5.2 Decision Trees

    08:45

  • 5.3 Random Forests

    07:15

  • 5.4 Gradient-Boosting Trees

    07:10

  • 5.5 Linear Methods

    06:15

  • 5.6 Evaluation

    11:04

• Lesson 6 Pipeline and Grid Search

  21:34

  • 6.1 Learning Objectives

  • 6.2 Predicting Grant Applications: Introduction

    04:27

  • 6.3 Predicting Grant Applications: Creating Features

    04:52

  • 6.4 Predicting Grant Applications: Building a Pipeline

    04:16

  • 6.5 Predicting Grant Applications: Cross Validation and Model Tuning

    03:47

  • 6.6 Predicting Grant Applications: Wrapping Up

    04:12

  • Unlocking IBM Certificate