CompTIA

DataX

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About This Course

Skills You’ll Get

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1

Introduction

  • About the DataX Certification
  • How This Course Is Organized
2

What Is Data Science?

  • Data Science
  • Data Science Best Practices
  • Summary
  • Exam Essentials
3

Mathematics and Statistical Methods

  • Calculus
  • Probability Distributions
  • Inferential Statistics
  • Linear Algebra
  • Summary
  • Exam Essentials
4

Data Collection and Storage

  • Common Data Sources
  • Data Ingestion
  • Data Storage
  • Managing the Data Lifecycle
  • Summary
  • Exam Essentials
5

Data Exploration and Analysis

  • Exploratory Data Analysis
  • Common Data Quality Issues
  • Summary
  • Exam Essentials
6

Data Processing and Preparation

  • Data Transformation
  • Data Enrichment and Augmentation
  • Data Cleaning
  • Handling Class Imbalance
  • Summary
  • Exam Essentials
7

Modeling and Evaluation

  • Types of Models
  • Model Design Concepts
  • Model Evaluation
  • Summary
  • Exam Essentials
8

Model Validation and Deployment

  • Model Validation
  • Communicating Results
  • Model Deployment
  • Machine Learning Operations (MLOps)
  • Summary
  • Exam Essentials
9

Unsupervised Machine Learning

  • Association Rules
  • Clustering
  • Dimensionality Reduction
  • Recommender Systems
  • Summary
  • Exam Essentials
10

Supervised Machine Learning

  • Linear Regression
  • Logistic Regression
  • Discriminant Analysis
  • Naive Bayes
  • Decision Trees
  • Ensemble Methods
  • Summary
  • Exam Essentials
11

Neural Networks and Deep Learning

  • Artificial Neural Networks
  • Deep Neural Networks
  • Summary
  • Exam Essentials
12

Natural Language Processing

  • Natural Language Processing
  • Text Preparation
  • Text Representation
  • Summary
  • Exam Essentials
13

Specialized Applications of Data Science

  • Optimization
  • Computer Vision
  • Summary
  • Exam Essentials

1

Mathematics and Statistical Methods

2

Data Collection and Storage

3

Data Exploration and Analysis

4

Data Processing and Preparation

5

Modeling and Evaluation

6

Model Validation and Deployment

7

Unsupervised Machine Learning

8

Supervised Machine Learning

9

Neural Networks and Deep Learning

10

Natural Language Processing

11

Specialized Applications of Data Science

DataX

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