Data Science - Basic and Advanced Courses

Instructor: admin
Lectures: 73
Duration: 24 hours

Categories: Training Packages

:The program aims to

.Provide fundamental knowledge on data science applications and research to the trainees –

.Provide intensive training on utilizing data science tools and programming languages to the trainees –

.Ensure applying best practices of data science teaching and learning methods to the trainees –

Live Course 0/1
- Lecture 2.1
  
  Training Course Link
Introduction 0/2
- Lecture 3.1
  
  Objectives
- Lecture 3.2
  
  Training Manual
Session 1 : Introduction to Data Science 0/11
- Lecture 4.1
  
  Data Science General Overview
- Lecture 4.2
  
  ?What is Pattern Recognition
- Lecture 4.3
  
  Data Mining
- Lecture 4.4
  
  Data Mining Methodology
- Lecture 4.5
  
  Data Mining Techniques
- Lecture 4.6
  
  Types of Data
- Lecture 4.7
  
  Database Systems
- Lecture 4.8
  
  Data Warehouse
- Lecture 4.9
  
  Data Hierarchy
- Lecture 4.10
  
  Practical :Create Data Science Account
- Lecture 4.11
  
  Practical :Create Data Science Experiment
Session 2:Data Mining Tasks 0/11
- Lecture 5.1
  
  Definition of Data Mining Tasks
- Lecture 5.2
  
  Classification
- Lecture 5.3
  
  Regression
- Lecture 5.4
  
  Clustering
- Lecture 5.5
  
  Deviation/Anomaly Detection
- Lecture 5.6
  
  Pattern Discovery
- Lecture 5.7
  
  Association Rules Mining
- Lecture 5.8
  
  Challenging Issues
- Lecture 5.9
  
  Practical: Create Machine Learning Experiment
- Lecture 5.10
  
  Practical : Apply Classification Techniques
- Lecture 5.11
  
  Practical : Visualize and Discuss the Results
Session 3: Data Issues 0/8
- Lecture 6.1
  
  Dataset
- Lecture 6.2
  
  Objects
- Lecture 6.3
  
  Attributes
- Lecture 6.4
  
  Types of Data
- Lecture 6.5
  
  Data Quality
- Lecture 6.6
  
  Practical : Create Experiment Workspace
- Lecture 6.7
  
  Practical : Apply Preprocessing Techniques
- Lecture 6.8
  
  Practical : Visualize and Discuss the Results
Session 4 :Data Preprocessing 0/10
- Lecture 7.1
  
  ?Why Data Preprocessing is Important
- Lecture 7.2
  
  Data Preprocessing Tasks
- Lecture 7.3
  
  Correlation Analysis
- Lecture 7.4
  
  Data Transformation
- Lecture 7.5
  
  Data Reduction
- Lecture 7.6
  
  Data Compression
- Lecture 7.7
  
  Data Discretization
- Lecture 7.8
  
  Practical : Open Experiment Workspace
- Lecture 7.9
  
  Practical : Apply classification Techniques
- Lecture 7.10
  
  Practical : Visualize and Discuss the Results
Session 5 :Data Classification 0/11
- Lecture 8.1
  
  Classification and Prediction
- Lecture 8.2
  
  Classification Techniques
- Lecture 8.3
  
  Prediction Techniques
- Lecture 8.4
  
  Classification Task
- Lecture 8.5
  
  Classification Applications
- Lecture 8.6
  
  Overfitting in Classification
- Lecture 8.7
  
  Prediction Task
- Lecture 8.8
  
  Regression
- Lecture 8.9
  
  Practical: Data Prediction: Create Experiment Workspace
- Lecture 8.10
  
  Practical :Data Prediction : Apply Regression Techniques
- Lecture 8.11
  
  Practical : Data Prediction : Visualize and Discuss the Results
Session 6 :Data Clustering 0/10
- Lecture 9.1
  
  Clustering Approaches
- Lecture 9.2
  
  Clustering Applications
- Lecture 9.3
  
  Concepts of Clustering
- Lecture 9.4
  
  Types of Data in Clustering Analysis
- Lecture 9.5
  
  Measuring Similarity between Objects
- Lecture 9.6
  
  Types of Variables in Clustering Analysis
- Lecture 9.7
  
  Types of Clustering Approaches
- Lecture 9.8
  
  Practical Data Clustering :Create Experiment Workspace
- Lecture 9.9
  
  Practical Data Clustering :Apply Clustering Techniques
- Lecture 9.10
  
  Practical Data Clustering :Visualize and Discuss the Results
Session 7: Deep Learning 0/9
- Lecture 10.1
  
  Multiple Layers Neural Network
- Lecture 10.2
  
  Deep Learning Neural Network
- Lecture 10.3
  
  Learning Hierarchical Representations
- Lecture 10.4
  
  Types of Deep Learning Models
- Lecture 10.5
  
  Convolutional Neural Network
- Lecture 10.6
  
  Practical Assessment :Project Instructions
- Lecture 10.7
  
  Practical Assessment : Project Description
- Lecture 10.8
  
  Practical Assessment : Project Report
- Lecture 10.9
  
  Practical Assessment Project Assessment Criteria