General Data Protection Regulation (GDPR) and Cyber Security for Data Protection Officer

Academy for Health & Fitness

CPD Accredited Digital certificate

Digital certificate - Included

CPD Accredited Hard copy certificate

Hard copy certificate - Included

General Data Protection Regulation (GDPR) & Cyber Security Management

Hard copy certificate - Included

Assessment

Included in course price

Start your learning journey straightaway with our Data Protection Training!

Data Protection premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of the Data Protection is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.

★★★ Course Curriculum of General Data Protection Regulation (GDPR) and Cyber Security for Data Protection Officer Bundle ★★★

➽ Course 01: General Data Protection Regulation (GDPR) & Cyber Security Management

GDPR UK Training

• The Purpose and Principles of the UK GDPR
• The Legal Foundations of GDPR
• The Governance of GDPR
• Protecting Personal Data
• Exemptions to GDPR
• International Transactions and National Security
  

Cyber Security Awareness Training

• Introduction to Cyber Security
• Cyber Attacks
• Cybercrimes
• Cyber Security and Data Breaches
• Password Management
• Internet Browsing
• Office and Remote Work Security
• Device Management
• Preventative Measures

➽ Course 02: Understanding Data Protection and Data Security

• GDPR Basics
• GDPR Explained
• Lawful Basis for Preparation
• Rights and Breaches
• Responsibilities and Obligations

➽ Course 03: Data Science & Machine Learning with Python

• Course Overview & Table of Contents
• Introduction to Machine Learning - Part 1 - Concepts, Definitions and Types
• Introduction to Machine Learning - Part 2 - Classifications and Applications
• System and Environment Preparation - Part 1
• System and Environment Preparation - Part 2
• Learn Basics of Python - Assignment
• Learn Basics of Python - Assignment
• Learn Basics of Python - Functions
• Learn Basics of Python - Data Structures
• Learn Basics of NumPy - NumPy Array
• Learn Basics of NumPy - NumPy Data
• Learn Basics of NumPy - NumPy Arithmetic
• Learn the Basics of Matplotlib
• Learn Basics of Pandas - Part 1
• Learn Basics of Pandas - Part 2
• Understanding the CSV data file
• Load and Read CSV data files using the Python Standard Library
• Load and Read CSV data files using NumPy
• Load and Read CSV data files using Pandas
• Dataset Summary - Peek, Dimensions and Data Types
• Dataset Summary – Class Distribution and Data Summary
• Dataset Summary - Explaining Correlation
• Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
• Dataset Visualization - Using Histograms
• Dataset Visualization - Using Density Plots
• Dataset Visualization - Box and Whisker Plots
• Multivariate Dataset Visualization - Correlation Plots
• Multivariate Dataset Visualization - Scatter Plots
• Data Preparation (Pre-Processing) - Introduction
• Data Preparation - Re-scaling Data - Part 1
• Data Preparation - Re-scaling Data - Part 2
• Data Preparation - Standardizing Data - Part 1
• Data Preparation - Standardizing Data - Part 2
• Data Preparation - Normalizing Data
• Data Preparation - Binarizing Data
• Feature Selection - Introduction
• Feature Selection - Uni-variate Part 1 - Chi-Squared Test
• Feature Selection - Uni-variate Part 2 - Chi-Squared Test
• Feature Selection - Recursive Feature Elimination
• Feature Selection - Principal Component Analysis (PCA)
• Feature Selection - Feature Importance
• Refresher Session - The Mechanism of Re-sampling, Training and Testing
• Algorithm Evaluation Techniques - Introduction
• Algorithm Evaluation Techniques - Train and Test Set
• Algorithm Evaluation Techniques - K-Fold Cross Validation
• Algorithm Evaluation Techniques - Leave One Out Cross Validation
• Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
• Algorithm Evaluation Metrics - Introduction
• Algorithm Evaluation Metrics - Classification Accuracy
• Algorithm Evaluation Metrics - Log Loss
• Algorithm Evaluation Metrics - Area Under ROC Curve
• Algorithm Evaluation Metrics - Confusion Matrix
• Algorithm Evaluation Metrics - Classification Report
• Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
• Algorithm Evaluation Metrics - Mean Absolute Error
• Algorithm Evaluation Metrics - Mean Square Error
• Algorithm Evaluation Metrics - R Squared
• Classification Algorithm Spot Check - Logistic Regression
• Classification Algorithm Spot Check - Linear Discriminant Analysis
• Classification Algorithm Spot Check - K-Nearest Neighbors
• Classification Algorithm Spot Check - Naive Bayes
• Classification Algorithm Spot Check - CART
• Classification Algorithm Spot Check - Support Vector Machines
• Regression Algorithm Spot Check - Linear Regression
• Regression Algorithm Spot Check - Ridge Regression
• Regression Algorithm Spot Check - Lasso Linear Regression
• Regression Algorithm Spot Check - Elastic Net Regression
• Regression Algorithm Spot Check - K-Nearest Neighbors
• Regression Algorithm Spot Check - CART
• Regression Algorithm Spot Check - Support Vector Machines (SVM)
• Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
• Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
• Pipelines: Data Preparation and Data Modelling
• Pipelines: Feature Selection and Data Modelling
• Performance Improvement: Ensembles - Voting
• Performance Improvement: Ensembles - Bagging
• Performance Improvement: Ensembles - Boosting
• Performance Improvement: Parameter Tuning using Grid Search
• Performance Improvement: Parameter Tuning using Random Search
• Export, Save and Load Machine Learning Models: Pickle
• Export, Save and Load Machine Learning Models: Joblib
• Finalizing a Model - Introduction and Steps
• Finalizing a Classification Model - The Pima Indian Diabetes Dataset
• Quick Session: Imbalanced Data Set - Issue Overview and Steps
• Iris Dataset: Finalizing Multi-Class Dataset
• Finalizing a Regression Model - The Boston Housing Price Dataset
• Real-time Predictions: Using the Pima Indian Diabetes Classification Model
• Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
• Real-time Predictions: Using the Boston Housing Regression Model

=========>>>>> And 27 More Courses <<<<<=========

How will I get my Certificate?

After successfully completing the course, you will be able to order your Certificates as proof of your achievement.

• PDF Certificate: Free (Previously it was £12.99*11 = £143)
• CPD Hard Copy Certificate: Free (For The First Course: Previously it was £29.99)

This bundle is ideal for:

• Students seeking mastery in this field
• Professionals seeking to enhance their skills
• Anyone who is passionate about this topic

This Data Protection doesn't require prior experience and is suitable for diverse learners.

This Data Protection bundle will allow you to kickstart or take your career in the related sector to the next stage.

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