Exam Preparation
DSFData Science Fundamentals
التقييم:
الإنجليزي
متوسط
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لتحميل الكتيب
نبذة عن الدورة التدريبية
This ISACA course introduces you to data science, a growing and rapidly changing field that is becoming increasingly vital to business survival, job stability, and national security. Data science demands skilled professionals who possess the knowledge, skills, and ability to address the evolving threat landscape.
المخرجات الرئيسية
1
Identify key concepts and terminology in data sciences.
2
Define the key concepts, roles and domains of data sciences.
3
Explain legal, regulatory and ethical considerations regarding use of data.
4
Explain activities performed to prepare data for analysis, categorization and modeling.
5
Distinguish among types of visualization and reporting tools.
Information Systems Audit and Control Association
ISACA was incorporated in 1969 by a small group of individuals who recognized a need for a centralized source of information and guidance in the growing field of auditing controls for computer systems. Today, ISACA serves 140,000 professionals in 180 countries. As an independent, nonprofit, global association, ISACA engages in the development, adoption and use of globally accepted, industry-leading knowledge and practices for information systems. Previously known as the Information Systems Audit and Control Association, ISACA now goes by its acronym only, to reflect the broad range of IT governance professionals it serves.
محتوى الدورة التدريبية
Part 1
Session 1 – Data Characteristics: Basic Concepts
→ 1.1.1 What Is Data Science?
→ 1.1.2 Defining Big Data
→ 1.1.3 The Evolution of Big Data
→ 1.1.4 What Is Data?
→ 1.1.5 Raw Data vs. Contextualized Data
→ 1.1.6 Difference Between Data Statistics and Analytics
→ 1.1.7 Data Types
→ 1.1.8 ASCII and Unicode
→ 1.1.2 Defining Big Data
→ 1.1.3 The Evolution of Big Data
→ 1.1.4 What Is Data?
→ 1.1.5 Raw Data vs. Contextualized Data
→ 1.1.6 Difference Between Data Statistics and Analytics
→ 1.1.7 Data Types
→ 1.1.8 ASCII and Unicode
Session 2 – Use of Data in Information Systems
→ 1.1.9 DIKW Pyramid
→ 1.1.10 Metadata
→ 1.1.11 Data Flows and Data Diagrams
→ 1.1.12 Applicability of Data to Business
→ 1.1.10 Metadata
→ 1.1.11 Data Flows and Data Diagrams
→ 1.1.12 Applicability of Data to Business
Session 3 – Data Structures
→ 1.2.1 Characteristics of Data Structures
→ 1.2.2 Linear Structures
→ 1.2.3 Tree Structures
→ 1.2.4 Index and Pointer Structures
→ 1.2.2 Linear Structures
→ 1.2.3 Tree Structures
→ 1.2.4 Index and Pointer Structures
Part 2
Session 4 – Statistical Analysis
→ 1.3.1 Populations and Samples
→ 1.3.2 Statistical Modeling
→ 1.3.3 Key Performance Indicators (KPIs)
→ 1.3.2 Statistical Modeling
→ 1.3.3 Key Performance Indicators (KPIs)
Session 5 – Types of Databases
→ 2.1.1 ntroduction
→ 2.1.2 Operational Databases
→ 2.1.3 Relational vs. Non-Relational Databases
→ 2.1.4 Autonomous Databases
→ 2.1.2 Operational Databases
→ 2.1.3 Relational vs. Non-Relational Databases
→ 2.1.4 Autonomous Databases
Session 6 – Data Management
→ 2.1.5 Common Database Management Systems
→ 2.1.6 Data Lakes
→ 2.1.7 Data Warehouse
→ 2.1.8 Data Management Platforms
→ 2.1.6 Data Lakes
→ 2.1.7 Data Warehouse
→ 2.1.8 Data Management Platforms
Part 3
Session 7 – Governance
→ 2.2 Governance
→ 2.2.1 Data Governance
→ 2.2.2 Legal and Regulatory Compliance
→ 2.2.1 Data Governance
→ 2.2.2 Legal and Regulatory Compliance
Session 8 – Data Governance Roles
→ 2.2.3 Data Ethics
→ 2.2.4 Data Roles and Responsibilities
→ 2.2.4 Data Roles and Responsibilities
Session 9 – Access and Protection
→ 2.3 Access and Protection
→ 2.3.1 Data Accessibility and Protection
→ 2.3.2 Managing Permissions
→ 2.3.3 Third-Party and Vendor Access and Management
→ 2.3.4 Data Obfuscation
→ 2.3.5 Tokenization
→ 2.3.6 Encryption
→ 2.3.1 Data Accessibility and Protection
→ 2.3.2 Managing Permissions
→ 2.3.3 Third-Party and Vendor Access and Management
→ 2.3.4 Data Obfuscation
→ 2.3.5 Tokenization
→ 2.3.6 Encryption
Part 4
Session 10 – Data Discovery and Collection
3.1 Data Discovery and Goal Identification
→ 3.1.1 Requirements and Resources
→ 3.1.2 Formulation of Hypotheses
→ 3.2 Data Collection
→ 3.2.1 Database Queries
→ 3.2.2 Data Collection Methods
→ 3.1.1 Requirements and Resources
→ 3.1.2 Formulation of Hypotheses
→ 3.2 Data Collection
→ 3.2.1 Database Queries
→ 3.2.2 Data Collection Methods
Session 11 – Data Classification
→ 3.3 Data Classification
→ 3.3.1 Data Cleansing
→ 3.3.2 Data Clustering
→ 3.3.3 Data Tagging
→ 3.3.4 Data Governance Tools
→ 3.3.1 Data Cleansing
→ 3.3.2 Data Clustering
→ 3.3.3 Data Tagging
→ 3.3.4 Data Governance Tools
Session 12 - Data Processing Concepts
→ 3.4.1 Introduction
→ 3.4.2 Exploratory Data Analysis
→ 3.4.3 Model Development Tools
→ 3.4.4 Statistical Analysis Tools
→ 3.4.5 Business Analytics
→ 3.4.2 Exploratory Data Analysis
→ 3.4.3 Model Development Tools
→ 3.4.4 Statistical Analysis Tools
→ 3.4.5 Business Analytics
Part 5
Session 13 – Data Processing with Machine Learning, Part 1
→ 3.4.6 Machine Learning
Session 14 – Data Processing with Machine Learning, Part 2
→ 3.4.6 Machine Learning
Session 15 – Communication of Results
→ 3.5.1 Reporting Techniques
→ 33.5.2 Reporting Tools
→ 33.5.2 Reporting Tools
على من يجب الحضور؟
This highly practical and interactive course has been specifically designed for
→ IT professionals with a familiarity of basic information technology and information systems concepts, who are:
→ New to data sciences
→ Interested in entering the field of data sciences
→ Interested in the ISACA Data Sciences Certification
→ Students and recent graduates interested in the field of data sciences
→ Individuals with zero to three years data sciences experience
الدورات ذات الصلة
التعليمات
AR What language will the course be taught in and what level of English do I need to take part in an LEORON training program?
AR Most of our public courses are delivered in English language. You need to be proficient in English to be able to fully participate in the workshop and network with other delegates. For in-house courses we have the capability to train in Arabic, Dutch, German and Portuguese.
AR Are LEORON Public courses certified by an official body/organization?
AR LEORON Institute partners with 20+ international bodies and associations.We also award continuing professional development credits (CPE/PDUs) for:1. NASBA (National Association of State Boards of Accountancy) 2. Project Management Institute PDUs 3. CISI credits 4. GARP credits 5. HRCI recertification credits 6. SHRM recertification credits
AR What is the deadline for registering to a public course?
AR The deadline to register for a public course is 14 days before the course starts. Kindly note that occasionally we do accept late registrations as well, but this needs to be confirmed with the project manager of the training program or with our registration desk that can be reached at +91 4 95 5711 or [email protected].
AR What does the course fee cover?
AR The course fee covers a premium training experience in a 5-star hotel, learning materials, lunches & refreshments, and for some courses, the certification fee and membership with the accrediting bodies.
AR Does LEORON give discounts?
AR Yes, we can provide discounts for group bookings. If you would like to discuss a discount on a corporate level, we will be happy to talk to you.