Introduction to Big Data Concepts — Certification Brochure

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London Academy of Professional Training
Certificate
Introduction to Big Data Concepts
Workshop Level
REF: IT-DSA-W  •  IT Centres  •  Data Science & Artificial Intelligence
6Subjects 18Chapters 72Classes 440Total Marks 60%Pass Mark LifetimeValidity
At a Glance
ReferenceIT-DSA-W
AwardCertificate
Assessment440 marks — 264 to pass (60%)
PrerequisitesNone
ValidityLifetime
Awarding BodyLAPT — London Academy of Professional Training
Curriculum
1 Basic Statistical Analysis 3 chapters · 12 classes  65 marks
• 1 Chapter 1 — Understanding Basic Statistical Concepts — 4 classes
› 1.1 Exploring Descriptive Statistics: Mean, Median, and Mode
› 1.2 Understanding Data Dispersion: Variance and Standard Deviation
› 1.3 Visualizing Data: Histograms and Box Plots
› 1.4 Analyzing Relationships: Correlation vs Causation
• 2 Chapter 2 — Descriptive Statistics for Big Data — 4 classes
› 2.1 Understanding Key Descriptive Statistics Concepts
› 2.2 Exploring Measures of Central Tendency
› 2.3 Analyzing Measures of Dispersion in Big Data
› 2.4 Applying Descriptive Statistics to Real Big Data Sets
• 3 Chapter 3 — Inferential Statistics in Big Data Analysis — 4 classes
› 3.1 Understand the Role of Inferential Statistics in Big Data
› 3.2 Explore Key Inferential Statistical Methods
› 3.3 Apply Hypothesis Testing in Data Analysis
› 3.4 Interpret Statistical Evidence from Big Data
2 Big Data Storage Solutions 3 chapters · 12 classes  65 marks
• 1 Understanding Big Data Storage Fundamentals — 4 classes
› 1.1 Exploring Key Concepts in Big Data Storage
› 1.2 Identifying Components of Big Data Storage Systems
› 1.3 Analyzing Data Storage Methodologies
› 1.4 Applying Big Data Storage Solutions to Real-World Scenarios
• 2 Exploring Distributed File Systems and Databases — 4 classes
› 2.1 Understanding the Basics of Distributed File Systems
› 2.2 Examining Key Characteristics of Distributed Databases
› 2.3 Comparing Use Cases for File Systems and Databases
› 2.4 Applying Distributed Storage Concepts to Real-World Scenarios
• 3 Implementing Big Data Storage Solutions in Practice — 4 classes
› 3.1 Understanding the Architecture of Big Data Storage
› 3.2 Exploring Data Storage Technologies and Their Applications
› 3.3 Configuring Big Data Storage Solutions for Optimal Performance
› 3.4 Evaluating and Selecting the Right Storage Solution for Big Data Needs
3 Big Data Tools and Platforms 3 chapters · 12 classes  65 marks
• 1 Understanding Big Data Ecosystems — 4 classes
› 1.1 Exploring the Components of Big Data Ecosystems
› 1.2 Distinguishing Big Data Tools and Technologies
› 1.3 Analyzing the Architecture of Big Data Platforms
› 1.4 Applying Big Data Solutions to Real-World Scenarios
• 2 Exploring Big Data Tools and Technologies — 4 classes
› 2.1 Discovering Key Big Data Platforms
› 2.2 Understanding Distributed Computing with Hadoop
› 2.3 Exploring Cloud-Based Data Solutions
› 2.4 Analyzing Data with Apache Spark
• 3 Leveraging Cloud Platforms for Big Data Solutions — 4 classes
› 3.1 Understanding Cloud Infrastructure for Big Data
› 3.2 Exploring Key Cloud Service Providers for Big Data
› 3.3 Integrating Big Data Tools with Cloud Platforms
› 3.4 Implementing Big Data Solutions in the Cloud
4 Data Collection and Preprocessing 3 chapters · 12 classes  90 marks
• 1 Understanding Data Collection Techniques and Tools — 4 classes
› 1.1 Exploring Data Collection Methods
› 1.2 Identifying Data Sources
› 1.3 Utilizing Data Collection Tools
› 1.4 Applying Data Collection Techniques
• 2 Data Cleaning and Transformation Processes — 4 classes
› 2.1 Understand the Role and Significance of Data Cleaning
› 2.2 Identify and Handle Missing Data
› 2.3 Detect and Correct Data Errors and Anomalies
› 2.4 Apply Data Transformation Techniques for Analysis
• 3 Implementing Data Preprocessing Techniques — 4 classes
› 3.1 Understanding Data Cleaning Processes
› 3.2 Applying Data Transformation Techniques
› 3.3 Implementing Feature Scaling Methods
› 3.4 Executing Data Integration and Reduction Strategies
5 Designing Data Workflows 3 chapters · 12 classes  90 marks
• 1 Chapter 1 — Understanding Data Workflow Principles — 4 classes
› 1.1 Exploring the Fundamentals of Data Workflows
› 1.2 Identifying Components of Effective Data Workflows
› 1.3 Analyzing Data Flow Patterns and Structures
› 1.4 Applying Workflow Principles to Real-World Scenarios
• 2 Chapter 2 — Tools and Techniques for Data Workflow Design — 4 classes
› 2.1 Exploring Essential Data Workflow Tools
› 2.2 Understanding Data Transformation Techniques
› 2.3 Implementing Data Collection Strategies
› 2.4 Optimising Workflow Efficiency through Automation
• 3 Chapter 3 — Implementing and Optimizing Data Workflows — 4 classes
› 3.1 Understanding Data Workflows in Big Data
› 3.2 Building Efficient Data Pipelines
› 3.3 Applying Optimization Techniques to Data Workflows
› 3.4 Evaluating and Iterating Data Workflow Performance
6 Introduction to Big Data 3 chapters · 12 classes  65 marks
• 1 Understanding the Basics of Big Data — 4 classes
› 1.1 Exploring the Fundamentals of Big Data
› 1.2 Identifying Key Characteristics and Components of Big Data
› 1.3 Understanding Big Data’s Importance and Impact
› 1.4 Applying Basic Big Data Concepts to Real-World Examples
• 2 Big Data Technologies and Tools — 4 classes
› 2.1 Exploring Big Data Frameworks: Hadoop and Spark
› 2.2 Understanding Data Storage Options: NoSQL and Data Lakes
› 2.3 Analyzing Data Processing Techniques: Batch vs. Stream Processing
› 2.4 Applying Big Data Tools: Hands-On with Popular Software
• 3 Applications and Implications of Big Data Analytics — 4 classes
› 3.1 Exploring Real-World Applications of Big Data Analytics
› 3.2 Understanding the Ethical Implications of Big Data
› 3.3 Examining Privacy Concerns in Big Data Usage
› 3.4 Evaluating the Impact of Big Data on Decision-Making
LAPT — 85 Great Portland Street, W1W 7LT, London, United Kingdom
info@lapt.org  •  +44 7513 283044  •  lapt.org		 UKPRN 10067351  •  Company 4984798
Curriculum current at the date of issue.