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About This Certification

Who Is This For?

This workshop is ideal for individuals in early-career roles or those transitioning from other fields who are interested in entering the data science and AI industry. No previous experience with big data is necessary; however, an enthusiasm for technology and data-driven decision-making is beneficial.

Course Curriculum

6 subjects • 18 chapters • 72 classes

Designing Data Workflows

3 chapters • 12 classes • 100 marks • 6h

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

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

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

Big Data Tools and Platforms

3 chapters • 12 classes • 75 marks • 6h

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

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

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

Big Data Storage Solutions

3 chapters • 12 classes • 75 marks • 6h

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

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

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

Basic Statistical Analysis

3 chapters • 12 classes • 75 marks • 7h

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

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

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

Data Collection and Preprocessing

3 chapters • 12 classes • 100 marks • 8h

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

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

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

Introduction to Big Data

3 chapters • 12 classes • 75 marks • 7h

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

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

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

Assessment & Grading

Assessment Methods

Written Examination
Practical Assignment
Portfolio Assessment

Theory

70%

Practical

20%

Project

10%

Workshop N/A

IT-DSA-W
Workshop Stage
500 total marks
Pass: 300 (60%)
Validity: Lifetime
IT Industry

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Introduction to Big Data Concepts

About This Certification

Who Is This For?

Course Curriculum

Assessment & Grading

Assessment Methods

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