Certificate in Data Analysis & Visualization — Certification Brochure

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London Academy of Professional Training
Certificate
Certificate in Data Analysis & Visualization
Foundation Level
REF: IT-DSA-F  •  IT Centres  •  Data Science & Artificial Intelligence
6Subjects 24Chapters 115Classes 450Total Marks 60%Pass Mark LifetimeValidity
At a Glance
ReferenceIT-DSA-F
AwardCertificate
Assessment450 marks — 270 to pass (60%)
PrerequisitesNone
ValidityLifetime
Awarding BodyLAPT — London Academy of Professional Training
Curriculum
1 Basic Statistics for Data Analysis 4 chapters · 20 classes  90 marks
• 1 Chapter 1 — Fundamentals of Descriptive Statistics — 5 classes
› 1.1 Understanding the Basics of Descriptive Statistics
› 1.2 Exploring Measures of Central Tendency: Mean, Median, and Mode
› 1.3 Analyzing Data Spread with Range, Variance, and Standard Deviation
› 1.4 Visualizing Data: Creating and Interpreting Histograms and Box Plots
› 1.5 Applying Descriptive Statistics to Real-World Data Sets
• 2 Chapter 2 — Exploring Probability Distributions — 5 classes
› 2.1 Understanding Probability Distributions: An Introduction
› 2.2 Analyzing Uniform Distribution Patterns
› 2.3 Exploring Normal Distribution Characteristics
› 2.4 Investigating Binomial Distributions in Practical Scenarios
› 2.5 Applying Probability Distributions to Real-World Data
• 3 Chapter 3 — Inferential Statistics and Hypothesis Testing — 5 classes
› 3.1 Understanding Inferential Statistics: From Sample to Population
› 3.2 Exploring Sampling Distributions: The Central Limit Theorem
› 3.3 Formulating Hypotheses: Null and Alternative Hypotheses
› 3.4 Conducting Hypothesis Tests: P-Values and Significance Levels
› 3.5 Applying Inferential Techniques: Confidence Intervals in Practice
• 4 Chapter 4 — Correlation and Regression Analysis — 5 classes
› 4.1 Understanding Correlation Coefficients
› 4.2 Exploring Types of Correlation
› 4.3 Calculating Linear Regression Parameters
› 4.4 Interpreting Regression Output
› 4.5 Applying Regression Analysis in Real-world Scenarios
2 Data Reporting and Communication 4 chapters · 15 classes  90 marks
• 1 Chapter 1 — Fundamentals of Data Reporting — 5 classes
› 1.1 Understanding the Basics of Data Reporting
› 1.2 Identifying Key Components of Effective Data Reports
› 1.3 Exploring Different Formats and Structures for Data Reporting
› 1.4 Recognizing the Role of Audience in Data Communication
› 1.5 Crafting Clear and Concise Data Narratives
• 2 Chapter 2 — Designing Effective Data Visualizations — 5 classes
› 2.1 Understanding the Principles of Effective Data Visualizations
› 2.2 Identifying Appropriate Chart Types for Your Data
› 2.3 Applying Design Best Practices to Enhance Clarity
› 2.4 Utilizing Color and Fonts to Improve Readability
› 2.5 Creating Interactive Visualizations for Enhanced Engagement
• 3 Chapter 3 — Storytelling with Data — 5 classes
› 3.1 Understanding the Elements of Data Storytelling
› 3.2 Crafting Narratives for Data Insights
› 3.3 Visualizing Data to Support Your Story
› 3.4 Engaging Your Audience with Compelling Data Narratives
› 3.5 Applying Storytelling Techniques to Real Data Scenarios
• 4 Chapter 4 — Tools and Technologies for Data Communication
3 Data Visualisation Techniques 4 chapters · 20 classes  90 marks
• 1 Understanding Data Visualization Fundamentals — 5 classes
› 1.1 Exploring the Purpose and Power of Data Visualization
› 1.2 Recognizing Key Components of Effective Visualizations
› 1.3 Identifying Different Types of Data Charts and Graphs
› 1.4 Interpreting Data through Basic Graphical Representations
› 1.5 Developing Skills to Choose Appropriate Visual Formats
• 2 Data Visualization Tools and Techniques — 5 classes
› 2.1 Understanding the Basics of Data Visualization
› 2.2 Exploring Key Tools for Visualization
› 2.3 Creating Simple Charts with Spreadsheets
› 2.4 Utilizing Software for Advanced Visualizations
› 2.5 Applying Visualization Techniques to Tell a Story
• 3 Design Principles for Effective Visual Communication — 5 classes
› 3.1 Understanding Key Design Principles in Visualization
› 3.2 Identifying the Role of Colour and Contrast in Visual Communication
› 3.3 Utilizing Space and Layout for Clarity and Impact
› 3.4 Applying Typography for Enhanced Readability
› 3.5 Evaluating and Refining Visualizations for Effective Communication
• 4 Advanced Visualization Strategies and Case Studies — 5 classes
› 4.1 Exploring Complex Data Through Advanced Charts
› 4.2 Utilizing Interactive Visualizations for Dynamic Data Representation
› 4.3 Integrating Multivariate Visual Techniques for In-Depth Analysis
› 4.4 Analyzing Case Studies: Effective Strategies in Real-world Scenarios
› 4.5 Crafting Storytelling with Data to Enhance Impact
4 Interpreting Data Visualisations 4 chapters · 20 classes  90 marks
• 1 Understanding Basic Data Visualization Concepts — 5 classes
› 1.1 Exploring the Purpose of Data Visualizations
› 1.2 Identifying Key Components of a Data Visualization
› 1.3 Analyzing Common Types of Data Visualizations
› 1.4 Interpreting Data Trends and Patterns
› 1.5 Evaluating the Effectiveness of Visual Representations
• 2 Analyzing Common Data Visualizations — 5 classes
› 2.1 Understanding Bar and Column Charts: Comparing Categories
› 2.2 Interpreting Line Charts: Analyzing Trends Over Time
› 2.3 Exploring Pie Charts: Evaluating Proportional Relationships
› 2.4 Decoding Scatter Plots: Identifying Correlations
› 2.5 Examining Histograms: Understanding Distribution Patterns
• 3 Evaluating the Effectiveness of Data Visualizations — 5 classes
› 3.1 Understanding Key Elements of Data Visualizations
› 3.2 Identifying Misleading Visual Techniques
› 3.3 Comparing Visualizations for Accuracy and Clarity
› 3.4 Evaluating Audience Interpretation of Visual Data
› 3.5 Applying Best Practices for Effective Data Visuals
• 4 Advanced Techniques in Data Interpretation — 5 classes
› 4.1 Recognizing Patterns in Complex Data
› 4.2 Analyzing Multidimensional Data Visualizations
› 4.3 Evaluating the Effectiveness of Visualization Techniques
› 4.4 Identifying Bias and Misinterpretation in Data Visualizations
› 4.5 Applying Insights from Data to Real-World Scenarios
5 Introduction to Data Science 4 chapters · 20 classes  45 marks
• 1 Understanding the Basics of Data Science — 5 classes
› 1.1 Exploring the Role of Data Science
› 1.2 Identifying Key Data Science Concepts
› 1.3 Distinguishing Between Structured and Unstructured Data
› 1.4 Understanding Data Collection and Cleaning
› 1.5 Applying Basic Statistical Techniques to Data
• 2 Data Acquisition and Management Principles — 5 classes
› 2.1 Understanding Data Sources and Types
› 2.2 Collecting Data: Methods and Techniques
› 2.3 Exploring Data Collection Tools and Technologies
› 2.4 Managing Data: Storage and Organization
› 2.5 Ensuring Data Quality and Integrity
• 3 Introduction to Data Analysis Techniques — 5 classes
› 3.1 Understanding Data Types and Structures
› 3.2 Exploring Statistical Measures for Data Analysis
› 3.3 Visualizing Data Patterns with Graphs and Charts
› 3.4 Applying Descriptive Analytics Techniques
› 3.5 Interpreting Data Insights for Decision Making
• 4 Fundamentals of Data Visualization — 5 classes
› 4.1 Understanding Data Visualization Concepts
› 4.2 Exploring Types of Data Visualizations
› 4.3 Analyzing Data with Graphical Tools
› 4.4 Applying Principles of Effective Data Visualization
› 4.5 Creating Interactive Data Visualizations
6 Tools for Data Analysis 4 chapters · 20 classes  45 marks
• 1 Chapter 1 — Introduction to Data Analysis Tools — 5 classes
› 1.1 Exploring Key Data Analysis Tools
› 1.2 Understanding Data Analysis Frameworks
› 1.3 Navigating Data Analysis Software Interfaces
› 1.4 Comparing Popular Data Analysis Tools
› 1.5 Selecting the Right Tool for Analysis Tasks
• 2 Chapter 2 — Spreadsheets for Data Manipulation — 5 classes
› 2.1 Understanding Spreadsheet Interfaces and Functionality
› 2.2 Importing and Organizing Data in Spreadsheets
› 2.3 Applying Basic Formulas and Functions
› 2.4 Visualizing Data with Charts and Graphs
› 2.5 Automating Tasks with Macros and Scripts
• 3 Chapter 3 — Statistical Software: R and Python — 5 classes
› 3.1 Exploring the Basics of R for Statistical Analysis
› 3.2 Navigating Python Libraries for Data Manipulation
› 3.3 Implementing Data Visualization Techniques in R
› 3.4 Conducting Statistical Tests in Python
› 3.5 Integrating R and Python for Enhanced Data Insights
• 4 Chapter 4 — Data Visualization Tools: Tableau and Power BI — 5 classes
› 4.1 Understanding the Basics of Tableau for Beginners
› 4.2 Navigating the Power BI Interface
› 4.3 Creating Simple Visualizations in Tableau
› 4.4 Building Dynamic Dashboards in Power BI
› 4.5 Comparing and Selecting Between Tableau and Power BI
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.