Certificate in Data Analysis & Visualization

LAPT

London Academy of Professional Training

Certificate Level 2-3 Foundation IT Industry

REF: IT-DSA-F

Subjects

500

Total Marks

60%

Pass Mark

Lifetime

Validity

Who Is It For

This certification is designed for individuals at the beginning of their career in data science or those looking to transition into the field. It is suitable for recent graduates or entry-level professionals who seek to establish a foundational understanding of data analysis and visualisation.

Prerequisites

None

Awarding Body: LAPT — London Academy of Professional Training

Curriculum Overview

1 Data Reporting and Communication 4 chapters · 15 classes · 100 marks

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

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

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

Chapter 4 — Tools and Technologies for Data Communication

2 Interpreting Data Visualisations 4 chapters · 20 classes · 100 marks

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

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

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

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

3 Tools for Data Analysis 4 chapters · 20 classes · 50 marks

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

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

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

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

4 Data Visualisation Techniques 4 chapters · 20 classes · 100 marks

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

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

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

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

5 Basic Statistics for Data Analysis 4 chapters · 20 classes · 100 marks

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

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

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

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

6 Introduction to Data Science 4 chapters · 20 classes · 50 marks

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

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

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

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

Assessment Breakdown

70%

Theory

20%

Practical

10%

Project

Passing Mark: 300 / 500 (60%)

Methods: Written Examination, Practical Assignment, Portfolio Assessment

How to Enrol

Website: lapt.org

Email: info@lapt.org

Phone: +44 7513 283044

Address: 85 Great Portland Street, W1W 7LT, United Kingdom

Hours: Monday – Friday, 9AM – 5PM

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