Certificate in Data Visualization
This online course is designed to give learners a comprehensive introduction to data visualization tools and techniques. Students will learn how to create meaningful visuals that effectively communicate results and insights.
Data Visualization Online Course Description:
This online course is designed to give learners a comprehensive introduction to data visualization tools and techniques. Students will learn how to create meaningful visuals that effectively communicate results and insights. The course will cover topics such as basic chart types, data representation, best practices for creating effective visuals, the use of colors in charting, and the application of various software programs for producing quality graphics.
In addition, students will acquire practical skills in data cleaning and preparation for visualizing complex datasets. Through hands-on exercises and projects, students will gain experience using popular packages like Tableau, D3.js, R Shiny, Gephi, CartoDB and more to produce compelling visuals from real world datasets. By the end of this course learners should be well-equipped with the necessary knowledge and skills to produce effective and visually appealing data visualizations.
• Develop an understanding of why data visualization is important for decision-making and communication.
• Explore the various types of visuals available and when to use them.
• Learn best practices for creating effective visuals that accurately convey insights.
• Acquire practical skills in data cleaning/preparation for visualizing complex datasets.
• Gain hands-on experience using popular software packages to produce quality graphics from real world datasets.
• Be able to apply acquired knowledge through projects utilizing real world datasets.
The course is divided into four sections:
Section 1: Introduction to Data Visualization:
– A high-level overview of why visualizing data effectively has become so important in today’s business world, what types of visuals are available, and best practices for creating quality visuals.
Section 2: Basic Chart Types:
– An introduction to various types of charts and how they can be used in different situations. Students will learn when and how to use line, bar, pie, scatterplot and other chart types to visualize their data.
Section 3: Advanced Visuals:
– Learn advanced techniques such as heatmaps, network diagrams, tree maps, and dashboarding to create visuals that are both informative and engaging.
Section 4: Visualization Software :
– A look at the latest software tools available for creating data visualizations, including Tableau, D3.js, R Shiny, Gephi, CartoDB and more. Students will also gain hands-on experience using these tools to produce meaningful visuals from real world datasets.
By the end of this course learners should have a comprehensive understanding of data visualization fundamentals as well as practical skills in creating compelling visualizations with modern software packages. Additionally, students will have the opportunity to practice applying their newfound knowledge through several projects utilizing real world datasets.
No prior knowledge or experience with data visualization is necessary to take this course, although it is recommended that learners have a basic working knowledge of Excel or other spreadsheet programs as well as an understanding of basic statistical concepts.
By the end of this course learners should have a comprehensive understanding of data visualization fundamentals and be able to create effective visuals using modern software packages. Additionally, students will gain experience in applying their newfound knowledge through several projects utilizing real-world datasets.
Duration: 10 hours – This online course can be completed at your own pace within 30 days from start date.
Certification: Upon completion of this course, you will receive a certificate in Data Visualization.
This course is designed for a beginner learner who wants to understand the fundamentals of data visualization and have the necessary knowledge and skills to produce effective visuals from real world datasets with popular software packages such as Tableau, D3.js, R Shiny, Gephi, CartoDB and more.
Data Visualization – The use of visual elements to represent data, results and insights in an organized manner.
Chart Types – Different types of diagrams used to communicate different aspects of a dataset or set of results.
Data Representation –The way in which data is represented visually, including the type of chart used and its overall composition.
Visualization Software – Programs used for creating meaningful visuals from datasets and analytical results. Popular examples include Tableau, D3.js, R Shiny, Gephi, CartoDB and more.
Data science: The field of study that uses mathematics, statistics and computer science to uncover patterns in large data sets.
Data analyst: Professionals who analyze data and develop insights from it to assist businesses in making informed decisions.
Data visualization skills: The ability to represent data with visual elements such as charts, graphs, and maps in order to effectively communicate information.
Data analysis: The process of extracting meaningful insights from raw or structured data.
Advanced data: Data mining techniques used to discover hidden relationships between variables within a dataset.
Raw data: Unstructured or uncategorized data that has not been manipulated or analyzed yet.
Behind data: Strategies used by organizations to make sense of their vast amounts of data to gain a better understanding of their customers, operations, and the markets they compete in.
Impactful data visualization: The use of visuals to communicate information in an engaging and effective way.
Concepts of data visualizations: Basic principles necessary for creating meaningful and accurate visuals from data sets.
Data visualization certification: Professional certifications that demonstrate proficiency in the field of data visualization.
Skills in data analysis: The ability to extract insights from raw or structured data using mathematical, statistical, and computer science techniques.
Building data visualizations: The process of creating informative and aesthetically appealing graphics from datasets.
Data using Tableau: Techniques for utilizing the Tableau software package to create visualizations from datasets.
Data visualization techniques: Different methods used to communicate different aspects of a dataset or set of results.
Data visualization package: Popular software packages used for creating meaningful visuals from datasets and analytical results. Examples include Tableau, D3.js, R Shiny, Gephi, CartoDB and more.
Data visualization and exploratory data analysis: A method of displaying data so that patterns, trends and outliers become more apparent.
Showcase the data: Presenting a dataset in a visual manner to allow viewers to better understand the information contained within it.
Process data: Organizing and manipulating raw or structured data into meaningful formats for further analysis.
Data visualization consultant: Professional who assists businesses in designing visuals from their datasets to showcase insights and results.
Combine data: Merging multiple datasets together in order to create a more comprehensive view of an area of study.
Leverage data: Using previously collected information to gain new insights or discover hidden relationships between variables within a dataset.
Python courses: Classes focused on teaching students how to use the Python programming language for data analysis.
Data mining: The process of extracting information from a large dataset in order to discover new relationships and insights.
Interpret data: Analyzing results to gain understanding and draw conclusions about a specific situation or field of study.
Big data: A term used to describe extremely large datasets that require advanced processing power and sophisticated algorithms in order to be analyzed effectively.
Data analytics: The practice of using collected data to gain insights into a particular topic or area of study.
Data sets: Structured collections of related information gathered together for the purpose of further analysis.
Data scientist: Professionals who specialize in utilizing data to uncover relationships and generate insights.
Microsoft Excel: Popular spreadsheet software used for data entry, analysis and visualization.
Programming language: A set of instructions used to create computer programs that can be run on a device or in the cloud.
Machine learning: A subset of artificial intelligence that focuses on developing algorithms that can learn from previously collected data.
Tell a story: Creating narratives with data visualizations in order to make complex information understandable and engaging.