Key Sessions

This program will be delivered across 5 days from September 23 to September 27, 2024, 9am to 3pm EST with breaks for morning tea, lunch, and afternoon tea.

The CompTIA Data+ certification is structured around core modules that cover a broad spectrum of data analytics concepts and practices. Below is an overview of these modules, with key focus areas highlighted for each:

Data Concepts and Environments

• Understanding basic data concepts, including types, structures, and lifecycles.
• Familiarity with various data environments, such as traditional databases, big data ecosystems, and cloud storage solutions.
• Identifying and implementing data quality standards to ensure accuracy and reliability of data.
• Recognizing the significance of data governance and compliance with data protection regulations.
• Exploring the use of data in different industry sectors and its impact on business strategies.

Visualization and Reporting

• Designing and creating data visualizations to effectively communicate data insights.
• Selecting appropriate visualization tools and techniques based on the data and the intended audience.
• Developing reports and dashboards to present findings and support business decisions.
• Applying principles of data storytelling to enhance the understanding and impact of data insights.
• Ensuring accessibility and comprehension of data visualizations across diverse audiences.

Data Mining

• Introduction to data mining concepts and techniques for extracting valuable information from large datasets.
• Utilizing data extraction methods to retrieve data from various sources.
• Applying data cleaning techniques to prepare data for analysis.
• Understanding the basics of data manipulation and transformation.
• Recognizing patterns and trends through exploratory data analysis.

Data Analysis

• Applying statistical methods and techniques to analyze data.
• Conducting quantitative and qualitative analysis to interpret data findings.
• Utilizing analytical models to solve business problems and make data-driven decisions.
• Introduction to machine learning algorithms for predictive analytics.
• Assessing the effectiveness of data analysis through key performance indicators (KPIs) and metrics.

Tools and Technologies

• Familiarity with a range of data analytics tools and software, including spreadsheets, databases, and specialized analytics platforms.
• Introduction to programming languages commonly used in data analytics, such as SQL, Python, and R.
• Navigating business intelligence (BI) tools and data visualization software.
• Leveraging cloud-based analytics services for scalable data analysis solutions.
• Understanding the role of automation in data analytics for efficiency and accuracy.