• Define data, its various types (structured, unstructured, semi-structured), and their importance.
• Explore the Data Lifecycle (acquisition, storage, processing, analysis, visualization).
• Discuss data quality concepts (accuracy, completeness, consistency) and data cleaning techniques.
• Introduce essential data analysis tools and technologies (Excel, Power Query, SQL, Python, etc.).

Activities:

• Interactive exercises on data classification and lifecycle stages.
•  Hands-on labs practicing data cleaning methods in a common tool (Excel/Python).
• Group discussions on data quality challenges and best practices.

• Define data, its various types (structured, unstructured, semi-structured), and their importance.
• Explore the Data Lifecycle (acquisition, storage, processing, analysis, visualization).
• Discuss data quality concepts (accuracy, completeness, consistency) and data cleaning techniques.
• Introduce essential data analysis tools and technologies (Excel, Power Query, SQL, Python, etc.).

Activities:

• Interactive exercises on data classification and lifecycle stages.
•  Hands-on labs practicing data cleaning methods in a common tool (Excel/Python).
• Group discussions on data quality challenges and best practices.

• Introduce Exploratory Data Analysis (EDA) techniques for understanding data characteristics (descriptive statistics, visualizations).
• Cover data wrangling methods for preparing data for analysis (data transformation, feature engineering).
• Discuss common data analysis techniques (hypothesis testing, correlation analysis).

Activities:

• interactive workshops on calculating descriptive statistics using a chosen tool (Excel/Power Query/Python).
•  Hands-on labs practicing data wrangling techniques and data analysis methods.
•  Group projects conducting basic EDA and analysis on a provided dataset.

• Explain the importance of data visualization in communicating insights effectively.
• Introduce visual perception principles and best practices for creating clear and compelling visualizations.
• Cover common data visualization techniques (bar charts, histograms, scatter plots, line charts) and their use cases.

Activities:

• Interactive exercises on applying visual perception principles to data visualizations.
• Hands-on workshops on creating various data visualizations using a chosen tool (Excel/Tableau/Power BI).
• Group discussions on selecting the right visualizations for different data types and analysis goals.

• Introduce advanced data visualization techniques (heatmaps, box plots, pie charts, network graphs) and their applications using Power BI.
• Discuss interactive dashboards and storytelling techniques for presenting data insights.
• Explore data visualization best practices for accessibility and ethical considerations.

Activities:

• Hands-on labs on creating advanced data visualizations using a chosen tool.
• Group projects on designing interactive dashboards to communicate data-driven stories.
• Case studies analyzing effective and ineffective data visualizations from real-world examples.

• Explain the art of data storytelling: crafting a narrative using data to engage the audience and influence decisions.
• Discuss the key elements of a compelling data story (context, evidence, insights, recommendations).
• Cover effective communication techniques for presenting data insights clearly and concisely.

Activities:

• Interactive exercises on identifying the elements of a strong data story.
• Group projects on developing data stories from provided datasets.
• Peer-review sessions on refining data storytelling techniques.

• Explain how data analysis helps organizations make informed and data-driven decisions.
• Discuss real-world examples of data impacting business outcomes (marketing campaigns, product development, customer service).
• Introduce key performance indicators (KPIs) and their role in measuring data-driven success.
• Explore potential challenges and biases in data analysis and how to mitigate them.

Activities:

• Case studies analyzing how companies have used data to achieve business goals.
• Group projects on identifying potential data-driven solutions to a business challenge.
• Interactive exercises on identifying potential biases in data and decision-making.

• Explain the benefits of automation in data analysis tasks (data collection, cleaning, reporting).
• Discuss different data automation tools and technologies (e.g., ETL/ELT tools, Python scripts).
• Explore best practices for implementing data automation solutions within organizations.
• Identify potential challenges and limitations of data automation.

Activities:

• Case studies analyzing how companies have used automation to improve data analysis efficiency.
• Hands-on tutorials on basic data automation techniques using a chosen tool.
• Group discussions on identifying tasks for automation within a specific business scenario.

• Explain the concept of the customer data journey and its touchpoints.
• Discuss various methods for collecting customer data at different stages of the journey.
• Analyze customer decision-making processes and how data can be used to influence them.
• Explore strategies for personalization and targeted marketing based on customer data insights.

Activities:

• Interactive exercises on mapping the customer data journey for a specific product or service.
• Group projects on developing customer personas based on data analysis.
• Case studies analyzing how companies have used data to personalize customer experiences.

• Define a data-driven culture and its core characteristics (data literacy, collaboration, open communication).
• Discuss the importance of fostering a culture of data-driven decision-making across the organization.
• Explore strategies for promoting data literacy and encouraging data exploration among employees.
• Identify potential challenges in building a data culture and how to overcome them.

Activties:

• Interactive exercises on identifying key principles of a data-driven culture.
• Group discussions on developing strategies for promoting data literacy within an organization.
• Role-playing exercises on communicating data insights to teams with varying levels of data expertise.