Data literacy course overview

Further insights into your learning program

The basics of data analytics

Understanding the fundamentals of data analytics, its significance, and the value of data-driven decision-making.
Gaining knowledge and familiarity with essential data terminology and concepts.
Exploring the data analytics lifecycle, from data discovery to insights and decision-making.
Differentiating between information and data, and understanding their respective roles in driving business outcomes.
Gaining insights into your own data landscape, uncovering valuable information about your organization's data sources, challenges, and opportunities.

Data Handling and tools.

Practical skills in data preparation, including data cleaning, transformation, and structuring.
Basic introduction to Ai, Machine Learning and Python - how are they used in Data Analytics.
Understanding the significance of data accuracy and the impact it has on decision-making.
Data Collection Methods - Surveys, web scraping, sensors, logging, etc. How to gather quality data.
Data Storage and Databases - SQL, NoSQL, data warehouses, data lakes. Storing and querying data.
Data Processing - ETL processes, data pipelines, workflows, automation. Organising and moving data.
Data Quality - Assessing, validating, improving data completeness, validity, accuracy, consistency.
Data Governance - Policies, guidelines, roles and responsibilities for managing data assets.
Metadata Management - Attaching meaningful descriptions & definitions to data for better discovery & understanding.
Master Data Management - Managing "golden records" and reference data critical to an organization.
Data Modeling - Structuring data logically and functionally to capture relationships and semantics.
Data Visualization - Using visual representations like charts, graphs and dashboards to analyze data.
Spreadsheets - Excel skills for managing, manipulating, analyzing and presenting datasets.

Data Communication

Data visualisation: Master the skills needed to create visually compelling representations of data.
Storytelling: Learn how to craft narratives that engage and resonate with audiences, bringing insights to life.
Acquire the ability to design and build interactive dashboards that effectively communicate key information.
Report automation: Develop proficiency in automating the generation of reports, saving time and improving efficiency.
Hone skills such as clarity, adaptability in listening, and the art of persuasion to effectively convey data-driven insights.

Embracing change and data analytics

Resistance to change, fear of the unknown, lack of confidence, and misconceptions.
The intersection of business knowledge & data literacy
Importance and benefits of adopting data literacy in decision-making processes.
Risks of not integrating data into decision-making and the consequences.
External and internal factors driving the need for data literacy.
Personal impact of transitioning to a data-driven role.
Strategies for overcoming resistance to data-driven change.
Timeline for becoming data literate.

The Human Dimension of Data Literacy

Cognitive biases and their impact on data interpretation and decision-making.
Psychological barriers to data literacy and strategies to overcome them.
The role of emotions and perception in data analysis and communication.
Data skepticism and the importance of critical thinking in evaluating data sources.
The psychology of data visualisation and its influence on understanding and communication.
Motivation and mindset for developing data literacy skills.
Ethical considerations and psychological implications related to data privacy and security.
The psychology of data-driven decision-making and its effects on organizational culture.
Psychological factors influencing data-driven storytelling and effective data communication.
The psychology of data-driven problem-solving and innovation

Future skills & Mindset in data analytics.

Critical thinking: Analyze information objectively, evaluate arguments, make reasoned judgments based on evidence.
Understanding and extracting meaningful insights from complex datasets to inform decision-making.
Applying statistical methods to analyze data, identify patterns, and draw valid conclusions.
Presenting data in a visually compelling and understandable format to facilitate communication and insights.
Writing instructions in a programming language to develop software applications or automate tasks.
Utilizing algorithms and models to enable computers to learn from data and make predictions or decisions.
Managing and organizing data storage and retrieval systems, often using cloud technology.
Identifying and resolving challenges by employing analytical and creative thinking.
Focusing on accuracy and precision, ensuring all aspects of a task or project are thoroughly addressed.
Applying logical principles and deductive reasoning to analyze and solve problems.
Curiosity: A desire to explore and learn, asking questions and seeking new knowledge and perspectives.

Ethics, management, compliance & Security

Mastering data literacy: Acquiring the necessary skills and knowledge to understand and work effectively with data.
Boosting organizational value: Demonstrating the ability to leverage data effectively
Understanding the role of data: how it can drive decision-making and business outcomes.
Potential career opportunities and how developing data skills can position you for strategic transitions.
Understanding how data skills can benefit professionals in various roles, even if their primary focus is not data-related.
Establish a professional profile that emphasises your expertise in working with data and its applications.
Importance of networking: Recognizing the value of connecting with others in the data community
Continuous skill upgrading: Emphasizing the need to consistently update and improve data skills to stay relevant

Diversity, Equality & Sustainability in data literacy

The significance of having an understanding of your industry, organisation, & cross-functional departments
Understanding how acquiring knowledge about different aspects of the business fosters collaboration
Practical strategies such as conducting informational interviews, job shadowing, and participating in cross-departmental projects to gain a comprehensive understanding of various business areas.
Stakeholder transparency: Promoting transparency with stakeholders by sharing data insights, fostering an inclusive approach to data-driven business growth.
Solidifying your role as a valuable asset within your organisation's data journey by leveraging your comprehensive business understanding and contributing to data-driven initiatives.