2553 Courses in Cardiff delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Building High-Performance Teams: In-House Training This course pulls together the most current and popular theories and writings on this complex topic and presents this amalgamated view in a highly interactive workshop and activity-based approach. Students will understand and have the skills required to build and participate in high-performance project teams and will possess the insight to proactively affect change within their respective organizations by guiding the existing culture to one that promotes high performance. What you will Learn At the end of this program, you will be able to: Define a team and describe the optimum team size for effective performance Describe characteristics and guiding behaviors of high-performance teams Describe the major elements of each development stage in two distinct models Recognize cultural barriers in achieving high performance List the attributes of a high-performing corporate culture Assess your own corporate culture Discuss corporate leadership as a factor in building high-performance project teams Describe the three A's of selecting team members State three leadership responsibilities Describe leadership responsibilities, styles, and roles List and describe the eight components of the team charter model for building high-performance teams Foundation Concepts The Nature of Teams Characteristics of High-Performance Teams Understanding Team Development Stages of Team Development - Model 1 Stages of Team Development - Model 2 Designing a High-Performance Culture Corporate Cultures Corporate Leadership Establishing the Attributes of High Performance Choosing the Right People Team Effectiveness Team Leadership The Team Charter Model

Duration 1 Days 6 CPD hours This course is intended for This course is designed for business leaders and decision makers, including C-level executives, project and product managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who have a vested interest in the representation of ethical values in technology solutions. Other individuals who want to know more about data ethics are also candidates for this course. This course is also designed to assist learners in preparing for the CertNexus DEBIZ™ (Exam DEB-110) credential. The power of extracting value from data utilizing Artificial Intelligence, Data Science and Machine Learning exposes the learning differences between humans and machines. Humans can apply ethical principles throughout the decision-making process to avoid discrimination, societal harm, and marginalization to maintain and even enhance acceptable norms. Machines make decisions autonomously. So how do we train them to apply ethical principles as they learn from decisions they make? This course provides business professionals and consumers of technology core concepts of ethical principles, how they can be applied to emerging data driven technologies and the impact to an organization which ignores ethical use of technology. Introduction to Data Ethics Defining Data Ethics The Case for Data Ethics Identifying Ethical Issues Improving Ethical Data Practices Ethical Principles Ethical Frameworks Data Privacy Accountability Transparency and Explainability Human-Centered Values and Fairness Inclusive Growth, Sustainable Development, and Well-Being Applying Ethical Principles to Emerging Technology Improving Ethical Data Practices Sources of Ethical Risk Mitigating Bias Mitigating Discrimination Safety and Security Mitigating Negative Outputs Data Surveillance Assessing Risk Ethical Risks in sharing data Applying professional critical judgement Business Considerations Data Legislation Impact of Social and Behavioral Effects Trustworthiness Impact on Business Reputation Organizational Values and the Data Value Chain Building a Data Ethics Culture/Code of Ethics Balancing organizational goals with Ethical Practice Additional course details: Nexus Humans CertNexus Data Ethics for Business Professionals (DEBIZ) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the CertNexus Data Ethics for Business Professionals (DEBIZ) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

The Internet of Things (IoT) promises a wide range of benefits for industry, energy and utility companies, municipalities, healthcare, and consumers. Data can be collected in extraordinary volume and detail regarding almost anything worth measuring, such as public health and safety, the environment, industrial and agricultural production, energy, and utilities.

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Trade barriers are going up across the globe. And cybercrime is on the increase. The link between the two? The value of trade secrets. As countries become increasingly protectionist as regards international trade, so their IP law has been changing, with the result that companies that previously would have sought protection through patents are opting to go down the trade secret route instead. But is this a high-risk strategy? Technology is changing and this is having an impact on forms of commercial co-operation. Collaborative or open forms of innovation by their very nature involve the sharing of intellectual property (IP), and in many instances this IP is in the form of valuable confidential business information (ie, trade secrets). Little surprise, then, that trade secrets disputes have increased accordingly. At the same time, the changes in technology make trade secrets more vulnerable to attack, misappropriation, theft. So just how effective are the legal protections for trade secrets? How can organisations safeguard the value in their IP (increasingly, the single biggest line in their balance sheets)? This programme is designed to help you address these issues. Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. This session is designed to give you a deeper understanding of: Emerging trends in trade secrets protection and exploitation The current situation in key jurisdictions Recent case law How leading companies are responding The importance of trade secret metadata Different external stakeholders and their interests Key steps for effective protection of trade secrets Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. 1 What are trade secrets? Definitions Examples Comparison with other forms of IP (patents, confidential information, know-how, copyright) 2 Current trends The various changes taking place affecting trade secrets - legal changes, trade wars, cybercrime, technology, commercial practice The current position in the UK, Europe, USA, China, Japan, Russia Corporate best practice 3 Trade secret disputes - how to avoid them Trade secret policies, processes and systems Administrative, legal and technical protection mechanisms The role of employees The sharing of trade secrets with others 4 Trade secret disputes - how to manage them Causes Anatomy of a trade secret court case 'Reasonable particularity' 5 Related issues Insurance Tax authorities and investigations Investor relations 6 Trade secret asset management roadmap Maturity ladder First steps Pilot projects

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Project Risk Management: Virtual In-House Training Have you been surprised by unplanned events during your projects? Are you and your project team frequently fighting fires? Well, you are not alone. Uncertainty exists in any project environment. While it's impossible to predict project outcomes with 100% certainty, you can influence the outcome, avoid potential risks, and be ready to respond to challenges that arise. In this course, you'll gain the proper knowledge needed to identify, assess, plan for, and monitor risk in your projects. You'll learn how to set up and implement risk management processes, helping you to minimize uncertainty and achieve more consistent, predictable outcomes as a result. What You Will Learn You'll learn how to: Demonstrate to others how the risk management processes in A Guide to the Project Management Body of Knowledge (PMBOK® Guide) apply to your project's environment, especially for high-risk projects Adapt these processes for a particular high-risk project team's operating principles Explain the importance of using risk management best practices at single and enterprise project levels Lead an initiative to implement risk management best practices in your project environment Foundation Concepts Risk-related definitions The risk management process High-risk projects and project failures Classical failures in implementing risk management Plan Risk Management Project risk management and governance Risk management planning for high-risk projects High-risk variations on a risk management plan Identify Risk Adapting the risk identification process for high-risk projects Recognizing risks spontaneously Confirming and structuring risk events for treatment Wrapping up risk identification for high-risk projects Perform Qualitative Risk Analysis Adapting qualitative risk analysis for high-risk projects Accelerating risk analysis Clearing risk action Wrapping up qualitative risk analysis for the next level Perform Quantitative Risk Analysis Adapting quantitative risk analysis for high-risk projects Ensuring effective risk analyses with data quality assessments Building a foundation for quantitative risk analysis Using discrete quantitative tools Using continuous quantitative tools Wrapping up quantitative risk analysis for high-risk projects Plan Risk Responses Adapting risk response planning for high-risk projects Optimizing active risk response strategies Leveraging contingencies for high project performance Wrapping up risk response planning for high-risk projects Implement Risk Responses Implementing Risk Responses Process Executing Risk Response Plans Tools and Techniques Best Practices Continuous Risk Management Monitor Risks Adapting risk monitoring for high-risk projects Optimizing risk plan maintenance Weaving risk reassessment into the project's progress Maintaining a continuous 'vigil' in high-risk project environments

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