366 Courses in Cardiff delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.

Talent Management and Succession Planning,” the podcast where we explore the critical aspects of attracting and retaining top finance talen Talent management is the lifeblood of any organisation, and finance departments are no exception. In a competitive business landscape, attracting and retaining top finance talent can make a significant difference. Highly skilled and motivated professionals drive innovation, improve financial performance, and contribute to strategic decision-making. By investing in talent management, CFOs ensure their organisations have the right people in the right roles, which is vital for sustainable growth and success. Talent management also enables CFOs to build a culture of continuous learning and development. By nurturing the skills and capabilities of finance professionals, we create an environment that fosters innovation and adaptability. This is crucial in today’s rapidly changing business landscape, where finance teams need to keep pace with evolving technologies, regulations, and industry trends. Talent management provides a foundation for building a resilient and agile finance function. Succession planning is an integral part of talent management. How do CFOs approach succession planning, particularly in finance leadership roles? Succession planning is a proactive approach to ensure a smooth transition of leadership roles. CFOs need to identify high-potential individuals within their finance teams and provide them with opportunities for growth and development. This includes mentorship, training programs, and exposure to cross-functional experiences. By preparing a pipeline of future finance leaders, CFOs can mitigate the risks associated with unexpected departures or retirements, ensuring continuity and stability in finance leadership. Additionally, succession planning should encompass diversity and inclusion. CFOs recognize the importance of building diverse finance teams that reflect the broader talent pool. By providing equal opportunities for underrepresented groups and promoting inclusivity, we foster a culture of belonging and tap into a wider range of perspectives and ideas. Diverse teams drive innovation and improve decision-making, contributing to the overall success of the organisation. How do CFOs create a talent development culture within their finance teams, and what initiatives can be implemented to foster continuous growth? CFOs can create a talent development culture by prioritizing learning and development initiatives. This includes offering ongoing training programs, supporting professional certifications, and providing access to resources that enhance technical and soft skills. CFOs should encourage finance professionals to take ownership of their own development and provide opportunities for them to stretch their capabilities. This may involve cross-functional projects, exposure to different areas of the business, or participation in industry conferences and networking events. Additionally, mentorship and coaching programs play a crucial role in talent development. CFOs can pair experienced finance leaders with up-and-coming talent, fostering knowledge transfer, and providing guidance and support. Encouraging regular feedback and performance discussions helps finance professionals understand their strengths and areas for improvement, enabling targeted development plans. By creating a culture that values continuous learning and growth, CFOs empower their finance teams to reach their full potential. https://www.fdcapital.co.uk/podcast/talent-management-and-succession-planning/ Tags Online Events Things To Do Online Online Classes Online Business Classes #leadership #development #successionplanning #employees #talentmanagement

Duration 5 Days 30 CPD hours This course is intended for New or junior administrators and operators; system managers accountable for staffing and training Horizon operators and administrators. Experienced system operators, administrators, and integrators responsible for managing and maintaining VMware Horizon solutions Overview By the end of the course, you should be able to meet the following objectives: Implement a structured approach to troubleshooting Resolve common issues that occur in a VMware Horizon environment Troubleshoot issues with linked and instant clones Configure the Windows client Identify the correct log level for gathering logs Optimize protocols for best end-user experience Discuss Horizon Connection Server advanced configurations List troubleshooting techniques for Horizon Connection Server common issues Interpret Horizon 8 Connection Server logs Identify Unified Access Gateway configuration and certificate issues List troubleshooting steps for Unified Access Gateway common issues Describe BLAST configuration verification using logs and settings Describe BLAST optimization recommendations for different use cases Describe Horizon 8 Connections and how to troubleshoot related issues Describe Horizon 8 certificates List troubleshooting steps for common issues with Horizon 8 certificates Leverage Horizon infrastructure troubleshooting steps to resolve issues  This five-day course builds your skills in resolving common issues that occur in a VMware Horizon© environment. You engage in a series of lab exercises to bring existing environment issues to resolution. The exercises mirror real-world troubleshooting use cases. These exercises equip learners with the knowledge and practical skills to manage typical challenges faced by virtual desktop administrators and operators and provides you with the advanced knowledge, skills, and abilities to troubleshoot VMware Horizon© 8 infrastructure related issues. This workshop teaches the required skill and competence for troubleshooting VMware Horizon© Connection Server?, VMware Unified Access Gateway?, protocols, connections, and certificates Course Introduction Introductions and course logistics Course objectives Overview of Virtual Desktop Troubleshooting Structured approach to troubleshooting configuration and operational problems Applying troubleshooting methods Documenting the steps to resolving the problem Command-Line Tools and Backup Options Using command-line tools Backing-up and restoring VMware Horizon databases Troubleshooting Horizon Linked Clone Desktops Only applicable for Horizon 7.x environments Describe the components that make up a VMware Horizon desktop Explain how the View Agent Direct-Connection plug-In is useful for diagnosing problems Highlight the best practice for optimizing a VMware Horizon desktop Troubleshoot common problems with VMware Horizon desktops Troubleshooting Instant Clones Discuss how instant clones are created Discuss what gets logged when an instant clone is created Discuss the keywords to look for in the logs when troubleshooting instant clones Discuss how to troubleshoot problems with instant clones Windows Client Correctly configure the Windows Client Identify the correct log level for gathering logs Enable the required SSL configuration level for the environment Ports and Protocols Discuss the key ports on a Horizon Environment Discuss protocols used in the Horizon Environment Understand the benefit of optimizing Blast Become familiar with the optimization features for Blast Implement GPO changes for Blast Become familiar with the causes for Black Screens Discuss how to troubleshoot Black Screen problems Identify problems encountered when applying GPOs Discuss how to troubleshoot GPO-related problems Horizon Connection Server Troubleshooting Discuss Horizon Connection Server general troubleshooting techniques Identity Horizon Connection Server common issues through logs Describe AD LDS replication Discuss Horizon Connection Server replication common issues Review and Interpret Horizon Connection Server logs Compare successful and unsuccessful logs from everyday infrastructure administration tasks Unified Access Gateway Troubleshooting List and identify common Unified Access Gateway deployment issues Monitor the Unified Access Gateway deployment to identify health and issues Identify and troubleshoot Unified Access Gateway certificate issues Monitor, test, and troubleshoot network problems Discuss general Unified Access Gateway troubleshooting processes BLAST Configuration Verification Discuss BLAST Codecs and Encoder Switch settings. Describe how to verify BLAST configuration using logs and settings BLAST Optimization List general BLAST optimization recommendations Summarize BLAST tuning recommendations that apply to WAN connections Summarize BLAST tuning recommendations that apply to work-from-home and home-office-to-cloud use cases Describe recommended tuning options to increase display protocol quality for all use cases and applications. VMware Horizon Connections Troubleshooting Explain Horizon connections Describe the role of Primary and Secondary protocols in Horizon connections Describe HTML client access connections Describe Horizon connections load balancing Describe timeout settings, supported health monitoring string, and suitable load balancer persistence values Identify troubleshooting steps for failing Horizon load balancer connections List troubleshooting steps for Horizon connections VMware Horizon Certificates Troubleshooting List Horizon certificate functions Describe Horizon certificates scenarios. Discuss potential challenges related to certificates in Horizon Describe the troubleshooting approach to Horizon certificate issues VMware Horizon Challenge Lab Leverage Horizon infrastructure troubleshooting steps to resolve issues

Duration 3 Days 18 CPD hours This course is intended for This course is appropriate for advanced users, system administrators and web site administrators who want to use Python to support their server installations, as well as anyone else who wants to automate or simplify common tasks with the use of Python scripts. Students can apply the course skills to use Python in basic web development projects or automate or simplify common tasks with the use of Python scripts. Overview This skills-focused course is about 50% hands-on lab to lecture ratio, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert instructor, you'll learn how to: Create working Python scripts following best practices Use python data types appropriately Read and write files with both text and binary data Search and replace text with regular expressions Work with with the standard library and its work-saving modules Create 'real-world', professional Python applications Know when to use collections such as lists, dictionaries, and sets Work with Pythonic features such as comprehensions and iterators Write robust code using exception handling Introduction to Python Programming Basics is a hands-on Python programming course that teaches you the key skills you?ll need to get started with programming in Python to a solid foundational level. The start of the course will lead you through writing and running basic Python scripts, and then guide you through how to use more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting. This course provides you with an excellent kick start for users new to Python and scripting, enabling you to quickly use basic Python skills on the job in a variety of ways. You?ll be able use Python in basic web development projects, or use it to automate or simplify common tasks with the use of Python scripts. The course also serves as a solid primer course / foundation for continued Python study in support for next level web development with Python, using Python in DevOps, Python for data science / machine learning or Python for systems admin or networking support. Python Quick View What is Python? Python timeline Advantages/disadvantages Installing Python Getting help The Python Environment Starting Python Using the interpreter Running a Python script Editors and IDEs Getting Started with Python Using variables Builtin functions String data Numberic data Converting types Console input/output Command line parameters Flow Control About flow control The if statement Relational and Boolean operators while loops Exiting from loops Array Types About array types Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions and generators Working with Files File overview Opening a text file Reading a text file Writing to a text file Dictionaries and Sets About dictionaries Creating dictionaries Iterating through a dictionary About sets Creating sets Working with sets Functions Defining functions Returning values Parameters and arguments Variable scope Sorting The sorted() function Custom sort keys Lambda functions Sorting in reverse Using min() and max() Errors and Exception Handling Exceptions Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages Creating Modules The import statement Module search path Using packages Function and module aliases Getting Started with Object Oriented Programming and Classes About object-oriented programming Defining classes Constructors Understanding self Properties Instance Methods and data Class methods and data Inheritance Additional course details: Nexus Humans Introduction to Python Programming Basics (TTPS4800) 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 Introduction to Python Programming Basics (TTPS4800) 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.

Duration 3 Days 18 CPD hours This course is intended for Blockchain Architects Blockchain DevelopersApplication Developers Blockchain System AdministratorsNetwork Security Architects Cyber Security ExpertsIT Professionals w/cyber security experience Overview Those who attend the Security for Blockchain Professionals course and pass the exam certification will have a demonstrated knowledge of:Identifying and differentiating between security threats and attacks on a Blockchain network.Blockchain security methods, best practices, risk mitigation, and more.All known (to date) cyber-attack vectors on the Blockchain.Performing Blockchain network security risk analysis.A complete understanding of Blockchain?s inherent security features and risks.An excellent knowledge of best security practices for Blockchain System/Network Administrators.Demonstrating appropriate Blockchain data safeguarding techniques. This course covers all known aspects of Blockchain security that exist in the Blockchain environment today and provides a detailed overview of all Blockchain security issues, including threats, risk mitigation, node security integrity, confidentiality, best security practices, advanced Blockchain security and more. Fundamental Blockchain Security Cryptography for the Blockchain Hash Functions Public Key Cryptography Elliptic Curve Cryptography A Brief Introduction to Blockchain The Blocks The Chains The Network Promises of the Blockchain Blockchain Security Assumptions Digital Signature Security Hash Function Security Limitations of Basic Blockchain Security Public Key Cryptography Review Real-Life Public Key Protection Cryptography and Quantum Computers Lab 1 (Tentative) Finding Hash Function Collisions Reversible hash function Hash function with poor non-locality Hash function with small search space Breaking Public Key Cryptography Brute Forcing a Short Private Key Brute Forcing a Poorly-Chosen Private Key Consensus in the Blockchain Blockchain Consensus and Byzantine Generals Blockchain Networking Review Byzantine Generals Problem Relation to Blockchain Byzantine Fault Tolerance Introduction to Blockchain Consensus Security Blockchain Consensus Breakthrough Proof of Work What is Proof of Work? How does Proof of Work Solve BGP? Proof of Work Security Assumptions Attacking Proof of Work Proof of Stake What is Proof of Stake? How does Proof of Stake Solve BGP? Proof of Stake Security Assumptions Attacking Proof of Stake General Attacks on Blockchain Consensus Other Blockchain Consensus Algorithms Lab 2 (Tentative) Attacking Proof of Work Performing a 51% Attack Performing a Selfish Mining Attack Attacking Proof of Stake Performing a XX% Attack Performing a Long-Range Attack Malleable Transaction Attacks Advanced Blockchain Security Mechanisms Architectural Security Measures Permissioned Blockchains Checkpointing Advanced Cryptographic Solutions Multiparty Signatures Zero-Knowledge Proofs Stealth Addresses Ring Signatures Confidential Transactions Lab 3 (Tentative) Permissioned Blockchains 51% on a Checkpointed Blockchain Data mining on a blockchain with/without stealth addresses Zero-Knowledge Proof Simulation Trying to fake knowledge of a ZKP Module 4: Blockchain for Business Introduction to Ethereum Security What is Ethereum Consensus in Ethereum Smart Contracts in Ethereum Ethereum Security Pros and Cons of Ethereum Blockchains Introduction to Hyperledger Security What is Hyperledger Consensus in Hyperledger Smart Contracts in Hyperledger Hyperledger Security Pros and Cons of Hyperledger Blockchains Introduction to Corda Security What is Corda Consensus in Corda Smart Contracts in Corda Corda Security Pros and Cons of Corda Blockchains Lab 4 Blockchain Risk Assessment What are the Risks of the Blockchain? Information Security Information Sensitivity Data being placed on blockchain Risks of disclosure Regulatory Requirements Data encryption Data control PII protection Blockchain Architectural Design Public and Private Blockchains Open and Permissioned Blockchains Choosing a Blockchain Architecture Lab 5 Exploring public/private open/permissioned blockchains? Basic Blockchain Security Blockchain Architecture User Security Protecting Private Keys Malware Update Node Security Configuring MSPs Network Security Lab 6 (TBD) Smart Contract Security Introduction to Smart Contracts Smart Contract Security Considerations Turing-Complete Lifetime External Software Smart Contract Code Auditing Difficulties Techniques Tools Lab 7 (Tentative) Try a couple of smart contract code auditing tool against different contracts with built-in vulnerabilities Module 8: Security Implementing Business Blockchains Ethereum Best Practices Hyperledger Best Practices Corda Best Practices Lab 8 Network-Level Vulnerabilities and Attacks Introduction to Blockchain Network Attacks 51% Attacks Denial of Service Attacks Eclipse Attacks Routing Attacks Sybil Attacks Lab 9 Perform different network-level attacks System-Level Vulnerabilities and Attacks Introduction to Blockchain System Vulnerabilities The Bitcoin Hack The Verge Hack The EOS Vulnerability Lab 10 Smart Contract Vulnerabilities and Attacks Introduction to Common Smart Contract Vulnerabilities Reentrancy Access Control Arithmetic Unchecked Return Values Denial of Service Bad Randomness Race Conditions Timestamp Dependence Short Addresses Lab 11 Exploiting vulnerable smart contracts Security of Alternative DLT Architectures What Are Alternative DLT Architectures? Introduction to Directed Acyclic Graphs (DAGs) DAGs vs. Blockchains Advantages of DAGs DAG Vulnerabilities and Security Lab 12 Exploring a DAG network

Unlock the Power of Teams: Elevate your leadership with our Building and Leading Effective Teams course. Learn the art of collaboration, communication, and synergy to create high-performing teams that drive success Course overview Duration: 1 day (6 hours) Everyone works in teams today in one way or another. Whatever we do in the workplace we need to interact with another person or a number of people at various times. It is important that we know how to communicate, how to listen, how to work together and how to overcome conflict when it arises in our workplace. Challenges are often created by a lack of trust, poor conflict handling skills, a lack of shared vision and confusion over roles and responsibilities. This course will help delegates understand how to encourage more productive team working in the workplace. By the end of the course delegates will be able to describe what makes a High Performing Team and realistically assess their current strengths and weaknesses. Delegates will also be able to identify and plan behavioural changes that will improve the team’s performance. The course is experiential based with lots of learning by doing activities, reflecting and discussion. This will allow team members to get to know/improve their knowledge of team colleagues and energise or re-energise teams. Objectives  Know more about their colleagues Be able to describe what makes a high performing team Understand the importance of playing to different strengths and skills Recognise the need for clear and effective communication Content The Importance of Team Work The importance of teamwork The ‘third dimension’ – how working together has the potential to achieve more The dangers of an overly competitive workplace culture Psychological Safety Team Development Models Stages of Team Development Behaviours at each stage Dysfunctional Teams – what does good and bad look like Characteristics of high performing and elite teams Teaming and Leadership Skills Teaming competencies Active Listening Proactive Language Values and Beliefs Building Mental Fitness of Teams Becoming an inspirational leader Establishing a climate of psychological safety Team Challenges The importance of Accountability The Team Charter Smart Teams:Building TrustBuilding CollaborationOvercoming ConflictGaining CommitmentDeveloping Shared Goals The importance of shared goals Effective Team communication

Persuade your audience through storytelling

Managing teams and projects in line with a private, public or voluntary organisation's operational or departmental strategy.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Supporting and engaging with different parts of the organisation and interact with internal or external customer.