16 Technical Manager courses in Edinburgh delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for This basic course is intended for anyone who is new to software development and wants, or needs, to gain an understanding of the fundamentals of coding and basics of Java and object-oriented programming concepts. Attendees might include: Technically-minded attendees who want or who want to begin the process of becoming an OO application developer Technical team members from non-development roles, re-skilling to move into software and application development roles within an organization Recent college graduates looking to apply their college experience to programming skills in a professional environment, or perhaps needing to learn the best practices and standards for programming within their new organization Technical managers tasked with overseeing programming teams, or development projects, where basic coding knowledge and exposure will be useful in project oversight or communications needs Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in basic coding with Java, coupling the most current, effective techniques with the soundest industry practices. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn: The steps involved in the creation and deployment of a computer program What OO programming is and what the advantages of OO are in today's world To work with objects, classes, and OO implementations The basic concepts of OO such as encapsulation, inheritance, polymorphism, and abstraction The basic constructs that all programming languages share The basic Java constructs supporting processing as well as the OO orientation How to use Java exception handling About and how to use classes, inheritance and polymorphism About use collections, generics, autoboxing, and enumerations How to take advantage of the Java tooling that is available with the programming environment being used in the class Getting Started with Programming, OO and Java Basics for Non-Developers is a skills-focused, hands-on coding course that teaches students the fundamentals of programming object oriented (OO) applications with Java to a basic level, using sound coding skills and best practices for OO development. This course is presented in a way that enables interested students to embrace the fundamentals of coding as well as an introduction to Java, in a gentle paced environment that focuses on coding basics.Students are introduced to the application development cycle, structure of programs, and specific language syntax. The course introduces important algorithmic constructs, string and character manipulation, dynamic memory allocation, standard I/O, and fundamental object-oriented programming concepts. The course explains the use of inheritance and polymorphism early on so the students can practice extensively in the hands-on labs. Structured programming techniques and error handling are emphasized. The course includes the processing of command line arguments and environment variables, so students will be able to write flexible, user-friendly programs. Students will leave this course armed with the required skills to begin their journey as a Java programmer using modern coding skills and technologies. Introduction to Computer Programming Introduction to Programming Programming Tools Programming Fundamentals Thinking About Objects Program Basics Programming Constructs Java: A First Look The Java Platform Using the JDK The Eclipse Paradigm Writing a Simple Class OO Concepts Object-Oriented Programming Inheritance, Abstraction, and Polymorphism Getting Started with Java Adding Methods to the Class Language Statements Using Strings Specializing in a Subclass Essential Java Programming Fields and Variables Using Arrays Java Packages and Visibility Advanced Java Programming Inheritance and Polymorphism Interfaces and Abstract Classes Exceptions Java Developer's Toolbox Utility Classes Enumerations and Static Imports Formatting Strings Collections and Generics Introduction to Generics Collections

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

Duration 1 Days 6 CPD hours This course is intended for This overview-level course is ideally suited for professionals seeking an introduction to microservices architecture and its application within a business context. Ideal attendee roles include software developers, system architects, technical managers, and IT professionals who are part of teams transitioning to a microservices approach. It's also an excellent starting point for non-technical roles such as product owners or business analysts who work closely with technical teams and want to better understand and become conversant in the language and principles of microservices. Overview This course combines engaging instructor-led presentations and useful demonstrations with engaging group activities. Throughout the course you'll explore: Understand the Basics of Microservices: Get to know the fundamental principles and characteristics of microservices and how they revolutionize traditional software development approaches. Explore the Design of Microservices: Gain an overview of how microservices are designed based on business requirements and what makes them unique in the software architecture world. Overview of Managing and Scaling Microservices: Get an introduction to how microservices are managed and scaled independently, and understand the significance of these features in your business operations. Familiarize with the Microservices Ecosystem: Learn about the typical patterns, best practices, and common pitfalls in the microservices world, setting a foundation for future learning and implementation. Introduction to Microservices in a Business Context: Acquire a basic understanding of how microservices can be aligned with specific business capabilities, and get a glimpse into how they can coexist with legacy systems in a business setting. Microservices have rapidly emerged as a popular architectural style, breaking down applications into small, independent services that can be developed, deployed, and scaled individually. Microservices offer a robust method to address a variety of projects, such as e-commerce platforms and content management systems, enhancing scalability and boosting productivity. This technology, when employed correctly, can greatly increase software delivery speed and system resilience, making it a crucial skill set for modern technology professionals.Understanding Microservices - A Technical Overview is a one-day course ideally suited for technical professionals seeking an introduction to microservices architecture and its application within a business context. Under the guidance of an industry expert, this engaging class combines lecture-style learning with lively demonstrations, case study review and group discussions.Throughout the course you?ll explore the principles and characteristics that define microservices, how to identify suitable projects for a microservices approach, the factors to consider when designing them, and the strategies to effectively manage and scale them within complex systems. You?ll also learn about the best practices, patterns, and anti-patterns, arming you with the knowledge to make the right architectural choices. This course also explores the real-world implementation of microservices in a business enterprise. We'll discuss how to align the application of microservices with your organization's specific business capabilities, and offer strategies for smoothly integrating this technology within existing legacy systems. Introduction to Microservices Understand what microservices are and their role in modern software development. Introduction to Microservices: what they are and why they matter. Monolithic vs Microservices: highlighting the shift and benefits. Key principles and characteristics of microservices. Identifying suitable applications for microservices transformation. Demo: Analyzing a sample application and identifying potential microservices Architecting and Managing Microservices Learn the basic strategies for scaling and managing microservices. Scaling Microservices: from a single service to hundreds. Key components of a microservices architecture. Introduction to resilience patterns: Circuit-Breakers and Bulkheads. Load management and provisioning in a microservices setup. Understanding the role of cloud services in microservices. Optional Demo: Illustrating how a microservice-based application scales in real-time Designing Microservices Learn the key aspects to consider when designing microservices. Defining microservice boundaries: Deciding the scope of a microservice. Communication patterns in microservices. Understanding Microservice endpoints. Exploring data stores and transaction boundaries in microservices. Overcoming challenges in Microservices design. Demo: Designing microservices for a hypothetical business requirement Implementing Microservices in a Business Enterprise Understand the process and considerations for implementing microservices in an enterprise context. Assessing enterprise readiness for microservices. Building the business case for microservices: strategic advantages and potential challenges. Aligning microservices with business capabilities. Organizational changes: Team structures and processes for microservices. Dealing with Legacy Systems: Strategies for microservices integration. Demo: Exploring a case study of successful microservices implementation in a business enterprise The Microservices Ecosystem Understand the key tools and best practices in the Microservices ecosystem. Understanding the typical Microservices Stack. Monitoring and Logging in Microservices. Introduction to Docker: Containerization of Microservices. Deployment strategies in a Microservices setup. Introduction to Orchestration in Microservices Demo: Containerizing and deploying a simple microservice Microservices Deployment Strategies Understand various ways to safely introduce changes in a microservices environment. The concept of Blue-Green Deployment: changing services without downtime. Canary Releases and Feature Toggles: slowly rolling out changes to users. Database changes in a microservices environment: keeping data consistent. Demo: Examining various deployment strategies Microservices Best Practices and DevOps Learn key strategies to ensure a smooth operation of your microservices setup. The DevOps culture in Microservices: collaboration for efficiency. Defining a Minimum Viable Product in a Microservices setup: building small, delivering fast. Dealing with data in a distributed setup: managing Data Islands. The importance of Continuous Integration/Continuous Delivery in a microservices setup. Governance: Keeping track of your services and their consumers. Demo: Visualizing a simple continuous delivery pipeline Microservices Patterns and Anti-Patterns Learn about common do's and don'ts when working with microservices. Understanding patterns that help with efficient microservices operation. Recognizing and avoiding anti-patterns that can hinder performance. Dealing with common challenges: dependencies between services, managing service boundaries. Demo: Examples of real-world patterns and anti-patterns Simple Overview of OAuth and OpenID for Microservices Introduction to OAuth and OpenID: What they are and why they matter in Microservices. The role of tokens in OAuth 2.0: How they help in securing communications. A simplified look at OpenID Connect: Linking identities across services. Demo

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

What do engineers and project managers need to know of finance? 'Nothing - leave it to the accountants!' No, no, no! Engineers must be conversant with the terminology and statements that accountants use. Technical expertise in projects, service delivery, production or other areas can only really be harnessed if the managers understand the accounting and reporting that drives businesses. This course gives the necessary understanding to project, production and technical managers. It develops their skills in understanding financial and management accounting. Accountants may not always like it but a major part of their work is to be the 'servants of business' and to gather, compile and present your figures. So you must understand the figures - they belong to you, your processes or projects. There are many reasons for maintaining accurate accounts. This course focuses on the strategic issues (those over-used words) - what figures reveal about the drivers of business and what they reveal about the day-to-day issues that accountants bother you with. The course will enhance your understanding of finance and of the accounting issues which affect your projects, production and technical areas of business. This course will help you: Understand the business world in figures - make sense of what the accountants are telling you Appreciate what drives business - and how this affects your role in your part of the business Relate your activities to the success of the business - through figures Gain the skills to advance in management - financial awareness is a 'must have' if you are to progress in your career 1 What do accountants do? The finance function, types of accountant, financial v management accounting and the treasury function Understanding the role of the finance function and how the information you provide may be used 2 The basic financial statements Balance sheets and income statements (P&L accounts) What they are, what they contain and above all what they can reveal - how to read them The accounting process - from transactions to financial statements What underpins the statements - accounting systems and internal controls 3 Why be in business - from a financial perspective The driving forces behind financial information Performance measures - profitability, asset utilisation, sales and throughput, managing capital expenditure 4 Accounting rules - accounting standards Accounting concepts and the accounting rules: accruals, 'going concern' - substance over form and other 'desirable qualities' Accruals - why the timing of a transaction is so important to the finance function Depreciation and amortisation - the concepts and practice Accounting standards - the role of International Financial Reporting Standards 5 Cash The importance of cash flow - working capital management Cash flow statements - monitoring overall cash flows Raising cash - levels of borrowing, gearing Spending cash - an outline of capital expenditure appraisal 6 Budgeting Why budget? - good and bad practice Determining why budgets play a key role and should not be simply an annual ritual Justifying your budgets - the link between the strategic plan and day-to-day budgeting - alignment of company culture Budgets as motivators - the importance of the right culture Techniques to improve budgeting - whether day-to-day or capital budgeting 7 Costing The type and detail of costing very much depends on your business - eg, manufacturing piston rings is quite different from the construction of a power plant Issues with overhead allocation Accounting for R&D 8 Reading financial statements Annual financial statements - why they are produced, what's in them and what you should look for Learning what a set of accounts reveals about a company's current situation, profitability and future prospects 9 Performance measurement - analytical reviews and ratio analysis ROI/ROCE Profitability, margins and cost control Sales - asset turnover Efficiency (asset / stock turnover, debtor / creditor days) 'City' measures Investment (interest / dividend cover, earnings per share, dividend yield)