15 Estimator courses in Sheffield delivered Live Online

For new users and the curious. Hello, For new users and the curious. IMPORTANT: Firstly, once you are signed up, send us your requests on what you would like to see within the demonstration and learn on the course. We will then cater for your needs and answer them during the session. This is designed to be an introduction into how to start a room by room survey using the Heat Engineer app, then sending this survey to the online dashboard. We will then go through the different steps to complete this heat loss report. Optional pages will also be worked through and shown. Examples of how to select the flow temperature and the heat source (heat pumps and boilers) will be presented once the heat loss result is completed.

For new users and the curious. Hello, For new users and the curious. IMPORTANT: Firstly, once you are signed up, send us your requests on what you would like to see within the demonstration and learn on the course. We will then cater for your needs and answer them during the session. This is designed to be an introduction into how to start a room by room survey using the Heat Engineer app, then sending this survey to the online dashboard. We will then go through the different steps to complete this heat loss report. Optional pages will also be worked through and shown. Examples of how to select the flow temperature and the heat source (heat pumps and boilers) will be presented once the heat loss result is completed.

For new users and the curious. Hello, For new users and the curious. IMPORTANT: Firstly, once you are signed up, send us your requests on what you would like to see within the demonstration and learn on the course. We will then cater for your needs and answer them during the session. This is designed to be an introduction into how to start a room by room survey using the Heat Engineer app, then sending this survey to the online dashboard. We will then go through the different steps to complete this heat loss report. Optional pages will also be worked through and shown. Examples of how to select the flow temperature and the heat source (heat pumps and boilers) will be presented once the heat loss result is completed.

About this Training Course The two elements which consistently remain in the forefront of every executive, tasked to manage a project, are 'Cost' and 'Time'. There is probably no disagreement or a need to prove that a strong correlation exists between Cost and Schedule. The mechanics of capturing cost during the execution is not that difficult a task, as it is when undertaking Design, Engineering, Estimating, Planning, Scheduling, and seeking financing for the project. The corporate management is expected to provide realistic, reliable and risks adjusted projections of a project's overall financial performance. This 3 full-day course and workshop is developed to make the delegates walk through the engineering formulas and equations to become a professional in managing estimates, projections, risks, uncertainties and secure financing for high value, high risk projects, from both On-shore and Offshore Oil & Gas industry segments. This course can also be offered through Virtual Instructor Led Training (VILT) format. Training Objectives Objectives of this Training Course: This course and the workshops are developed for the participants to gain comprehensive understanding of the field of Cost Engineering and its impact on the project execution, financing and risk management. The underlying objectives are: Enhance familiarization with mechanics of cost engineering in developing budgets and Project Cost Models. Gain understanding of diversity in financing of EPC Projects in context of Offshore and Onshore Oil & Gas projects in international arena. Identify risks associated with cost estimates and costing elements, with an objective of developing project strategies and minimizing the exposure to escalation of prices and market factors. Appreciate the correlation between cost and schedule, resulting in the delivery of contractual obligations. Develop competency to manage risks of costs and time overrun, by implementing appropriate cost control mechanism. Target Audience If you are responsible for undertaking one or more of the following functions in the Oil & Gas and Offshore & Marine Industry, you can't afford to miss this course: Management Accountants, Finance Managers, Cost Controllers, Project Directors, Contracts & Projects Managers, Estimators, Planners and Risk Managers. Course Level Intermediate Training Methods Unique Features of this Enhanced Course Curriculum: This course and the workshops are developed for the participants to gain comprehensive understanding of the field of Cost Engineering and its impact on the project execution, financing and risk management. The underlying objectives are: Enhance familiarization with mechanics of cost engineering in developing budgets and Project Cost Models. Gain understanding of diversity in financing of EPC Projects in context of Offshore and Onshore Oil & Gas projects in international arena. Identify risks associated with cost estimates and costing elements, with an objective of developing project strategies and minimizing the exposure to escalation of prices and market factors. Appreciate the correlation between cost and schedule, resulting in the delivery of contractual obligations. Develop competency to manage risks of costs and time overrun, by implementing appropriate cost control mechanism. Trainer Principal Management Consultant Chartered Valuer and Appraiser (CVA) FACICA | FAMTAC | FAIADR | M.S.I.D | Member, AIEN LL.M. (IP Law), M. Sc. (Maritime Studies), M. Tech (Knowledge Engineering), MBA, First Class CoC (MCA, UK), B. E. (Elect) Your expert course leader, during the last 47 year period, has worked and consulted in the industry verticals encompassing: Technology, Oil & Gas Exploration & Production, Petrochemical Process Plants and Power Plant Construction Projects, Logistics & Warehousing, Marine, Offshore, Oil & Gas Pipelines, Infrastructure Development Projects (Ports, Offshore Supply Bases, Oil & Gas Terminals and Airports etc), EPCIC Contracts, and Shipyards, in South East Asia, Africa, Middle East, Americas and Europe. He serves as the Principal Management Consultant with a management consultancy in Hong Kong and Singapore, specialising in the fields of corporate management consultancy, international contracts reviews and alternative dispute resolutions services. He undertakes special assignments for conducting audits and valuation of intangible properties involving proprietary processes for licensed production, and licensing of intellectual property rights (IP Rights) in patents, trademarks, and industrial designs. He is frequently engaged for assignments like due diligence, acquisitions, mergers, resolving various operational issues, technology transfer and agency services contracts reviews, cost controls, and enhancement of Supply Chain Management. He has been conferred the credentials of Chartered Valuer & Appraiser (CVA) by SAC and IVAS, in accordance with the international valuation standards setting body IVSC. His consulting experience includes Charterparty Management, Business Process Re-engineering, Diversifications, Corporate Development, Marketing, Complex Project Management, Feasibility Studies, Dispute Resolutions and Market Research. He has successfully assisted Marine and offshore E & P clients in managing contractual disputes arising from various international contracts for upgrading & conversion projects. He continues to be actively engaged in claims reviews, mediation, arbitration, litigation, and expert witness related assignments, arising from international contracts and Charterparty Agreements. He graduated with a Bachelor's degree in Electrical Engineering, MBA in General Management, Master of Technology in Knowledge Engineering, Master of Science in Maritime Studies, and LL.M. (IP Law). He also holds professional qualifications in Business Valuations and Appraisers for CVA, arbitration, law, and marine engineering, including the Chief Engineer's First-Class Certificate of Competency (MCA, UK). He is further qualified and accredited as Certified International Arbitrator, Chartered Arbitrator, Sports arbitrator under CAS Rules, WIPO Neutral, Australian Communications and Media Authority (ACMA) Bargaining Code Arbitrator, Accredited Adjudicator and Accredited Mediator (Malaysia). He is admitted to the international panels of arbitrators and neutrals with WIPO, Geneva; ACICA, AMTAC and ACMA, Australia; BVIAC (British Virgin Islands); JIAC (Jamaica); HKIAC Hong Kong; AIAC, Malaysia; AIADR, Malaysia; KCAB, Seoul, South Korea; ICA, Delhi, India; ICC (Singapore); SISV, Singapore; SCMA, Singapore; SCCA, Saudi Arabia; VIAC Vienna, Austria; Thailand Arbitration Centre (THAC), and Mediator with AIAC Malaysia, CMC, and SIMI Singapore. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

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