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Big data certification for non-programmers, business analysts, testers, and SQL developers

About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. 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

24 Hours Left! Enrol Now in this Data Analyst Course! Today's world revolves around data. Organisations across all industries increasingly depend on Data Analysts to make crucial business decisions. So, if you want to collect, evaluate, and understand data using various methods and business intelligence tools and further enhance your Data Analyst skills, our comprehensive Data Analyst course is perfect for you. Designed for success, this Data Analyst course covers everything from basics to advanced topics in Data Analyst. Each lesson in this Data Analyst course is crafted for easy understanding, enabling you to become proficient in Data Analyst. Whether you are a beginner or looking to sharpen your existing skills, this Data Analyst is the ideal choice. Learning Outcomes of Data Analyst course: Introduce yourself to the critical concepts of analysing data. Know how to build, change, and format charts. Become well-versed in working with multiple worksheets and workbooks. Master the key aspects of modifying a worksheet. Discover how to work with lists. Understand how to use PivotTables, PivotCharts, Lookup Functions, and Formula Auditing. Explore the process of applying data validation, searching for invalid data and formulas with errors, and working with macros. Find out how to automate workbook functionality, create sparklines, and map data. Why Prefer this Course? Get a Free CPD Accredited Certificate upon completion of Data Analyst Get a free student ID card with Data Analyst Training Get instant access to this Data Analyst course. Learn Data Analyst from anywhere in the world The Data Analyst is affordable and simple to understand The Data Analyst is an entirely online, interactive lesson with voiceover audio Lifetime access to the Data Analyst course materials The Data Analyst comes with 24/7 tutor support Start your Data Analyst Diploma journey straightaway! This Data Analyst's curriculum has been designed by Data Analyst experts with years of Data Analyst experience behind them. The Data Analyst course is extremely dynamic and well-paced to help you understand Data Analyst with ease. You'll discover how to master the Data Analyst skill while exploring relevant and essential topics. *** Course Curriculum *** Here is the curriculum breakdown of this course: Module 01: Modifying a Worksheet Module 02: Working with Lists Module 03: Analysing Data Module 04: Visualizing Data with Charts Module 05: Using Pivot Tables and Pivot Charts Module 06: Working with Multiple Worksheets and Workbooks Module 07: Using Lookup Functions and Formula Auditing Module 08: Automating Workbook Functionality Module 09: Creating Sparklines and Mapping Data Module 10: Forecasting Data Assessment Process After passing the Diploma in Data Analyst exam, you will be able to request a certificate with additional cost that has been endorsed by the Quality Licence Scheme. CPD 150 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Data Analyst Diploma Anyone willing to gain a better understanding. Already working in this field and want to learn more about Data Analyst Requirements Data Analyst Diploma You will not need any prior background or expertise. Career path Career Path: Data Analyst Data Scientist Data Analytics Manager Certificates CPD Accredited Certificate Digital certificate - £10 Diploma in Data Analysis at QLS Level 5 Hard copy certificate - £119 Show off Your New Skills with a Certificate of Completion Endorsed Certificate of Achievement from the Quality Licence Scheme After successfully completing the Diploma in Data Analysis at QLS Level 5, you can order an original hardcopy certificate of achievement endorsed by the Quality Licence Scheme. The certificate will be home-delivered, with a pricing scheme of - 119 GBP inside the UK 129 GBP (including postal fees) for international delivery CPD Accredited Certificate Upon finishing the Data Analyst (Data Analytics)course, you need to order to receive a Certificate Accredited by CPD that is accepted all over the UK and also internationally. The pricing schemes are: 29 GBP for Printed Hardcopy Certificate inside the UK 39 GBP for Printed Hardcopy Certificate outside the UK (international delivery)

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Curriculum of Bundle Course 01: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding Course 02: Python for Data Analysis Welcome, Course Introduction & overview, and Environment set-up Python Essentials Python for Data Analysis using NumPy Python for Data Analysis using Pandas Python for Data Visualization using matplotlib Python for Data Visualization using Seaborn Python for Data Visualization using pandas Python for interactive & geographical plotting using Plotly and Cufflinks Capstone Project - Python for Data Analysis & Visualization Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Python for Machine Learning - scikit-learn - Logistic Regression Model Python for Machine Learning - scikit-learn - K Nearest Neighbors Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Python for Machine Learning - scikit-learn - K Means Clustering Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Recommender Systems with Python - (Additional Topic) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Course 03: Statistical Analysis Module 01: The Realm of Statistics Module 02: Basic Statistical Terms Module 03: The Center of the Data Module 04: Data Variability Module 05: Binomial and Normal Distributions Module 06: Introduction to Probability Module 07: Estimates and Intervals Module 08: Hypothesis Testing Module 09: Regression Analysis Module 10: Algorithms, Analytics and Predictions Module 11: Learning From Experience: The Bayesian Way Module 12: Doing Statistics: The Wrong Way Module 13: How We Can Do Statistics Better Course 04: SQL NoSQL Big Data and Hadoop Module 01: Introduction Module 02: Relational Database Systems Module 03: Database Classification Module 04: Key-Value Store Module 05: Document-Oriented Databases Module 06: Search Engines Module 07: Wide Column Store Module 08: Time Series Databases Module 09: Graph Databases Module 10: Hadoop Platform Module 11: Big Data SQL Engines Module 12: Distributed Commit Log Module 13: Summary Course 05: Complete Microsoft Power BI 2021 Module 01: Introduction Module 02: Preparing our Project Module 03: Data Transformation - The Query Editor Module 04: Data Transformation - Advanced Module 05: Creating a Data Model Module 06: Data Visualization Module 07: Power BI & Python Module 08: Storytelling with Data Module 09: DAX - The Essentials Module 10: DAX - The CALCULATE function Module 11: Power BI Service - Power BI Cloud Module 12: Row-Level Security Module 13: More data sources Module 14: Next steps to improve & stay up to date Course 06: Data Analysis in Excel Level 3 Course Modifying a Worksheet Working with Lists Analyzing Data Visualizing Data with Charts Using PivotTables and PivotCharts Working with Multiple Worksheets and Workbooks Using Lookup Functions and Formula Auditing Automating Workbook Functionality Creating Sparklines and Mapping Data Forecasting Data Course 07: Data Analytics with Tableau Module 01: Introduction to the Course Module 02: Project 1: Discount Mart (Sales and Profit Analytics) Module 03: Project 2: Green Destinations (HR Analytics) Module 04: Project 3: Superstore (Sales Agent Tracker) Module 05: Northwind Trade (Shipping Analytics) Module 06: Project 5: Tesla (Stock Price Analytics) Module 07: Bonus: Introduction to Database Concepts Module 08: Tableau Stories Course 08: Basic Google Data Studio Module 01: Introduction to GDS Module 02: Data Visualization Module 03: Geo-visualization Module 04: A Socio-Economic Case Study Course 09: Business Analytics Module 01: What is business analysis? Module 02: Strategy analysis Module 03: Collaboration Module 04: Requirements analysis and Design definition Module 05: Requirements lifecycle management Module 06: Solution quality Module 07: Stakeholder management Module 08: BA Governance Module 09: Legal notes and Copyright information Course 10: Complete Introduction to Business Data Analysis Level 3 Module 1: Statistics Fundamentals Module 2: Data Analysis Module 3: Probability Module 4: Random Variables and Discrete Distributions Module 5: Continuous Distributions Module 6: Sampling Distributions Module 7: Confidence Interval Module 8: Hypothesis Testing with One Sample Module 9: Hypothesis Testing with Two Samples Module 10: The Chi-Square Distribution Module 11: F Distribution and One-Way ANOVA Module 12: Correlation analysis Module 13: Simple Linear Regression Analysis Course 11: Business Intelligence and Data Mining Masterclass Module 01: What is Business Intelligence? Module 02: Starting Case in understanding BI needs in diff phase of business Module 03: Decision Making Process and Need of IT systems Module 04: Problem Structure and Decision Support System Module 05: Introduction to BI Applications Module 06: Dashboard presentation systems Module 07: Different Types of Charts used in 131 Dashboards Module 08: Good Dashboard and BSC Module 09: Examples of Bad Dashboards 1 Module 10: Examples of Bad Dashboards 2 And much more... Course 12: Research Methods in Business Section 01: Applied Project & Research Methods in Business Section 02: Writing a Purpose / Quantitative and Qualitative Research Approaches Section 03: Mixed Method Research Approaches, Ethical Considerations & Writing Effectively Written Methodology Part 3 !@@ Section 04: Writing Data Collection Tools, Qualitative & Quantitative Data Analysis Section 05: Comparing Findings to Literature and Writing the Final Paper Course 13: Computer Science: Graph Theory Algorithms Module 00: Promo Module 01: Introduction Module 02: Common Problem Module 03: Depth First Search Module 04: Breadth First Search Module 05: Breadth First Search Shortest Path on a Grid And much more... Course 14: Data Protection and Data Security Level 2 GDPR Basics GDPR Explained Lawful Basis for Preparation Rights and Breaches Responsibilities and Obligations CPD 165 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Data Analysis and Data Science bundle. Requirements Our Data Analysis and Data Science course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. Career path Having this Data Analysis and Data Science expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included You will get the PDF Certificate for the title course (Introduction to Data Analysis) absolutely Free! Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Introduction to Data Analysis) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

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From creating interactive dashboards to conducting in-depth data analysis, learners will acquire the proficiency needed to navigate the complexities of modern data analytics with confidence and precision. In addition to honing technical expertise, participants will explore the realm of big data analytics, leveraging powerful tools such as PySpark, Tableau Desktop, and MongoDB. Dive deep into the world of data processing and interpretation, equipping yourself with the skills necessary to tackle large-scale datasets effectively. Through hands-on learning experiences and engaging course content of Microsoft Power BI & Microsoft Excel, individuals will emerge equipped to tackle real-world challenges in data analytics, poised to make meaningful contributions to their organizations' success. CPD 150 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Microsoft Power BI & Microsoft Excel course is ideal for: Professionals seeking to enhance their proficiency in Microsoft Power BI & Microsoft Excel. Individuals aspiring to pursue careers in data analytics and business intelligence. Students and recent graduates looking to bolster their skill set for employment opportunities. Entrepreneurs aiming to leverage data-driven insights for business growth. Professionals seeking to advance their career prospects through enhanced communication and presentation skills. Individuals keen on developing a strategic approach to career development and networking. Requirements You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment. Career path Upon completion of this Microsoft Power BI & Microsoft Excel bundle course, participants can pursue diverse career paths, including: Data Analyst Business Intelligence Analyst Data Scientist Financial Analyst Business Consultant Project Manager IT Manager Certificates 13 CPD Quality Standard Certificates Digital certificate - Included 2 QLS Endorsed Certificates Hard copy certificate - Included

Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning

Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning