42 Data Analysis courses in London

This course covers the essential Excel data skills. Our course is interactive, instructor-led. This Excel Data course is on offered on intermediate level of Excel and concentrates on the Data Analytical capabilities of Excel. These concepts are daily used by data analysts and is a basic essential skill for data professionals. By the end, you'll be able to use Excel for analytics, data summaries, trends and statistics. You will create Excel data analytical spreadsheets and samples that you can use as examples at work.

Python Data Analytics with Python using Numpy, Pandas, Dataframes. Most attendees are in-work Data Professional. Private individuals are very welcome. Our Style: Hands-on, Practical Location: Online, Instructor-led

Python Data Analytics boot camp. 12 1 day lessons, learn Python Basics through to machine learning and front-ends. With practical project to give you full confidence and credibility.

Data analysis translates numbers and data into information that can be used to solve problems or track business performance. Data analysis produces graphs, charts, tables and reports. Data analysis is in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. The ability to pay attention to detail, communicate well and be highly organised are essential skills for data analysts. They not only need to understand the data but be able to provide insight and analysis through clear visual, written and verbal communication. This course provides the knowledge and skills to help you hone your data analysis skills.

Pivot tables are really very easy to create, but very powerful. By using Excel pivot tables, one can get very interesting and valuable business intelligence from your data in very little time. This short session aims to give you the techniques to use this valuable Excel tool creatively. It is enough time to learn a lot about Excel Pivot Tables, but there is very little time for other questions.

This course is designed for students who already have foundational knowledge and skills in Excel and who wish to perform robust and advanced data and statistical analysis with Microsoft Excel using PivotTables, use tools such as Power Pivot and the Data Analysis ToolPak to analyze data and visualize data and insights using advanced visualizations in charts and dashboards in Excel.

Overview Data and visual analytics are emerging fields concerned with analysing, modelling, and visualizing complex high-dimensional data. It can be analysed and visualised with many languages like Python, R Programming and more. This course will help to attain the skills and give in-depth knowledge to the participant's enhanced way of modelling, analysing and visualizing techniques.  The course will highlight practical challenges including composite real-world data and will also comprise several practical studies

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

This Access Course concentrates on using creative queries, to extract data from large data sets to get insight from your data and report it. Learn and explore various query techniques from where clauses, to aggregation, joins and more. This private style tuition helps to maximise the value you get from the day.

Project Quality Management In today's environment, quality is the responsibility of everyone. Project success is no longer just the fulfillment of a project on schedule, on budget, and within the scope. Today, projects aren't successful unless the customer's needs are met at the highest level of quality at the lowest cost to the organization. Project Managers must know customer needs, and manage to them throughout the project lifecycle, in order to gain acceptance. Project Quality Management provides an interactive, hands-on environment for participants to practice identification of critical quality requirements (quality planning), fulfillment of those requirements through well-designed processes (Quality Assurance), and statistical awareness of technical specifications of project deliverables (Quality Control). What You Will Learn You'll learn how to: Plan for higher quality project deliverables Measure key performance indicators on projects, processes, and products Turn data into useful project information Take action on analyzed data that will drive down non-value-added costs and drive up customer acceptance and satisfaction Reduce defects and waste in current project management processes Foundation Concepts Quality Defined Customer Focus Financial Focus Quality Management Process Management Cost of Quality Planning for Quality Project Manager Role in Planning Voice of the Customer Quality Management Plan Measurement System Accuracy Data Gathering Data Sampling Manage Quality Process Management Process Mapping Process Analysis Value Stream Mapping Standardization Visual Workplace and 5S Error Proofing (Poka-Yoke) Failure Mode and Effect Analysis Control Quality The Concept of Variation Common Cause Special Cause Standard Business Reports Tracking Key Measurements Control Charts Data Analysis Variation Root Cause Analysis Variance Management Designing for Quality