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Course code: 000616
School of Computer Science
ITSP007 – Fundamentals of Machine Learning and Data Science for the Oil and Gas Industry
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Currently, this course is conducted only in an intracorporate format.
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What this course about?

The advent of the digital age and the exponential growth of data has sparked the transformation of numerous industries, with the Oil and Gas sector being no exception. Our comprehensive training program, "Fundamentals of Machine Learning and Data Science for the Oil and Gas Industry," is designed to equip professionals with the tools and skills needed to navigate and leverage this data-centric landscape.

The program is organised into six modules, beginning with foundational data science skills. Here, participants will explore Python and R, two powerful programming languages essential to modern data science. The first module also introduces participants to the world of Excel for basic data manipulation—a crucial, often overlooked, aspect of data science.

The second module delves into data visualization and analysis, utilizing advanced features of Excel and the dynamic capabilities of Power BI. In today's data-driven decision-making processes, the ability to present data in a meaningful, visually appealing manner is of paramount importance. With Power BI, participants will learn to create interactive visualizations and dashboards, thereby enhancing their ability to communicate complex data insights effectively.

From there, the program transitions to the practical applications of machine learning, with separate modules dedicated to regression and classification techniques. Participants will learn to predict the mass of oil, carry out facies classification, and handle imbalanced data scenarios, which are common in the Oil and Gas industry.

In the fifth module, the program explores the increasingly important field of Natural Language Processing (NLP), teaching participants how to analyse textual data and classify injury reports—a key aspect of Health, Safety, and Environment (HSE) management in the Oil and Gas sector.

The final module introduces participants to the cutting-edge field of Deep Learning, using TensorFlow to apply techniques such as image segmentation for identifying salt deposits in seismic sessions.

By the end of this training course, participants will have gained a broad yet detailed skill set. They will be equipped to apply Python and R for data science tasks, perform data manipulation and analysis using Excel, and create dynamic visualizations with Power BI. In addition, they will have a firm grasp of applying various machine learning algorithms, conducting NLP for textual data, and implementing deep learning techniques for image analysis.

This training program is ideal for a wide range of professionals, from engineers and data scientists to researchers and managers. Even those transitioning their careers or starting their journey in data science will find the course beneficial. Ultimately, this program aims to cultivate a data-driven mindset, enabling professionals to leverage machine learning and data science to drive innovation and efficiency in the Oil and Gas industry.

Who is this course for?
This course is designed for a variety of professionals who are interested in leveraging data science and machine learning within the Oil and Gas industry. This includes but is not limited to:
- Oil and Gas Professionals: Engineers, geoscientists, and other technical professionals in the Oil and Gas industry who wish to integrate data science into their work to make better-informed decisions and improve operational efficiency.
- Data Analysts/Scientists: Professionals in the field of data analysis or data science who are looking to specialize or gain expertise in the Oil and Gas industry.
- IT Professionals: IT professionals working in or aiming to work in Oil and Gas companies who want to upskill in machine learning and data science to contribute more effectively to their organizations.
- Researchers and Academics: Those involved in research related to the Oil and Gas industry who are interested in incorporating machine learning techniques into their research methodologies.
- Management Professionals: Professionals in managerial or decision-making roles in the Oil and Gas industry who want to understand the potential of machine learning and data science to improve business strategies and outcomes.
What will you learn?
  • Utilize programming languages: Develop a strong foundation in Python and R, enabling them to write code, manipulate data, and implement machine learning algorithms
  • Employ data analysis techniques: Analyze and interpret complex data sets using a variety of statistical techniques, and visualize this data for clearer comprehension and presentation
  • Handle data effectively in Excel: Execute basic and advanced data manipulation tasks using Excel, a widely-used tool in the industry
  • Create insightful visualizations with Power BI: Build interactive data visualizations and dashboards to convey data-driven insights using Power BI
  • Apply machine learning algorithms: Build, validate, and interpret machine learning models including regression, classification, and deep learning algorithms using popular libraries like Scikit-Learn and TensorFlow
  • Conduct Natural Language Processing (NLP): Analyze textual data, implement TF-IDF techniques, and apply NLP for practical use-cases like injury report classification
  • Implement deep learning for image analysis: Use TensorFlow to apply deep learning techniques for image segmentation in seismic sessions
  • Solve real-world problems in the Oil and Gas industry: Apply data science and machine learning techniques to solve specific problems in the Oil and Gas industry such as predicting oil yield, facies classification, and injury report analysis
  • Make informed decisions: Choose the appropriate data science or machine learning tool or technique for various scenarios, and interpret the results to make data-driven decisions
Course outline
  • Introduction to Python for Data Science
  • Introduction to R for Data Science
  • Basic Data Manipulation with Excel
  • Advanced Data Analysis with Excel
  • Introduction to Data Visualization in Power BI
  • Advanced Data Visualization Techniques in Power BI
  • Understanding Data and Its Nature
  • Data Cleaning Techniques
  • Introduction to Linear Regression
  • Advanced Regression with Gradient Boosting
  • Model Interpretation and Evaluation
  • Data Imputation Techniques
  • Effective Feature Engineering
  • Logistic Regression and Handling Imbalanced Data
  • Advanced Classification with Gradient Boosting
  • Introduction to Natural Language Processing
  • Text Analysis using TF-IDF
  • Classification of Injury Reports
  • Introduction to Tensorflow
  • Image Segmentation in Seismic Sessions
Eni
Total
Eni
Endesa
Shell
Chevron
Gas Natural
Iberdrola
Eni
Inpex
Eni
Exonmobile

Training can take place in 4 formats:

  • Self-paced
  • Blended learning
  • Instructor-led online (webinar)
  • Instructor-led offline (classroom)

Description of training formats:

  • Self-paced learning or e-Learning means you can learn in your own time and control the amount of material to consume. There is no need to complete the assignments and take the courses at the same time as other learners.
  • Blended learning or "hybrid learning" means you can combine Self-paced learning or e-Learning with traditional instructor-led classroom or webinar activities. This approach requires physical presence of both teacher and student in physical or virtual (webinars) classrooms or workshops. Webinar is a seminar or presentation that takes place on the internet, allowing participants in different locations to see and hear the presenter, ask questions, and sometimes answer polls.
  • Instructor-led training, or ILT, means that the learning can be delivered in a lecture or classroom format, as an interactive workshop, as a demonstration under the supervision and control of qualified trainer or instructor with the opportunity for learners to practice, or even virtually, using video-conferencing tools.

When forming groups of students, special attention is paid to important criteria - the same level of knowledge and interests among all students of the course, in order to maintain stable group dynamics during training.

Group dynamics is the development of a group in time, which is caused by the interaction of participants with each other and external influence on the group. In other words, these are the stages that the training group goes through in the process of communicating with the coach and among themselves.

The optimal group size for different types of training:

  • Self-paced / E-learning: 1
  • Instructor-led off-line (classroom): 6 – 12
  • Instructor-led on-line (webinar): 6 – 12
  • Blended learning: 6 – 12
  • Workshop: 6 – 12
  • On-the-job: 2 – 4
  • Simulator: 1 – 2

Feedback in the form of assessments and recommendations is given to students during the course of training with the participation of an instructor and is saved in the course card and student profile.

In order to control the quality of the services provided, students can evaluate the quality and training programme. Forms of assessment of the quality of training differ for courses with the participation of an instructor and those that are held in a self-paced format.

For courses with an instructor, start and end dates are indicated. At the same time, it is important to pay attention to the deadlines for passing tests, exams and practical tasks. If the specified deadlines are missed, the student may not be allowed to complete the entire course programme.

A personal account is a space for storing your training preferences, test and exam results, grades on completed training, as well as your individual plan for professional and personal development.

Users of the personal account have access to articles and blogs in specialized areas, as well as the ability to rate the completed training and leave comments under the articles and blogs of our instructors and technical authors

Registered users of a personal account can have various roles, including the role of a student, instructor or content developer. However, for all roles, except for the student role, you will need to go through an additional verification procedure to confirm your qualifications.

Based on the results of training, students are issued a certificate of training. All training certificates fall into three main categories:

  • Certificate of Attendance - students who successfully completed the course but did not pass the tests and exams can apply for a certificate of attendance.
  • Certificate of Completion - students who have successfully completed a course could apply for a Certificate of Completion, this type of certificate is often required for compliance training.
  • Verified Certificate - it is a verified certificate that is issued when students have passed exams under the supervision of a dedicated proctor.

You can always download a copy of your training certificate in PDF format in your personal account.

You will still have access to the course after completing it, provided that your account is active and not compromised and Tecedu is still licensed for the course. So if you want to review specific content in the course after completing it, or do it all over again, you can easily do so. In rare cases, instructors may remove their courses from the Tecedu marketplace, or we may need to remove a course from the platform for legal reasons.

During the training, you may encounter various forms of testing and knowledge testing. The most common assessment methods are:

  • preliminary (base-line assessment) - to determine the current level of knowledge and adapt the personal curriculum
  • intermediate - to check the progress of learning
  • final - to complete training and final assessment of knowledge and skills, can be in the form of a project, testing or practical exam

Travel to the place of full-time training is not included in the cost of training. Accommodation during full-time studies can be included in the full board tuition fees.

While Tecedu is not an accredited institution, we offer skills-based courses taught by real experts in their field, and every approved, paid course features a certificate of completion or attendance to document your accomplishment.

You can preview samples of the training materials and review key information about the course on our website. You can also review feedback and recommendations from students who already completed this course.

We want you to be happy, so almost all purchased courses can be returned within 30 days. If you are not satisfied with the course, you can request a refund, provided the request complies with our return policy.

The 30-day money back policy allows students to receive quality teaching services with minimal risk, we must also protect our teachers from fraud and provide them with a reasonable payment schedule. Payments are sent to instructors after 30 days, so we will not process refund requests received after the refund period.

We reserve the right, in our sole discretion, to limit or deny refund requests in cases where we believe there is refund abuse, including but not limited to the following:

  • A significant portion of the course has been consumed or downloaded by a student before the refund was requested.
  • Multiple refunds have been requested by a student for the same course.
  • Excessive refunds have been requested by a student.
  • Users whose account is blocked or access to courses is disabled due to violation of our Terms and Conditions or the Rules of Trust and Security.
  • We do not grant refunds for any subscription services.
  • These refund restrictions will be enforced to the extent permitted by applicable law.

We accept most international credit and debit cards like Visa, MasterCard, American Express, JCB and Discover. Bank Transfers also may be an option.

Smart Virtual Classroom (open digital / virtual classroom).

Conducting classes is based on the fact that the teacher demonstrates text, drawings, graphics, presentations on an interactive board, while the content appears in the student's electronic notebook. A specially designed digital notepad and pen are used to create and edit text and images that can be redirected to any surface via a projector.

Classes are live streamed online, automatically recorded and published on the Learning Portal, allowing you to save them for reuse anytime, anywhere, on any mobile device. This makes it possible not to miss classes and keep up with classes and keep up with the passage of new material.

Game Based Learning (learning using a virtual game environment)

Real-life training uses the principles of game organization, which allows future professionals to rehearse and hone their skills in a virtual emergency. Learning as a game provides an opportunity to establish a connection between the learning activity and real life.

The technology provides the following learning opportunities:

  • Focused on the needs of the user
  • Instant feedback
  • Independent decision making and choice of actions
  • Better assimilation and memorization of the material
  • Adaptive pace of learning tailored to the individual needs of the student
  • Better transfer of skills learned in a learning situation to real conditions

Basic principles of training:

  • A gradual increase in the level of difficulty in the game;
  • Using a simplified version of a problem situation;
  • Action in a variable gaming environment;
  • The right choice is made through experimentation.

The main advantages of Game Based Learning technology:

  • Low degree of physical risk and liability
  • Motivation to learn while receiving positive emotions from the process;
  • Practice - mirroring the real situation
  • Timely feedback
  • Choice of different playing roles
  • Learning in collaboration
  • Developing your own behavior strategy
Laboratory workshops using remote access technologies

Conducting practical classes online using remote access technologies for presentations, multimedia solutions and virtual reality:

  • Laboratory workshops that simulate the operation of expensive bench equipment in real production
  • Virtual experiment, which is visually indistinguishable from a remote real experiment performed
  • Virtual instruments, which are an exact copy of real instruments
  • Mathematical modeling to clarify the physical characteristics, chemical content of the investigated object or phenomenon.
ITSP007 – Fundamentals of Machine Learning and Data Science for the Oil and Gas Industry
Language: English, Russian
Level: Foundation
mail@tecedu.org
+7 747 898 5041
+7 7182 901 933