Course10310578260 lectures
Introduction to Python and Petroleum Data Analytics
Prof. Archana · IIT-ISM Dhanbad
About
Petroleum Data Analytics (PDA) is rapidly transforming the oil and gas industry through the integration of Artificial Intelligence (AI) and Machine Learning (ML). As we look ahead, it's evident that mastering these technologies will be pivotal for shaping the future of engineering disciplines, particularly in petroleum engineering.
This 12- week course aims to equip the next generation of petroleum professionals with essential foundations in PDA. While it's acknowledged that a single course cannot cover all aspects of becoming a PDA expert, it serves as a crucial starting point. Participants will gain a realistic understanding of AI and ML fundamentals as they apply to solving engineering challenges in the petroleum sector.
For engineering-domain experts, transitioning into skilled AI and ML practitioners is becoming increasingly important. The ability to harness data-driven insights through these technologies will not only optimize existing processes but also drive innovation in exploration, production, and operational efficiency within the industry.
Ultimately, this course serves as a catalyst for enthusiasts and professionals alike to grasp the transformative potential of PDA. It's a step towards unlocking the future where data-driven strategies and advanced analytics play a central role in shaping the trajectory of the oil and gas industry.
Intended audience: Undergraduate, post graduate and PhD students’ professional practitioner in the discipline of Petroleum Engineering, Petroleum Refinery Engineering, Chemical Engineering
Prerequisites: Bachelor’s degree in any Engineering discipline
Lectures
Week 1 | Significance of Python and Petroleum Data Analytics
- Lecture 1 | Overview
- Lecture 2 | Significance of Python and Petroleum Data Analytics
- Lecture 3 | Significance of Python and Petroleum Data Analytics
- Lecture 4 | Significance of Python and Petroleum Data Analytics
- Lecture 5 | Significance of Python and Petroleum Data Analytics
Week 2 | Programming Fundamentals
- Lecture 6 | Programming Fundamentals - 1
- Lecture 7 | Programming Fundamentals - 2
- Lecture 8 | Programming Fundamentals - 3
- Lecture 9 | Programming Fundamentals - 4
- Lecture 10 | Programming Fundamentals - 5
Week 3 | Implementation of Python Libraries
- Lecture 11 | Implementation of Python Libraries - 1
- Lecture 12 | Implementation of Python Libraries - 2
- Lecture 13 | Implementation of Python Libraries - 3
- Lecture 14 | Implementation of Python Libraries - 4
- Lecture 15 | Implementation of Python Libraries - 5
Week 4 | Implementation of NUMPY & Matplotlib
- Lecture 16 | Implementation of Python Libraries - NUMPY (I)
- Lecture 17 | Implementation of Python Libraries - NUMPY (II)
- Lecture 18 | Implementation of Python Libraries - NUMPY (III)
- Lecture 19 | Implementation of Python Libraries - Matplotlib (I)
- Lecture 20 | Implementation of Python Libraries - Matplotlib (II)
Week 5 | Data Wrangling and Pre-processing I
- Lecture 21 | Data Wrangling and Preprocessing (I)
- Lecture 22 | Data Wrangling and Preprocessing (II)
- Lecture 23 | Data Wrangling and Preprocessing (III)
- Lecture 24 | Data Wrangling and Preprocessing (IV)
- Lecture 25 | Data Wrangling and Preprocessing (V)
Week 6 | Data Wrangling and Pre-processing II
- Lecture 26 | Data Wrangling and Preprocessing (VI)
- Lecture 27 | Data Wrangling and Preprocessing (VII)
- Lecture 28 | Data Wrangling and Preprocessing (VIII)
- Lecture 29 | Data Wrangling and Preprocessing (IX)
- Lecture 30 | Data Wrangling and Preprocessing (X)
Week 7 | Data Manipulation
- Lecture 31 | Data Manipulation - I
- Lecture 32 | Data Manipulation - II
- Lecture 33 | Data Manipulation - III
- Lecture 34 | Data Manipulation - IV
- Lecture 35 | Data Manipulation - V
Week 8 | Handling Missing Values | Machine Learning
- Lecture 36 | Handling Missing Values-CCA
- Lecture 37 | Handling Missing Values-Univariate Analysis
- Lecture 38 | Handling Missing Values-Multivariate Analysis
- Lecture 39 | Machine Learning- Supervised - I
- Lecture 40 | Machine Learning- Supervised - II
Week 9 | Supervised Machine Learning
- Lecture 41 | Supervised Machine Learning - I
- Lecture 42 | Supervised Machine Learning - II
- Lecture 43 | Supervised Machine Learning - III
- Lecture 44 | Supervised Machine Learning - IV
- Lecture 45 | Supervised Machine Learning - V
Week 10 | Supervised Machine Learning
- Lecture 46 | Supervised Machine Learning - VI
- Lecture 47 | Supervised Machine Learning - VII
- Lecture 48 | Supervised Machine Learning - VIII
- Lecture 49 | Supervised Machine Learning - IX
- Lecture 50 | Supervised Machine Learning - X
Week 11 | Supervised Machine Learning Algorithm
- Lecture 51 | Supervised Machine Learning Algorithm - I
- Lecture 52 | Supervised Machine Learning Algorithm - II
- Lecture 53 | Supervised Machine Learning Algorithm - III
- Lecture 54 | Supervised Machine Learning Algorithm - IV
- Lecture 55 | Supervised Machine Learning Algorithm - V
Week 12 | Supervised & Unsupervised Machine Learning
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT-ISM Dhanbad
- Department
- Chemical Engineering
- Teacher
- Prof. Archana
- Level
- Undergraduate
- Language
- English
- Subjects
- Chemistry, Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Kharagpur.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed