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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

From Petroleum Engineering to Data Science: Jaiyesh Chahar’s Journey

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Analytics Vidhya Last Updated : 07 May, 2023
4 min read

Introduction

Jaiyesh Chahar, a Petroleum Engineer turned Data Scientist, shares his educational journey, the inspiration behind his switch to data science, and his experiences in the field. With a strong background in petroleum engineering and a passion for mathematics, Jaiyesh found his calling in data science. In this article, we delve into his journey, the challenges he faced, and his advice for those interested in pursuing a career in data science.

Let’s get into this thrilling and compelling conversation with Jaiyesh.

Interview Excerpts with Jaiyesh Chahar

AV: Please introduce yourself and share your educational journey with us.

Jaiyesh: Hi, I am Jaiyesh Chahar, a Petroleum Engineer turned Data Scientist. I have done my bachelor’s in petroleum engineering from the University of Petroleum and Energy Studies, Dehradun. After that, I pursued my master’s from IIT ISM Dhanbad, and there I decided to take Machine Learning as my minor. From there, my journey of becoming a Data Scientist started. After that, I worked as a Petroleum Data Scientist for an Oil and Gas Startup. And later, I joined Siemens as a Data Scientist.

AV: What made you decide to become a Data Scientist?

Jaiyesh: I always loved mathematics, and if we see data science, machine learning, or even complex deep learning networks is nothing but the application of algebra, differential calculus, and statistics. So, mathematics attracted me towards data science. Also, as I was a petroleum engineer in Oil and gas industry, there was an immense increase in data. So, I decided to take advantage of this vast amount of data to solve problems in the Oil and Gas Industry.

AV: You have a specialization in Petroleum; what made you switch? What steps did you take to gain the necessary skills and knowledge to succeed in the field of Data Science?

Jaiyesh: I started by using Data Science as a tool for solving problems in Petroleum Industry. So, I was a Petroleum Engineer who knew Data Science. And regarding the steps, the initial step was learning how to code using python. Started with python, followed by useful libraries of python like Numpy, pandas, and Matplotlib. Then Statistics, machine learning, and at last deep learning. Also, at each step, a lot of practice is required.

data science | data scientist | petroleum engineering

AV: What is the biggest challenge you have faced in your career as a Data Scientist? How did you overcome it?

Jaiyesh: My biggest challenge was to get a job as a fresher without any experience, but here my petroleum background helped me because there are very few people who have knowledge of oil and gas as well as data science. So that mixture helped me to get my first job.

AV: You are one of the co-founders of “Petroleum From Scratch.” What was the inspiration behind it?

Jaiyesh: We started Petroleum from Scratch during covid times 2020. A lot of organizations started during that time, and they were charging hefty amounts for providing training to petroleum students/engineers. And also oil and gas market was at its lowest point, as crude prices went below zero. So a lot of layoffs happened in the oil and gas industry. So, to help professionals and students, we came up with Petroleum From Scratch, where we share knowledge free of cost.

AV: After working at Siemens for over a year, can you describe a recent project you have worked on, and what were some key insights or takeaways?

Jaiyesh: So, one of my recent projects is for a giant automotive company, where we built a complete pipeline for the detection of faulty parts in the manufacturing unit. In this project, not only the Data Science part but the software piece was also delivered by us. So, this project showed me the importance of knowing software pipeline development, even being a data scientist.

Tips for Data Scientist Enthusiasts

AV: What are habits that you swear by which have led you to be successful?

Jaiyesh: Consistency and showing up daily are key habits that can help in achieving success in any area of life. When we consistently show up and put in the effort, we are more likely to make progress and see results over time.

This is especially true when it comes to learning new skills or developing new habits. By consistently practicing or working on something every day, we can build momentum and make steady progress towards our goals. This can help us stay motivated and avoid getting discouraged or giving up too soon.

In addition to consistency, other habits that can contribute to success include setting clear goals, prioritizing tasks, staying organized, and maintaining a positive attitude. By combining these habits with consistency and daily effort, we can create a powerful formula for achieving success in any area of life.

AV: What advice would you give someone interested in pursuing a career in data science, and what skills or qualities do you believe are most important for success in this field?

Jaiyesh: Always start with basics, and have a strong grasp of these basic things. Because many people in the industry don’t know what exactly is going under the hood of complex algorithms, and the answer to that is basic mathematics. So, it’s always good to have a strong understanding of basic concepts. Mathematics, Statistics, and Python Programming are the skills that will help a lot. If you want to become a Data Scientist. Then, click here for more details.

Conclusion

Jaiyesh Chahar’s journey from petroleum engineering to data science is an inspiration for those looking to make a career switch. With a strong foundation in mathematics and a willingness to learn, Jaiyesh has successfully navigated the challenges of the data science field. His co-founding of “Petroleum From Scratch” showcases his commitment to sharing knowledge and helping others. His advice for aspiring data scientists to have a strong grasp of basic concepts and skills in mathematics, statistics, and programming is invaluable. Jaiyesh’s journey proves that with passion and perseverance, one can achieve success in any field.

careerdata science interviewdata scientist success storiesEngineeringmathematicspythonstatistics
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