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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

In Conversation with Dr. Sagar Dhanraj Pande, Assistant Professor of Data Science

Sakshi Raheja
Sakshi Raheja Last Updated : 14 Jun, 2023
5 min read

Introduction

In today’s rapidly evolving digital age, the integration of data science has revolutionized numerous industries, including education. As educators strive to provide personalized learning experiences and enhance student outcomes, the role of data science in the teaching domain has become increasingly critical. The ability to collect, analyze, and derive insights from vast amounts of educational data has opened up new avenues for understanding student behavior, optimizing instructional strategies, and ultimately transforming the way we educate and make an initiative for students to take data science as a career.

Today’s guest of honour is Dr. Sagar Dhanraj Pande. He has published and presented more than 60 papers in Springer, Elsevier, CRC, Taylor & Francis, and other reputable journals which are Scopus indexed & peer-review journals. Also, he has published papers at international conferences of Springer on the topics of Data Mining, Network Security, IoT, and its Application. He has supervised several postgraduate students in cybersecurity, computer networks, communication, and IoT. He is responsible for teaching Artificial Intelligence, Deep Learning, Machine Learning, Cyber Crime and Security, and Python Programming courses to undergraduate and postgraduate students.

Assistant Professor of Data Science | Sagar Dhanraj Pande

Professional Journey of Dr. Sagar Dhanraj Pande

AV: Can you tell us about your educational background and how it led you to your current position as Assistant Professor Senior Grade I at VIT-AP?

Dr. Sagar: Hi, I am Dr. Sagar Dhanraj Pande, currently working as Assistant Professor Senior Grade I at VIT-AP. I have done my bachelor’s and master’s in computer science and engineering from P. R. Patil College of Engineering, Amravati, Maharashtra, India. After that, I pursued a PhD from Lovely Professional University, Punjab, India, and there I decided to take Deep Learning and Network Security as my research areas. From there, my journey of becoming a Data Science started. After that, I worked as an Assistant Professor at Lovely Professional University.

AV: What inspired you to pursue a career in the field of Data Science? How did you get started in this field?

Dr. Sagar:I have always been a math nerd; when it comes down to it, data science, machine learning, and even sophisticated deep learning networks are just applications of it. Thus, my interest in data science was sparked by mathematics. Also, I opted for deep learning and network security as my Ph.D. area, I became more interested in this field.

AV: Can you describe a particularly challenging project you worked on in your career? How did you overcame the obstacles you faced?

Dr. Sagar:The project I did during my Ph.D. itself was very challenging as a lot of existing frameworks were available on the dataset which I had used for my project work. To overcome this, I proposed my own customized framework to perform feature selection on the dataset, and to cross-verify the efficiency of the proposed framework I applied it to another dataset where it performed very well.

AV: How do you balance your teaching responsibilities with your research and industry engagements. What strategies have you found to be most effective for managing your time?

Dr. Sagar: Well, I always try to explore new subjects which are more relevant to my research areas. I always love to read more recent innovations and happenings in the field of data science. I used to utilize lecture breaks to do my research activities. Whatever collaborative project work I do with my student my primary focus is that it should be converted into either patent, copyright, or research article. Due to this, I don’t have to take out separate time for doing the research.

AV: Can you talk about a time when you faced a failure or setback in your career, and how you bounced back from it?

Dr. Sagar: Mathematics is one of my favourite subjects. But during my bachelor’s, I got very fewer marks in mathematics due to which I didn’t get good grades that semester. I felt very depressed during that time. But my parents, teachers, and friends had shown faith in me. They motivated me from time to time and somehow, I ended my bachelor’s as well as master’s by getting University Rankings.

 Tips On Data Science As A Career

AV: What are some of the most important skills or qualities that you think are essential for success in the field of Data Science?

Dr. Sagar: In the field of Data Science, one should have knowledge of mathematics, logic, statistics, probability, python, etc. Most importantly one should have problem-solving skills. Every time you will be provided with the raw data and you can analyze the data and provide more detailed insights of it will provide fruitful outcomes. I would like to recommend a few courses like Data Analytics with Python by NPTEL, Essentials of Data Science with R Software _ 1, Data Science for Engineers by NPTEL etc. Some of the courses from Analytics Vidhya are quite useful, Introduction to Python, NLP and so on. 

AV: Can you share a few tips for students and young professionals who are interested in pursuing a career in Data Sciences?

Dr. Sagar: I would like to suggest to all the students that they should concentrate on not only their programming skills but also on their analytical skills. Apart from this, they should learn programming languages like Python and R.  They should also practice mathematical and algorithms concepts. I would like to recommend some books like “Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python” by Peter Bruce, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron etc.

Latest Developments in Data Science

AV: How do you stay up-to-date with the latest developments in Data Science? What resources do you rely on for this?

Dr. Sagar: Mostly, I prefer to read relevant and recent research articles. Apart from this, I prefer to attend International Conferences and Workshop which gives me vision and ideas regarding the recent happening in this domain. I would like to recommend few insightful websites like ResearchGate, Towards Data Science etc. 

AV: Can you tell us about any specific initiatives or programs you have implemented or led at VIT-AP to promote and advance in the field? What impact have these initiatives had on the university and broader data community?

Dr. Sagar: Currently, I am teaching machine learning and deep learning subjects at VIT-AP where I usually prefer to give assignments to students where they must create their own dataset and perform data analytics on it. Through this initiative almost all students were able to get acceptance for their research work in reputed international conferences and journals. 

Conclusion

For students aspiring to embark on a career in data science, this success story serves as a beacon of inspiration. The field of data science offers an exciting and rewarding pathway, brimming with opportunities to make a tangible impact on diverse domains, including education.

As a data scientist, you hold immense power in your hands, capable of shaping decisions, policies, and outcomes. Remember, success in data science is not an endpoint but a lifelong journey of discovery and growth. Embrace the challenges, celebrate the victories, and never stop pushing the boundaries of what is possible. As you embark on this exciting career path, armed with passion, knowledge, and a commitment to making a difference. You have the potential to shape the future and become a true catalyst for positive change through the transformative power of data science.

If you want to become a full stack data scientist then enroll to our blackbelt program.

Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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Wow! Your blog was an absolute gem. It was informative, well-written, and truly captivating. Thank you for sharing your expertise and enriching our lives!

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