SOUPTIK MUKHERJEE Data Science, Machine Learning and AI
No reviews yet

Data Scientist | Physicist | Teacher | Startup Founder

Hi,

If you are looking for Data Science help using Python, I have some solutions for you. But before that let me introduce myself. I am a Data Scientist, a Physicist and start-up founder. I spent 17 years in the United States, having done two masters, PhD and worked in fortune 500 companies. Right now I am heading a startup Learn Code Quiz in India. We execute client projects and also teach students Python, Data Science in collaboration with our parent company American Software Consulting Group.

I will be happy to assist you based on my experiences gathered over the years.

These are the modules I teach

Module 1: Beginning Your Data Science Journey

Setting Up Your Python Environment
Installing Jupyter Notebook
The Importance of Learning Data Science
Understanding the Field of Data Science
Essential Tools for Data Science
Module 2: Harnessing Python and Jupyter's Power

Working with Python Data Types and Operators
Getting Familiar with Jupyter Notebook
Exploring Basic Data Types
Understanding Comparison and Logical Operators
Module 3: Exploring Lists, Tuples, and Dictionaries

Lists and How to Use Indexing
Advanced Indexing Techniques
Modifying Data Within Lists
Introduction to Tuples
Getting to Know Python Dictionaries
Module 4: Crafting Python Functions

Writing Functions in Python
Working with Function Arguments
Understanding Methods
Creating User-Defined Functions
Exploring Nested and Lambda Functions
Module 5: Working with Loops and Conditionals

Using Conditional Statements (If Statements)
Implementing While Loops
Getting Comfortable with For Loops
Looping Through Dictionary Items
Module 6: Navigating Data with NumPy and Pandas

Introduction to 2D NumPy Arrays
Iterating Over NumPy Arrays
Creating DataFrames with Pandas
Slicing and Filtering DataFrames with Pandas
Utilizing NumPy and Pandas for Statistical Analysis
Module 7: Manipulating Data with Pandas

Importing and Exporting Data
Understanding Pandas Objects: Series and DataFrames
Common Functionality with Pandas Objects
Selecting and Modifying Data with Pandas
Combining and Reshaping DataFrames
Module 8: Data Visualization with Matplotlib

Introduction to Data Visualization
Exploring Matplotlib for Plotting
Creating Line Plots
Generating Bar Plots
Crafting Scatter Plots
Understanding Histograms
Customizing Graphs
Exploring Line of Best Fit
Delving into Box Plots
Analyzing Data with Pair Plots
Visualizing Time Series Data
Introduction to 3D Plotting
Exporting Figures for Sharing
Module 9: Exploring Statistics and Probability

Quiz on Statistics, Probability, and Linear Algebra
Understanding Probability and Statistics
Differentiating Probability vs. Statistics in Python
Sampling Techniques in Python
Exploring Random Variables and Probability Distributions
Analyzing Probability Mass and Density Functions
Module 10: Statistical Distributions and Hypothesis Testing

Overview of Statistical Distributions
Exploring the Uniform Distribution
Understanding Bernoulli and Binomial Distributions
Unveiling the Normal Distribution
Investigating Exponential, Poisson, and T Distributions
Confidence Intervals and Hypothesis Testing
The Data Cleaning Process and Strategies
Handling Missing or Duplicate Data
Concluding Data Cleaning Tasks
Module 11: Introduction to Exploratory Data Analysis

Getting Started with Exploratory Data Analysis (EDA)
Analyzing Descriptive Statistics, Frequencies, and Averages
Understanding Correlation in Data
Visualizing Data in EDA
Data Preprocessing for Analysis
Summary of Exploratory Data Analysis
Module 12: Introduction to Linear Algebra

Foundations of Linear Algebra
Matrices and Vectors
Operations with Matrices
Dot Product and Cross Product
Matrix Multiplication and Division
Transposing Matrices
Determinants, Inverses, and More
Linear Independence and Eigenvalues
Singular Value Decomposition (SVD)
Principal Component Analysis (PCA)
Maximum Likelihood Estimation (MLE)
Module 13: Supervised and Unsupervised Machine Learning

Overview of Supervised Machine Learning
Introduction to Unsupervised Machine Learning
The Basics of Data Modeling
Multivariate Data Analysis with Gaussian Distributions
Understanding Probabilistic Models
Linear Regression: A Foundational Technique
Practical Example of Linear Regression
Insight into Least Squares
Expanding the Scope of Linear Regression
Module 14: Understanding Regression Techniques

Geometry Behind Least Squares Regression
Essential Concepts of Linear Regression
Probabilistic Perspective on Linear Regression
An Exploration of Probability in Regression
Introducing Ridge Regression
Unveiling the Role of Regularization
The Balance Between Bias and Variance
Cross-Validation: A Model Evaluation Technique
Bayesian Inference: A Probabilistic Approach
Applying Bayesian Concepts to a Coin Toss Example
Module 15: Exploring Bayesian Methods

The Essence of Bayesian Methods
Instructions for Applying Bayesian Principles
Bayesian Linear Regression
Applications of Posterior Distribution
The Concept of Active Learning
Analytical Tools for Bayesian Analysis
Using Lagrange Multipliers for Optimization
Sparse Regression Techniques
Insights into Lp Regression
Module 16: Introduction to Classification Techniques

Understanding Classification
Optical Character Recognition with NN Classifier
Exploring the K-Nearest Neighbors Classifier
Statistical Foundations of Classification
Unveiling Optimal Classification Strategies
Embracing the Bayes Classifier
Gaussian Class Conditional Densities
Multivariate Gaussian Classification
Plug-In Classifiers: The Practical Approach
Linear Classification and Hyperplanes
Generalizing Classification to Polynomial Forms
Least Squares in Classification Tasks
Module 17: Delving into Logistic Regression

An In-Depth Look at Logistic Regression
The Likelihood in Logistic Regression
Unraveling the Logistic Regression Algorithm
Laplace Approximation: A Probabilistic Technique
Kernel Methods and the World of Gaussian Processes
Expanding Features with Kernel Techniques
A Comprehensive Study of Kernels
Kernelized Perceptron: A Deep Dive
Regression with Kernel Functions
The Magical World of Gaussian Processes
Module 18: The Art of Support Vector Machines

Maximum Margin Classifiers: The Foundation
Support Vector Machines: A Crucial Tool
Primal and Dual Problems in SVM
Soft-Margin SVM: Balancing Act
An Introduction to Decision Trees
Basics of Decision Tree Learning Algorithm
The Power of Bootstrapping
Bagging and Random Forest: A Closer Look
Two Exciting Projects Await
Module 19: Boosting and the World of Clustering

Boosting: A Technique to Boost Decision Stumps
Application Spotlight: Face Detection
A Detailed Analysis of Boosting
Unsupervised Learning: The Exploration Begins
Clustering: Grouping Similar Data Points
Understanding the Convergence of K-Means
Real-World Applications of K-Means Clustering
Module 20: Expectation-Maximization and Beyond

Maximum Likelihood: Laying the Groundwork
The Expectation-Maximization (EM) Algorithm
Navigating the EM Algorithm
EM for Handling Missing Data
Soft Clustering vs. Hard Clustering
Unveiling Gaussian Mixture Models
A Closer Look at the M-Step
An Example Run with Gaussian Mixtures
EM for Generic Mixture Models
Two Intriguing Projects Await
Module 21: Collaborative Filtering and Topic Modeling

Collaborative Filtering: A Puzzle to Solve
Matrix Factorization: The Key to Model Inference
Probabilistic Matrix Factorization
A Dive into Topic Modeling
Latent Dirichlet Allocation: Unveiling the Technique
Nonnegative Matrix Factorization: Exploring the Concept
Dual Objective Functions in NMF and Topic Modeling
Module 22: Principal Component Analysis (PCA)

Principal Component Analysis: A Dimension Reduction Technique
The Fundamentals of PCA
The Probabilistic Aspect of PCA
The Intricacies of Kernel PCA
Personalization through Dimension Reduction
Crafting Recommender Systems for Travelers
Module 23: Hidden Insights with Markov Models

Exploring Markov Models
Sequences and Their Significance
The Dynamics of Markov Chains
The First Order Markov Chain
Delving into State and Stationary Distributions
A Glimpse into Ranking Algorithms
Classification: A Continuing Journey
Module 24: Unveiling Hidden Patterns with HMMs and Kalman Filter

Understanding Hidden Markov Models
Learning the Art of HMM
Kalman Filtering: An Algorithmic Marvel
The Revisiting of Markov Models
Mastering Kalman Filtering Techniques
Module 25: Discovering Patterns with Association Analysis

The World of Association Analysis and Rules
Basket Processing: A Crucial Step
Dependencies in Frequency
Unearthing Association Rules
Selecting Models and Parameters
BIC: An Essential Derivative
Automated Tracking of Basketball Statistics
Module 26: Deep Learning and Text Analysis

The Deep Learning Abyss: A Journey Commences
The Intricate World of Deep Neural Networks
Activation Functions: The Heart of Deep Learning
Loss Functions: Understanding Model Errors
Gradients and Optimization Techniques
The Mathematical Formulations of Deep Neural Networks
A Glimpse into Convolutional Neural Networks
Handling Pixels, Edges, and Sharpening Images
Module 27: Language Secrets: Ciphers, Models, and Analysis

The Enigma of Ciphers
Language Models: Decoding Text Patterns
Sentiment Analysis: Extracting Emotions from Text
The Power of Trigrams
Building an Article Spinner in Python

Thanks and Regards,
Dr. Souptik Mukherjee

Subjects

  • Data Science and Machine Learning Beginner-Expert


Experience

  • Post Doctoral Fellow (Sep, 2020May, 2023) at University of Central Florida
    Projects involved Data Science and Optics Technology
  • Post Doctoral Fellow (Aug, 2019Aug, 2020) at Post Doctoral Scientist at Hong Kong University of Science at Technology
    Scientist. Projects involved Data Science and Optics
  • Scientist (Jul, 2017Mar, 2019) at Eastman
    Data Scientist and Optical Engineer using Liquid Crystal Technology at Eastman Chemicals, a Fortune 500 Company.
  • Data Scientist (Aug, 2016Aug, 2017) at Arroghia LLP
    Data Scientist . Job involved working with Supervised and Unsupervised learning as well as Deep Learning.
  • PhD Scholar (Nov, 2010Aug, 2016) at Kent State University, Kent, OH, USA
    Worked in Alpha Micron during this time as an Data scientist and Physicist
  • MS Physics (Aug, 2005Sep, 2008) at University of Texas at Arlington, Arlington, Texas
    Completed

Education

  • Chemical Physics (Nov, 2009Aug, 2016) from Kent State University, Kent, OH, USA

Fee details

    2501,000/hour (US$2.9711.89/hour)

    Negotiable


Courses offered

  • Data Science

    • 15000
    • Duration: 4 months
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Bengali
    • Certificate provided: Yes
    These are the modules I teach

    Module 1: Beginning Your Data Science Journey

    Setting Up Your Python Environment
    Installing Jupyter Notebook
    The Importance of Learning Data Science
    Understanding the Field of Data Science
    Essential Tools for Data Science
    Module 2: Harnessing Python and Jupyter's Power

    Working with Python Data Types and Operators
    Getting Familiar with Jupyter Notebook
    Exploring Basic Data Types
    Understanding Comparison and Logical Operators
    Module 3: Exploring Lists, Tuples, and Dictionaries

    Lists and How to Use Indexing
    Advanced Indexing Techniques
    Modifying Data Within Lists
    Introduction to Tuples
    Getting to Know Python Dictionaries
    Module 4: Crafting Python Functions

    Writing Functions in Python
    Working with Function Arguments
    Understanding Methods
    Creating User-Defined Functions
    Exploring Nested and Lambda Functions
    Module 5: Working with Loops and Conditionals

    Using Conditional Statements (If Statements)
    Implementing While Loops
    Getting Comfortable with For Loops
    Looping Through Dictionary Items
    Module 6: Navigating Data with NumPy and Pandas

    Introduction to 2D NumPy Arrays
    Iterating Over NumPy Arrays
    Creating DataFrames with Pandas
    Slicing and Filtering DataFrames with Pandas
    Utilizing NumPy and Pandas for Statistical Analysis
    Module 7: Manipulating Data with Pandas

    Importing and Exporting Data
    Understanding Pandas Objects: Series and DataFrames
    Common Functionality with Pandas Objects
    Selecting and Modifying Data with Pandas
    Combining and Reshaping DataFrames
    Module 8: Data Visualization with Matplotlib

    Introduction to Data Visualization
    Exploring Matplotlib for Plotting
    Creating Line Plots
    Generating Bar Plots
    Crafting Scatter Plots
    Understanding Histograms
    Customizing Graphs
    Exploring Line of Best Fit
    Delving into Box Plots
    Analyzing Data with Pair Plots
    Visualizing Time Series Data
    Introduction to 3D Plotting
    Exporting Figures for Sharing
    Module 9: Exploring Statistics and Probability

    Quiz on Statistics, Probability, and Linear Algebra
    Understanding Probability and Statistics
    Differentiating Probability vs. Statistics in Python
    Sampling Techniques in Python
    Exploring Random Variables and Probability Distributions
    Analyzing Probability Mass and Density Functions
    Module 10: Statistical Distributions and Hypothesis Testing

    Overview of Statistical Distributions
    Exploring the Uniform Distribution
    Understanding Bernoulli and Binomial Distributions
    Unveiling the Normal Distribution
    Investigating Exponential, Poisson, and T Distributions
    Confidence Intervals and Hypothesis Testing
    The Data Cleaning Process and Strategies
    Handling Missing or Duplicate Data
    Concluding Data Cleaning Tasks
    Module 11: Introduction to Exploratory Data Analysis

    Getting Started with Exploratory Data Analysis (EDA)
    Analyzing Descriptive Statistics, Frequencies, and Averages
    Understanding Correlation in Data
    Visualizing Data in EDA
    Data Preprocessing for Analysis
    Summary of Exploratory Data Analysis
    Module 12: Introduction to Linear Algebra

    Foundations of Linear Algebra
    Matrices and Vectors
    Operations with Matrices
    Dot Product and Cross Product
    Matrix Multiplication and Division
    Transposing Matrices
    Determinants, Inverses, and More
    Linear Independence and Eigenvalues
    Singular Value Decomposition (SVD)
    Principal Component Analysis (PCA)
    Maximum Likelihood Estimation (MLE)
    Module 13: Supervised and Unsupervised Machine Learning

    Overview of Supervised Machine Learning
    Introduction to Unsupervised Machine Learning
    The Basics of Data Modeling
    Multivariate Data Analysis with Gaussian Distributions
    Understanding Probabilistic Models
    Linear Regression: A Foundational Technique
    Practical Example of Linear Regression
    Insight into Least Squares
    Expanding the Scope of Linear Regression
    Module 14: Understanding Regression Techniques

    Geometry Behind Least Squares Regression
    Essential Concepts of Linear Regression
    Probabilistic Perspective on Linear Regression
    An Exploration of Probability in Regression
    Introducing Ridge Regression
    Unveiling the Role of Regularization
    The Balance Between Bias and Variance
    Cross-Validation: A Model Evaluation Technique
    Bayesian Inference: A Probabilistic Approach
    Applying Bayesian Concepts to a Coin Toss Example
    Module 15: Exploring Bayesian Methods

    The Essence of Bayesian Methods
    Instructions for Applying Bayesian Principles
    Bayesian Linear Regression
    Applications of Posterior Distribution
    The Concept of Active Learning
    Analytical Tools for Bayesian Analysis
    Using Lagrange Multipliers for Optimization
    Sparse Regression Techniques
    Insights into Lp Regression
    Module 16: Introduction to Classification Techniques

    Understanding Classification
    Optical Character Recognition with NN Classifier
    Exploring the K-Nearest Neighbors Classifier
    Statistical Foundations of Classification
    Unveiling Optimal Classification Strategies
    Embracing the Bayes Classifier
    Gaussian Class Conditional Densities
    Multivariate Gaussian Classification
    Plug-In Classifiers: The Practical Approach
    Linear Classification and Hyperplanes
    Generalizing Classification to Polynomial Forms
    Least Squares in Classification Tasks
    Module 17: Delving into Logistic Regression

    An In-Depth Look at Logistic Regression
    The Likelihood in Logistic Regression
    Unraveling the Logistic Regression Algorithm
    Laplace Approximation: A Probabilistic Technique
    Kernel Methods and the World of Gaussian Processes
    Expanding Features with Kernel Techniques
    A Comprehensive Study of Kernels
    Kernelized Perceptron: A Deep Dive
    Regression with Kernel Functions
    The Magical World of Gaussian Processes
    Module 18: The Art of Support Vector Machines

    Maximum Margin Classifiers: The Foundation
    Support Vector Machines: A Crucial Tool
    Primal and Dual Problems in SVM
    Soft-Margin SVM: Balancing Act
    An Introduction to Decision Trees
    Basics of Decision Tree Learning Algorithm
    The Power of Bootstrapping
    Bagging and Random Forest: A Closer Look
    Two Exciting Projects Await
    Module 19: Boosting and the World of Clustering

    Boosting: A Technique to Boost Decision Stumps
    Application Spotlight: Face Detection
    A Detailed Analysis of Boosting
    Unsupervised Learning: The Exploration Begins
    Clustering: Grouping Similar Data Points
    Understanding the Convergence of K-Means
    Real-World Applications of K-Means Clustering
    Module 20: Expectation-Maximization and Beyond

    Maximum Likelihood: Laying the Groundwork
    The Expectation-Maximization (EM) Algorithm
    Navigating the EM Algorithm
    EM for Handling Missing Data
    Soft Clustering vs. Hard Clustering
    Unveiling Gaussian Mixture Models
    A Closer Look at the M-Step
    An Example Run with Gaussian Mixtures
    EM for Generic Mixture Models
    Two Intriguing Projects Await
    Module 21: Collaborative Filtering and Topic Modeling

    Collaborative Filtering: A Puzzle to Solve
    Matrix Factorization: The Key to Model Inference
    Probabilistic Matrix Factorization
    A Dive into Topic Modeling
    Latent Dirichlet Allocation: Unveiling the Technique
    Nonnegative Matrix Factorization: Exploring the Concept
    Dual Objective Functions in NMF and Topic Modeling
    Module 22: Principal Component Analysis (PCA)

    Principal Component Analysis: A Dimension Reduction Technique
    The Fundamentals of PCA
    The Probabilistic Aspect of PCA
    The Intricacies of Kernel PCA
    Personalization through Dimension Reduction
    Crafting Recommender Systems for Travelers
    Module 23: Hidden Insights with Markov Models

    Exploring Markov Models
    Sequences and Their Significance
    The Dynamics of Markov Chains
    The First Order Markov Chain
    Delving into State and Stationary Distributions
    A Glimpse into Ranking Algorithms
    Classification: A Continuing Journey
    Module 24: Unveiling Hidden Patterns with HMMs and Kalman Filter

    Understanding Hidden Markov Models
    Learning the Art of HMM
    Kalman Filtering: An Algorithmic Marvel
    The Revisiting of Markov Models
    Mastering Kalman Filtering Techniques
    Module 25: Discovering Patterns with Association Analysis

    The World of Association Analysis and Rules
    Basket Processing: A Crucial Step
    Dependencies in Frequency
    Unearthing Association Rules
    Selecting Models and Parameters
    BIC: An Essential Derivative
    Automated Tracking of Basketball Statistics
    Module 26: Deep Learning and Text Analysis

    The Deep Learning Abyss: A Journey Commences
    The Intricate World of Deep Neural Networks
    Activation Functions: The Heart of Deep Learning
    Loss Functions: Understanding Model Errors
    Gradients and Optimization Techniques
    The Mathematical Formulations of Deep Neural Networks
    A Glimpse into Convolutional Neural Networks
    Handling Pixels, Edges, and Sharpening Images
    Module 27: Language Secrets: Ciphers, Models, and Analysis

    The Enigma of Ciphers
    Language Models: Decoding Text Patterns
    Sentiment Analysis: Extracting Emotions from Text
    The Power of Trigrams
    Building an Article Spinner in Python

    Thanks and Regards,
    Dr. Souptik Mukherjee

Reviews

No reviews yet. Be the first one to review this tutor.