Spandan Ghoshal Statistics & Data Analytics
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Hello! Welcome to my profile!
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Welcome! I am an experienced and passionate tutor specializing in Statistics and Data Analytics. With a strong background in the field, I find great joy in sharing my knowledge and helping students understand the intricacies of statistics.

Over the past four years, I have successfully tutored students majoring in Statistics and Economics, receiving excellent feedback for my teaching approach. My favorite topics to teach include R programming, Regression models, statistical inference, hypothesis testing, ANOVA, multivariate analysis, classification, pattern recognition, statistical learning, time series, sequential methods, and non-parametric methods.

In addition to my expertise in statistics, I have also completed courses in Real Analysis, Multivariate Calculus, and Linear Algebra. I believe in explaining complex concepts in a simple and lucid yet rigorous manner. Visualizations play a key role in my teaching approach as they enhance understanding and help students grasp difficult concepts more easily. I also strive to connect different topics, providing students with a holistic view of the subject matter.

The feedback I have received from students has been consistently excellent, reinforcing the effectiveness of my teaching methods. I am excited to engage with you and assist you in your learning journey. With my knowledge and dedication, I am committed to helping you succeed and achieve your goals in statistics and data analytics.

Thank you for considering me as your tutor. Let's embark on this educational adventure together!

Subjects

  • R programming Beginner-Expert

  • Data analysis Beginner-Expert

  • Statistics and Probability Beginner-Expert

  • Statistical Analysis Beginner-Expert

  • Probability and Random Process Beginner-Expert

  • Logistic regression Beginner-Expert

  • Statistical inference Beginner-Expert

  • Multivariate Analysis Beginner-Expert

  • R programming language Beginner-Expert

  • Bayesian Statistics Beginner-Expert

  • Statistics A Level

  • K-Means Clustering, ML DL algorithms, Logistic regression Beginner-Expert

  • Statistical learning Beginner-Intermediate

  • Deep learning with Python programming Beginner-Intermediate

  • Machine learning Python Beginner-Intermediate

  • R Markdown Beginner-Intermediate

  • R coding language Beginner-Expert

  • Mathematical Analysis 1 Beginner-Expert

  • Biostatistics and Epidemiology Beginner-Expert

  • Statistical inference and Machine learning Beginner-Expert

Experience

  • Statistics tutor (Aug, 2021–Apr, 2022) at cheenta ganit kendra
    I used to teach students and prepare them for competitive exams for post-graduation entrance exams in statistics like IIT JAM, M.STAT, DU, HU etc. Almost all of my students have experienced significant improvement in their understanding of the subject.

Education

  • PhD in Statistics (Jun, 2023–now) from Indian Statistical Institute Kolkata
  • M.Stat (Sep, 2021–now) from INDIAN STATISTICAL INSTITUTE, Delhi
  • B.Sc (Aug, 2018–Sep, 2021) from presidency university kolkata–scored 90

Fee details

    ₹1,000–3,000/hour (US$11.89–35.68/hour)

    I'm very flexible with the fees structure and we can adjust if you have any financial problem but I just want that the effort I put should get what it deserves.

Courses offered

  • A Brief Introduction to R Programming

    • ₹15000
    • Duration: 6 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Bengali
    • Certificate provided: No
    ## **Stage 1: Beginner**

    1. Introduction to R:
    - Installation and setup
    - RStudio interface overview
    2. Basic Data Types and Variables:
    - Numeric, character, and logical data types
    - Variable assignment and naming conventions
    - Basic arithmetic operations

    1. Data Structures:
    - Vectors: creating, indexing, and modifying
    - Matrices and arrays: creating and manipulating
    - Factors: understanding categorical data
    - Lists: creating and accessing elements
    2. Control Flow:
    - Conditional statements (if-else, switch)
    - Loops (for, while)
    - Functions: creating and calling

    1. Data Import and Export:
    - Reading and writing data from/to CSV, Excel, and other formats
    - Basic data manipulation using packages like **`dplyr`** and **`tidyr`**
    2. Basic Plotting:
    - Creating simple plots using base R graphics
    - Introduction to the **`ggplot2`** package for more advanced visualizations

    ## **Stage 2: Intermediate**

    1. Data Manipulation:
    - Exploring and manipulating data frames using **`dplyr`**
    - Filtering, sorting, summarizing data
    - Joining and merging datasets
    2. Data Visualization:
    - Advanced plotting with **`ggplot2`**
    - Customizing plots (axes, legends, titles)
    - Creating interactive plots using **`plotly`**
    3. Functions and Control Flow:
    - Advanced function concepts (arguments, default values, return values)
    - Error handling and debugging
    - Creating custom control flow functions
    4. Data Cleaning and Preprocessing:
    - Dealing with missing data
    - Handling outliers and data transformations
    - Data normalization and standardization
    5. Statistical Analysis:
    - Introduction to statistical tests (t-tests, chi-square tests, etc.)
    - Regression analysis (linear regression, logistic regression)
    - Exploratory data analysis techniques

    ## **Stage 3: Advanced**

    1. Advanced-Data Structures:
    - Data frames: advanced manipulation and reshaping
    - Tidy data principles and tidy verse packages (**`tidyverse`**)
    - Working with dates and times
    2. Advanced Programming Techniques:
    - Functional programming in R
    - Apply functions (apply, lapply, sapply)
    - Advanced control flow (recursion, switch statements)
    3. Advanced Statistical Analysis:
    - Multivariate analysis techniques (cluster analysis, principal component analysis)
    - Time series analysis
    - Machine learning with R (classification, regression, clustering)
    4. Performance Optimization:
    - Identifying and resolving performance bottlenecks
    - Profiling code using tools like **`profvis`**
    - Optimizing code with vectorization and parallelization
    5. Advanced Topics:
    - Creating R packages
    - Web scraping and API integration
    - Big data processing with R (using **`dplyr`**, **`data.table`**, or SparkR)

  • Introduction To Machine Learning

    • ₹25000
    • Duration: 2 Months
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Bengali
    • Certificate provided: No
    # Course Plan

    **Probability:**

    1. Basic concepts of probability
    2. Complementary, union, and intersection of events
    3. Conditional probability and Bayes' theorem
    4. Random variables and probability distributions (discrete and continuous)
    5. Expected value and variance
    6. Probability distributions (normal distribution, binomial distribution, Poisson distribution)

    **Statistics:**

    1. Descriptive statistics (mean, median, mode, variance, standard deviation)
    2. Sampling techniques and sampling distributions
    3. Central Limit Theorem and its Implications
    4. Confidence intervals and hypothesis testing
    5. Parametric and non-parametric tests
    6. Hypothesis testing and p-values
    7. Bayesian statistics and Bayesian inference
    8. Nonparametric methods (kernel density estimation, rank-based tests)

    **Linear Algebra:**

    1. Vectors, matrices, and matrix operations (addition, subtraction, scalar multiplication)
    2. Matrix multiplication and transpose
    3. Systems of linear equations and matrix inversion
    4. Eigenvalues and eigenvectors
    5. Matrix factorization (LU decomposition, QR decomposition)

    **Calculus:**

    1. Limits and continuity
    2. Differentiation and Rules of Differentiation
    3. Applications of differentiation (optimization, rates of change)
    4. Integration and rules of integration
    5. Applications of integration (area under the curve, probability density functions)
    6. Multivariable calculus (partial derivatives, gradients, optimization in multiple dimensions)

    **Optimization:**

    1. Unconstrained optimization (gradient descent, Newton's method)
    2. Constrained optimization (Lagrange multipliers)
    3. Linear programming and integer programming (basic concepts)
    4. Convex optimization (convex sets, convex functions)

    **Additional Topics:**

    1. Linear regression and least squares estimation
    2. Time series analysis and forecasting
    3. Principal Component Analysis (PCA) and dimensionality reduction
    4. Markov Chains and Hidden Markov Models
    5. Decision theory and utility theory

    **Machine Learning:**

    1. Introduction to Machine Learning
    2. Supervised Learning
    3. Unsupervised Learning
    4. Model Evaluation and Selection
    5. Linear Regression
    6. Logistic Regression
    7. Decision Trees and Random Forests
    8. Support Vector Machines (SVM)
    9. Naive Bayes Classifier
    10. Ensemble Methods (Boosting and Bagging)
    11. Neural Networks and Deep Learning
    12. Dimensionality Reduction Techniques (PCA, t-SNE)
    13. Clustering Algorithms (K-Means, Hierarchical Clustering)
    14. Evaluation Metrics and Cross-Validation
    15. Handling Imbalanced Data
    16. Handling Missing Data
    17. Feature Engineering and Selection
    18. Hyperparameter Tuning
    19. Model Deployment and Productionization
    20. Ethical Considerations in Machine Learning

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