Python Data Science Machine Learning Bootcamp

Python Data Science Class Bootcamp NYC (Affordable Coding Programming)

Sunday: Python Part 1 & Part 2

Monday: Blockchain / Solidity / DApp / Crypto Investing

Tuesday: Pandas Data Analytics & Wrangling  Clearning Time Series

Wed: Hadoop Big Data / SQL (Hive Pig) / Scala

Thursday: VBA Macro (Excel Analytics)

Friday: Machine Learning (Regression Classification)

Saturday: Hadoop Big Data / SQL (MySQL, SQlite)


SQL 9 hours:

Excel VBA 9 hours:

Machine Learning 9 hours:

Python 5 Days Intensive:

Block-chain 9 hours:

See the calendar of events at:

Python for Data Science & Machine learning Bootcamp in Manhattan New York – Tutor, Coach, Consultant or Freelancers

Python Data Science Class Bootcamp NYC  (Affordable Coding Programming for Machine Learning) 1 on 1 Tutoring also available

Cost effective and Reasonable Education for Data Science Enthusiast

1-on-1 Classes available @ $30 per hour


Cell: 929 356 5046

Also attend our free 2 day Bootcamp:

Python 1 Day Course PPT

Python Data science (1)

Three methods of course delivery used: 1) doubt based 2) real project based 3) book based (or combo).
Course is based on three books, which are given below. These are the most famous books and have several exercises and discussions available online.

Reference text books recommended to learn for the course:

  1. Learning python the hard way, pdf
  2. Python for Data Analysis, pdf
  3. Python Crash Course: A Hands-on, Project-based Introduction to Programming, pdf


Below are the three courses that I am providing at Math Matter:

The Course has three UNITS




  1. What is Data Science and Terminology Used by different Groups     Lesson 1
  2. Research Design and Python Pandas (Parallels with Excel)    Lesson 2
  3. Statistics Fundamentals I for Decision making Lesson 3
  4. Statistics Fundamentals II for Decision making  Lesson 4
  5. Introduction to Regression    Lesson 5
  6. Evaluating Model Fit    Lesson 6
  7. Introduction to Classification (How is it different from Regression)    Lesson 7
  8. Introduction to Logistic Regression    Lesson 8
  9. Communicating  Logistic Regression Results to non technical    Lesson 9
  10. Decision Trees and Random Forests    Lesson 10
  11. Natural Language Processing    Lesson 11
  12. Dimensionality Reduction    Lesson 12
  13. Time Series Data I    Lesson 13
  14. Time Series Data II    Lesson 14
  15. Database Technologies    Lesson 15
  16. Big Data Technologies    Lesson 16
  17. Final Project Presentations    Lesson 17




  • Describe course syllabus and setup development environment
  • Answer the questions: “what is Data Science? what roles exist in Data Science?”
  • Define the workflow, tools and approaches data scientists use to analyze data



  • Define a problem and identify appropriate data sets using the data science workflow
  • Walkthrough the data science workflow using a case study in the Pandas library
  • Import, format and clean data using the Pandas Library
  • Draw Parallels with



  • Use NumPy and Pandas libraries to analyze datasets using basic summary statistics: mean, median, mode, max, min, quartile, inter-quartile, range, variance, standard deviation and correlation
  • Create data visualization – scatter plots, scatter matrix, line graph, box blots, and histograms- to discern characteristics and trends in a dataset
  • Identify a normal distribution within a dataset using summary statistics and visualization


  • Explain the difference between causation vs. correlation
  • Test a hypothesis within a sample case study
  • Validate your findings using statistical analysis (p-values, confidence intervals)



Deeper insight into exploratory data analysis





Define data modeling and linear regression

Differentiate between categorical and continuous variables

Build a linear regression model using a dataset that meets the linearity assumption using the scikit-learn library



Define regularization, bias, and errors metrics

Evaluate model fit by using loss functions including mean absolute error, mean squared error, root mean squared error

Select regression methods based on fit and complexity



Define a classification model

Build a K-Nearest Neighbors using the scikit-learn library

Evaluate and tune model by using metrics such as classification accuracy/error



  • Build a Logistic regression classification model using the scikit-learn library
  • Describe the sigmoid function, odds, and odds ratios and how they relate to logistic regression
  • Evaluate a model using metrics such as classification accuracy/error, confusion matrix, ROC / AOC curves, and loss functions



  • Explain the tradeoff between the precision and recall of a model and articulate the cost of false positives vs. false negatives.
  • Identify the components of a concise, convincing report and how they relate to specific audiences/stakeholders
  • Describe the difference between visualization for presentations vs. exploratory data analysis



Focus on a topic selected by the instructor/class in order to provide deeper insight into data modeling




  • Describe the difference between classification and regression trees and how to interpret these models
  • Explain and communicate the tradeoffs of decision trees vs regression models
  • Build decision trees and random forests



  • Demonstrate how to tokenize natural language text
  • Categorize and tag unstructured text data
  • Explain how to build a text classification model using spacy



  • Explain how to perform a dimensional reduction Demonstrate how to refine data using Latent Dirichlet Allocation (LDA)
  • Extract information from a sample text dataset



  • Explain why time series data is different than other data and how to account for it
  • Create rolling means and plot time series data
  • Perform autocorrelation on time series data



  • Decompose time series data into trend and residual components
  • Validate and cross-validate data from different data sets
  • Use the ARIMA model to forecast and detect trends



  • Describe the use cases for different types of databases
  • Explain differences between relational databases and document-based databases
  • Write simple select queries to pull data from a database and use within Pandas



Introduction to Python


  •     Pillars of programming: Python built-in Data types
  •     Concept of mutability and behavior of different Data structures
  •     Control flow statements: If, Elif and Else
  •     Definite and Indefinite loops: For and While loops
  •     Writing user-defined functions in Python
  •     Read and write Text and CSV files with python
  •     List comprehensions and Lambda
  •     How to start using Python
  •     Parsing information with Python
  •     Practice Python to solve the real-world tasks



Python data science Tutor in New York / Mentor $25/hr (Downtown Manhattan)

Do you need a Programming Tutor that gets you things automated?

Teaching method tailored to meet specific student needs/types

Introduction to Python Anaconda Jupyter
Data processing using Panda
Web scraping using beautiful soup
Regression and Monte Carlo intro
Data visualization using matplot
Introduction to SparkR
Learner project based on his work also investment and trading

Skills that you will GAIN while working on the course are:

  • Python Programming Language
  • Statistical Hypothesis Testing
  • IPython
  • Hypothesis-testing
  • NetworkX
  • Matplotlib
  • Numpy
  • Pandas
  • Scipy
  • Python Lambdas
  • Python Regular Expressions

Python Basics

An introduction to the basic concepts of Python. Learn how to use Python both interactively and through a script. Create your first variables and acquaint yourself with Python’s basic data types.

Learn to store, access and manipulate data in lists: the first step towards efficiently working with huge amounts of data.

Functions and Packages

To leverage the code that brilliant Python developers have written, you’ll learn about using functions, methods and packages. This will help you to reduce the amount of code you need to solve challenging problems!


NumPy is a Python package to efficiently do data science. Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.

Course Syllabus

Section 1: Python Basics

Take your first steps in the world of Python. Discover the different data types and create your first variable.

Section 2: Python Lists

Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.

 Section 3: Functions and Packages & Control flow and Pandas

Learn how to get the most out of other people’s efforts by importing Python packages and calling functions.

Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.

Section 4: Numpy and Matplotlib

Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.

Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data.

Python data science Tutor $25/hr

Teaching method tailored to meet specific student needs/types

Introduction to Python Anaconda Jupyter
Data processing using Panda
Web scraping using beautiful soup
Regression and Monte Carlo intro
Data visualization using matplot
Introduction to SparkR
Learner project based on your work (marketing,investment and trading)

Collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:

  • python
  • jupyter anaconda notebooks
  • pandas
  • numpy
  • matplotlib
  • git
  • and many other tools.

We’ll cover the machine learning and data mining techniques real employers are looking for, including:

  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multivariate Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests


Statistics and Probability Refresher, and Python 

  • Bayes’ Theorem
  • Predictive Models
  • Linear Regression
  • Polynomial Regression
  • Multivariate Regression, and Predicting Car Prices
  • Multi-Level Models
  •  Machine Learning with Python
  • Supervised vs. Unsupervised Learning, and Train/Test
  • Using Train/Test to Prevent Overfitting a Polynomial Regression
  • Bayesian Methods: Concepts
  • Implementing a Spam Classifier with Naive Bayes
  • K-Means Clustering
  • Clustering people based on income and age
  • Measuring Entropy
  • Install GraphViz
  • Decision Trees: Concepts
  • Decision Trees: Predicting Hiring Decisions
  • Ensemble Learning
  • Support Vector Machines (SVM) Overview
  • Using SVM to cluster people using scikit-learn
  •  User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Finding Movie Similarities
  • Improving the Results of Movie Similarities
  • Making Movie Recommendations to People
  • Improve the recommender’s results
  •  More Data Mining and Machine Learning Techniques
  • K-Nearest-Neighbors: Concepts
  • Using KNN to predict a rating for a movie
  • Dimensionality Reduction; Principal Component Analysis
  • PCA Example with the Iris data set
  • Data Warehousing Overview: ETL and ELT
  • Reinforcement Learning
  •  Dealing with Real-World Data
  • Bias/Variance Tradeoff
  • K-Fold Cross-Validation to avoid overfitting
  • Data Cleaning and Normalization
  • Cleaning web log data
  • Normalizing numerical data
  • Detecting outliers
  • –Apache Spark: Machine Learning on Big Data
  • Installing Spark – Part
  • Spark Introduction
  • Spark and the Resilient Distributed Dataset (RDD)
  • Introducing MLLib
  • Decision Trees in Spark
  • K-Means Clustering in Spark
  • TF / IDF
  • Searching Wikipedia with Spark
  • Using the Spark 2.0 DataFrame API for MLLib
  • Experimental Design
  • A/B Testing Concepts
  • T-Tests and P-Values
  • Hands-on With T-Tests
  • Determining How Long to Run an Experiment
  • A/B Test Gotchas


Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!

Module 1 – Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning
Module 2 – Supervised Learning I

K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees
Module 3 – Supervised Learning II
Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting & Underfitting
Understanding Different Evaluation Models
Module 4 – Unsupervised Learning
K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters – Single Linkage Clustering
Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
Density-Based Clustering
Module 5 – Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection
Collaborative Filtering & Its Challenges

Advanced Data Science with Python: Machine Learning


Knowledge of Python programming and basic features of Python

Able to munge, analyze, and visualize data in Python with Pandas and charting


Unit 1: Introduction and Regression

How to dive into Machine Learning

Simple Linear Regression and Multiple Linear Regression

Forward and Backward Selection

Numpy/Scikit-Learn Lab

Class 2:

Part Classification I

Logistic Regression – Application in Default and other variables

Discriminant Analysis

Naive Bayes

Supervised Learning Lab

Resampling and Model Selection


Bootstrap – Breaking it down into simple

Feature Selection

Model Selection and Regularization lab

Class 3:

Classification II

Support Vector Machines SVM

Decision Trees – and Branch Analysis

Bagging and Random Forests

Decision Tree in Python and SVM Lab

Class 4:

Unsupervised Learning – Breaking it down

Principal Component Analysis

Kmeans and Hierarchical Clustering

PCA and Clustering Lab

Other areas of working:

Learn AWS Amazon Web Services to run analytics on Ubuntu using Python Anaconda on the cloud.
Learn basics of AWS setup and Ubuntu Commands, with Python setup
Experienced tutor available in Manhattan.
Intro for AWS certification.
Learn Scala for basic implementation on Ubuntu AWS – makes you ready for the job market!

Basics of Javascript and NodeJS

Big data tools:
Kafka, Elastic Map Reduce, Avro, Parque, Storm, Hbase
– MongoosDB
– Solr/Lucene

Cassandra, Spark
Data mining / machine learning software packages (e.g., Spark ML, scikit-learn, H2O, Weka, Keras)
Version control systems (git) and comfortable using command-line tools

Semantic web technology (e.g., RDF, OWL, SPARQL)
Knowledge of search technologies (e.g., Solr, ElasticSearch)
GitHub, Kaggle, KDD contributions earn major props

Course prepares for jobs:

Backend Developer with Web development and http, tcp, web-sockets
Modern NoSQL datastores like MongoDB, Redis
Web/mobile application development using Python
Python web framework Worked with MongoDB
Containers or VM like Docker, Kubernetes, Vagrant
Ansible and Jenkins
AWS products: For Compute, Storage, Database or Networking sections
Solr or ElasticSearch


Another variation of this course could be built on “automate the boring stuff with python” where the learner would learn with his data and work projects on how to automate office stuff.

We are a group of python automation consultant in Manhattan New York working remotely from India and onsite.

Although this course is based is more focused on Machine Learning.

We provide official automation services on Excel VBA Python SQL R in New York / Indore India

Automation is the next biggest revolution, legacy methods if not replaced by automation will reduce the productivity of the firm which might even lead to extinction.

Our can reduce a lot of manual work and use lot of Excel Analytics features, our clients have reduced work by upto 50-70% which helped me focus on their product and other value addition to their core business.

Get more hours from your employees and more robust analytical framework!

Please contact me for more details about various processes that we can automate.

Please email to know more information.

Keywords: Bootcamp, Private Home Tuition

Course is developed and taken by Shivgan Joshi:


Other Supporting Instructors and Course Developers for New York Clientele:

  1. Dr. Ankit Vora – New York
  2. Shubhangi Kulkarni Online Tutor for New York
  3. Dr. Parag Parandkar – Online dedicated for New York Learner
  4. Piyush Ludh – Business developer for New York Learners

About Shubhangi –

From Hyderabad, India and is having six years of experience in the IT industry in the domain of BFSI(US Home loan mortgages, credit cards,investment banking).Having completed an executive program in applied Finance from a premier institute in India called IIM Calcutta,I am currently working as a freelancer in the field of quant finance in the sense that I take online classes on VBA, R, on various topics in quantitative finance like derivatives pricing, time series, bayesian modeling, econometric modeling, credit risk using logistic regression,ordinal and multivariate regression, technical analysis of stocks,accounting which includes business valuation,M &A, project finance,interest rate derivatives,trading strategies,financial time series,data visualisation etc, I have a bachelors in engineering(Industrial Engineering and Management) as the base degree.


Course syllabus:

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