Excel VBA Help onsite consultant nyc

Understand legacy models and create documentation and Python alternative to the Excel VBA Model.

Understand VBA Model and create notes or simpler alternative.

Create Excel VBA models for particular case.

Executive Training to make your staff more effecient.

Any kind of EXCEL VBA consulting 360 support, subscription plan for 24 7 support.



Improve a process or automate a tool, you need help. Send us your description and we would be happy to give you a quote with several subscription and maintenance onsite and remote options. 

DevOps 101 for Data Science / Data Analyst / Big Data

What you'll learn in this data science course:

2 hour in-person, on-site course in NYC.

This class is meant for Data Science, Data Engineer and Big Data Developers / Admin.

The main language for this class would be Python and cloud used AWS.

This course will help you learn DevOps skills like Continuous Integration, Delivery and Deployment, Infrastructure as a Code and more using DevOps tools like Git, SVN, Docker, Docker Swarm, Kubernetes, Puppet, Ansible, Selenium, Maven, Nagios, etc.

The topics discussed would be:

• Introduction to DevOps methodology
• Introduction to AWS Docker using Amazon Elastic Container Service (Amazon ECS)
• Implementing Software Version Control example Git
• Software Virtualization using Containerizing Code on production using Docker
• Creating CI/CD Pipelines using Jenkins
• Configuration Management using Puppet and Ansible
• Automating build and test using Selenium and Maven. Python & Selenium.
• Container Orchestration using Kubernetes
• Performance Tuning and Monitoring using Nagios

Course is taught by expert instructor with experience in industry and teaching.

Machine Learning 101 Python Weekend Bootcamp Class NYC

What you'll learn in this machine learning course:

Machine learning is going to disrupt a lot of industries in the next decade. Whether it be driverless cars, cashierless shops, personal assistant or AI physicians, the effect of machine learning will be pervasive. 

Prepare for the next big disruption. This class assumes you don’t have any programming background. However, it is recommended to have a basic understanding in Python. Understanding of Pandas Python Library will help a lot. 

You will know when to run supervised or unsupervised learning for your data, whether to use classification or regression model, how to handle categorical vs continuous data. After the data is ready you will learn how to split the data and analyze the final results. We will use a lot of images to delineate different terms and topics used in Machine Learning. Although we would use classical data sets like IRIS, Titanic, etc but you will be scale and use your data for the models learned in the session. 

Takeaways include developing basic vocabulary for: Run machine learning models on your data using the setup clear.

Topics covered:

• Supervised vs Unsupervised Learning
• Regression vs Classification models
• Categorical vs Continuous feature spaces
• Python Scikit-learn LibraryModeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
• Interpreting Results of Regression and  Classification Models
• Parameters and Hyper Parameters
• Dimension Reduction
• SVM, K-Nearest Neighbor, Neural Networks

Projects for the session (Python):

1. Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
2. Calculating R-Square, MSE, Logit manually in excel for enhanced understanding
3. Understanding features of Popular Datasets: Titanic, Iris and Housing Prices
4. Running Logistic Regression on Titanic Data Set
5. Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset

Post Session Assessment:

1. Top 20 machine learning interview question
2. Small Project for Github
3. Make data ready, choose and configure the correct model for your data
4. Interpret results of your machine learning algorithm

Data Science 101 Python Weekend Evening Data Analytics

What you'll learn in this data science course:

Class for Beginners for audience of NYC! (5 hours Onsite Class in New York!)

Python Data Science knowledge opens door to great salaries and career in the current industry. Python Big Data Science salaries and career starts with a very attractive salary especially in NYC where the demand is every increasing. 

Course introduces Data Science concepts hands on using Jupyter notebook on Azure which comes with all the required libraries like Scikit and Pandas.

Learners will make their own Data science project with their own data that they will pull from popular data science datasets at Kaggle, Google Open Data in CSV or JSON format from flat file or API etc.

Agenda for the class will be to touch and use the below tools:
Matplotlib Numpy Pandas Scipy Lambdas Python  Collection of powerful, open-source, tools needed to analyze data and to conduct data science.Working with jupyter anaconda notebooks pandas numpy matplotlib git and many other tools.Data Loading, Storage, and File Formats like CSV, JSON and from APIsData Cleaning and PreparationData Wrangling: Join, Combine, and Reshape in Pandas DataFramesPlotting and VisualizationData Aggregation and Group OperationsAfter learning this sessions the learner will have enough exposure to revise and keep marching ahead to become a Data Scientist and start with structured, unstructured and Semi Structured data. 

Student will overcome basic syntax errors and setup troubles to create his launchpad project for a real data science project. The group size is small for individual attention. 

Based on the audience of NYC, New York the author fine tunes the lesson so that every learner is engaged and get individual attention. 

This is a one day (5 hours) course that repeats every week.

Python is a very popular programming language used by companies like Google, Facebook, Amazon, Microsoft, etc. Python is used for all variety of things like building websites using Django Python, web scraping, data analysis, machine learning, and natural language processing using Python.

Please bring your own device.

Data Analytics 101 Bootcamp Excel, VBA, SQL & Python

Data Analytics 101 Bootcamp

5 hour Course in Queens Jackson Heights

This one day Bootcamp will cover the below topics:

• Data Analytics in Excel: Vlookup, Match, Index, Offset
• Advanced Formulas in Excel for Analytics
• Data Cleaning in Excel using left right len trim
• VBA for Data Analytics Course - Loop through spreadsheets range
• Looping in VBA using For, while and Case
• SQL Relational Database Concepts - Primary Key / Foreign Key Foreign Key Primary Key Normalization to move your excel data to SQL
• Calculations and Analytics in PYTHON
• Loops and Functions (for, while)Python for Joins, Group by, Filters

After this session you will be able to:

• Automate Excel using VBA, SQL and Python
• Understand Charting capabilities of SQL and Python
• Get introduced to coding in VBA SQL and Python for Data Analytics

Our instructor has taken several sessions in NYC on Data Analytics and will explain you all concepts in a very eloquent manner.

Intro to SQL / No SQL / Spark for Data Science NYC

What you'll learn in this sql class:

2 hours hands on interactive in person SQL course in heart of NYC.

Data pulling and organization is a big effort in Data Science. Data exists in variety of Data source and the hardest part is to create a final Data Frame to run Data Science Algorithms. 

Most of job today requires Data Science Teams to get data from various sources. Creating data lakes is picking space but at this moment the the velocity of new data is so fast that companies expect Data Scientists to access data from variety of sources. Getting muddled up in Machine Learning Math sometimes leads new learners no chance to delve into data engineering aspects. 

Topics:

• Jupyter Notebook
• Connect MySQL to Python
• Connect SQLite to Python
• Push Dataframe to SQL - SQL to Dataframe
• Push MongoDB JSON to Pandas - Pandas to MongoDB
• PySpark for Unstructured Data & running Map reduce
• Learning MLib for running Machine Learning
• Scraping data from Beautiful soup

This is an optimal course for people who wants to get deeper in Data engineers aspect of Data Science. 

You will be able to answer the below questions after you take this session:

• How would I create a data science policy to utilize SQL and NoSQL Databases?
• Should I use Hadoop in my current data?
• How to connect Python to MySQL to improve intra team collaboration and putting my machine learning algo into an app?
• How to parse JSON data or data from API?
• How to use Pyspark to take care of unstructured data in Python?

Our instructor has extensive experience in delivering data science and data engineering courses in New York City.

Advanced Python: OOPs & Functional Paradigms NYC 103

What you'll learn in this python class:

Object Oriented and Functional Programming in Python
2 Hours Class 
Pre-requisite: Python 101 (basic) + 102 (interim)

After this class you will be able to understand Python more deeply and launch your career from scripting to software development / web development / Quant in Python . If you know basic syntax of Python then this course will launch you to the next level. 

Directions can you go if you learn OOP and Functional Python:

1. Web development: Django uses classes and functional style extensively.
2. Banks are converting their old code from proprietary softwares to the open source world so that they can harness the evolution in Big Data and Python.
3. Banks are building their own libraries and putting them in libraries to run their analytics which was earlier done by companies like MATLAB, SAS, etc. If you are a CFA, FRM or Quant developer this courses help you understand what you need to learn in Python.
4. Code made earlier can be made more optimal using functional aspects and then create classes and libraries of your earlier code. This ways you can organize your code and share it in a library.

This course introduces to OOPS and Functional aspects of Python.

Python allows us to use Object Oriented, Functional and scripting and hence it the language of data science, web, big data and software development.
Object oriented programming involves classes, inheritance, meta classes, encapsulation, overloading using classes.


Main Topics to be Discussed:

• Functional programming
• Decorators & Higher Order Functions
• Call backs
• Late binding / Closures
• Pass by reference / pass by value
• Object Oriented Programming
• Meta Programming & Meta Classes
• Abstract Class  / ABC Class
• __new__ vs __init__
• Types of Inheritance
• Super function in Python
• Generators, Iterators, Decorators, and Context Managers
• Unit Testing Python
• Multithreading in Python? Why is it a bad idea?
• Why a list comprehension is faster than a for loop (which really is to say understand how bytecode is generated, at high level)
• Overloading
• Other concepts
• What is the difference between deep and shallow copy?
• Terms for job interviews
  
  
  Our instructor has experience in teaching Python with people with little or no experience

Python Django 101 Web Development in Python

What you'll learn in this python class:

2 hours In-Person On-site class in NYC

Overview of Python. Introduction to Django

Traditional back end is SQL in Django. Implementing SQLite database and  developing front end with HTML, CSS, JavaScript, JQuery and Bootstrap. 

Topics include: Django views, class-based views, URLs, middleware, forms, templates, and templates. Django models, including model relationships, migrations, queries, and forms

We will see how to use different templates of building Django Websites.

• Installation
• Overview
• Virtual Environment
• Starting Django Project
• Overview & Creating App
• Views
• URL Mapping/ URL Dispatcher
• Regular expressions
• Error/Exception handling
• Decorators
• Templates
• Django Template language
• Filters
• Models
• Fields
• Queries [with Filters]
• Migrations
• Forms
• Validations
• Fields & Widgets

Expert Instructor with experience in Python, Data Analytics and Back end development. 

Intermediate Python 102 Evening Data Science

What you'll learn in this python class:

This course is to be taken after taking Python 101 or our Weekend Python Course. Check out the courses before enrolling in 102 course!

This is a 2 hour course hands on in-person class in NYC.
So you are done with Python 101! Practice our quiz before taking this course to revise what you did in Python 101.

This class is perfect for people who want to dive deeper in functional and object oriented aspect of Python. For example, these concepts are extensively used on Django web development, creating your own library, Python for software engineering or developing production level Python code.

This course also helps Data Science aspirants gets more deeper into Python to design and optimize their code. 

This course is not about Pandas or Numpy, check out Data Science 101 or Pandas course for those topics.

Topics to be discussed:
Range (Looping through range) & List comprehensions

Lambda functions for Map Reduce / Data Frames

Slicing of List, Dict, Sets, Dataframes

Object Oriented Python: Classes, Inheritance, Deep copy & Shallow copy Exception handling in Python

Iterators, Generators and Closures

Functional Programming & Decorators

Higher Order Functions

After this course you will feel confident to handle projects in Django, launch your Data Science project in production Mode. Use python in big data, create custom Python application. Automate all artificial intelligence aspect of your business. 

Our instructor has worked on several projects and helps students learn advanced aspect of Python which is otherwise hard to comprehend in New York City.

If you want to take your python career higher and be confident in Python then this course is for you.
Python is a very popular programming language used by companies like Google, Facebook, Amazon, Microsoft, etc. Python is used for all variety of things like building websites using Django Python, web scraping, data analysis, machine learning, and natural language processing using Python.
Python allows you to code fast, building complex applications with minimum lines of code and use existing libraries and use cloud infrastructure resulting true use of Infra on Cloud and code that is 5 times less than Java and 10 times less than C++ / C#.

Topics:

1. InheritanceIterators
2. Generators
3. Higher Order Functions
4. Decorators
5. Closures
6. Operator Overloading

Machine Learning Topics

Error functions, how to minimize errors (gradient descent)

What is alpha
Gradient Descent
Gradient descent keeps changing the Parameters to reduce the cost function gradually. With each iteration we shall come closer to a minimum. With each iteration the parameters must be adapted simultaneously! The size of a “step”/iteration is determined by the parameter alpha (the learning rate).
https://towardsdatascience.com/machine-learning-basics-part-1-concept-of-regression-31982e8d8ced
Partial Derivative Function


How to tune algorithms
Add parameters - time series, lags


Regularization - ridge, lasso
minimization of coeffeiences

When to use what
Process, steps, examples of data prep
https://scikit-learn.org/stable/modules/preprocessing.html

Scaling, one-hot encoding, outliers

One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction


Error functions, how to minimize errors (gradient descent)
What is alpha
How to tune algorithms
Regularization - ridge, lasso
When to use what
Process, steps, examples of data prep
Scaling, one-hot encoding, outliers


Demantra | Oracle Products - to predict demand using Time series modeling (Lags, Dummy Variables, Time Series)
Gradient descent - Derivation, partial differentiation
PCA analysis - Derivation
supply chain management machine learning
ARIMA Model for Time Series Forecasting in Python

Exploratory data analysis with Spark

Cholesky transform
https://en.wikipedia.org/wiki/Cholesky_decomposition
Lower diagnol matrix

We are going to calculate a matrix that summarizes how our variables all relate to one another.
We’ll then break this matrix down into two separate components: direction and magnitude.

https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c

https://www.kaggle.com/nishantbigdata/exploratory-data-analysis-with-spark

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Learn Python Data Science Class Bootcamp NYC (Affordable Coding Programming for Machine Learning) 1 on 1 Tutoring also available. New York Python SQL Bootcamp Coding Classes (Affordable & Cost-effective Machine Learning). Best Free classes in NYC. SQL 101 & Python 101 Classes. Big Data Science Classes for beginners interested in Analytics & Data Science. Weekend part time and full time classes in Manhattan & Queens. 1 on 1 Tutoring also available. Free weekend 2hrs class. Small group courses (2-3 attendees), free takes and 1 on 1 : Python 101, Python Data Science Immersive Python for Data Analytics. VBA Macros Immersive. SQL 1 day Class.Project and Portfolio Oriented on weekends and also free evening classes in NYC. Upload your portfolio to get better job. Best Python Class in NYC. FREE RETAKES.

Cost effective and Reasonable Education for Data Science Enthusiast