Offering Data Science Tutoring to Individuals or Small Groups! (Midtown East)

Sample profile of our students:
“I graduated college with a math degree and limited coding experience. As I moved through my career I became aware of the tremendous opportunity that is out there for anyone willing to take the time to learn Data Science. Through much trial and error of finding I found LPNYC, after learning Python, SQL, R, Machine Learning, Data Visualization, and so much more. This led to greater employment opportunities where my Data Science knowledge grew exponentially. It turns out being self-taught and from a non-engineering background made me ready for the taking courses at LearnProgrammingNyc.”

There are no prerequisites, as we can shape tutoring sessions to individual needs. We can work with you if you have limited coding experience but strong knowledge of math/stats. We can work with you if you are a coding wizard who needs to learn to think analytically. We can work with you if you are new to all of it but want to take a leap of faith! If you have a specific dataset or question you’d like to work on, that’s a great place to start. Or I can complement a University course or Boot Camp.

Specific topics that we can cover:
Python Programming Language
SQL Querying
R Programming Language
Data Structures and using Data Frames
Basic Statistics
Statistical Learning
Statistical Modeling
Machine Learning
Artificial Intelligence
Advanced Data Manipulation
Data Visualization

I charge $29-39 per 1-hour session for an individual. Groups of 2 or more will receive a per-person discount. Any Tutoring client is also given direct access to our instructors throughout the week to help answer questions and guide your own self-learning between sessions.

If you are interested let’s talk! You’re closer to being a Data Scientist than you think!

WHAT IS DATA SCIENCE

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

RESEARCH DESIGN AND PYTHON PANDAS / DATA WRANGLING TERMS AND CONCEPTS

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

STATISTICAL FUNDAMENTALS I

1. 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.
2. Create data visualization – scatter plots, scatter matrix, line graph, box blots, and histograms- to discern characteristics and trends in a dataset.
3. Identify a normal distribution within a dataset using summary statistics and visualization.

STATISTICAL FUNDAMENTALS II

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

DATA SCIENCE IN THE REAL WORLD

DECISION TREES AND RANDOM FOREST

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

NATURAL LANGUAGE PROCESSING

1. Demonstrate how to tokenize natural language text
2. Categorize and tag unstructured text data
3. Explain how to build a text classification model using spacy

DIMENSIONALITY REDUCTION

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

TIME SERIES DATA

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

CREATE MODELS WITH TIME SERIES DATA

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

DATABASE TECHNOLOGIES

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

Ankit Vora, PhD

Ankit Vora is a research scientist and a co-founder of a startup situated in the city of New York, and his research focuses on detecting cancer using machine learning algorithms. He also works on automating agricultural machinery using artificial intelligence and building smart robotics and drones for a host of applications. Ankit did his doctoral work on Meta-Materials for solar cells at the Michigan Technological University. He has an extensive teaching and mentoring experience; from mentoring university students, teaching to academic and industry professionals. Ankit has been teaching Python and other programming languages for the past five years to the industry professionals. As Python is gaining momentum and becoming popular, it is finding applications in the high demand fields such as data science, machine learning, artificial intelligence, big data, etc. Ankit has been actively involved in training and nurturing professionals into these high demand fields, especially data science and machine learning using Python. He also does professional consulting for industrial and academic projects.

Ankit’s link to professional profile on LinkedIn: https://www.linkedin.com/in/drankitvora

Google Scholar profile: https://scholar.google.com/citations?user=8rrNVZwAAAAJ&hl=en

Shivgan Joshi

Shivgan Joshi is an executive trainer and talent acquisition consultant at QcFinance and has worked in the field of data science across disciplines, including engineering, investment banking, and technology. Previously, Joshi served as data science manager for BaiNYC and co-founder of QcFinance. At QcFinance, Joshi developed Python, R, SQL, MatLab, VBA, and BAT courses focused on all aspects of finance, including trading and modeling of quantitative portfolios.

He is passionate about elevating businesses through the implementation of data science and data analytics. He has delivered online, live, and interactive training sessions to students and executives around the globe. Joshi realizes the value of adequately leveraging data and commits to helping businesses to do successfully. He holds an undergraduate and graduate degree in data science as well as a Master of Business Administration.

Or Call Us at : 1 31228 56886