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120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

abtesting

This contains details of AB Testing data analysis. Source code, RMarkdown files and dataset

awesome-algorithms

A curated list of awesome places to learn and/or practice algorithms.

code_snippets

coolprojects

Some of my cool projects during my free time. Just for fun!

cracking-the-data-science-interview

A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep

data-science-interviews

Data science interview questions and answers

datasciencecapstone

Natural Language Processing Project

datascienceinterview

Interview stuff for friends

datascientisttoolbox

I took the Data Science Specialization (full 10 courses in 1 year) provided by Coursera for Johns Hopkins University. It covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

datasharing

The Leek group guide to data sharing

developing_data_products

Developing Data Products Course from the Johns Hopkins Data Science Lab

developingdataproducts

dinhtuanphan

Config files for my GitHub profile.

ds-career-resources

Compilation of resources and insights that helped me on my journey to data scientist

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

exploratorydataanalysis

gettingandcleaningdata

Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

machine_learning_coursera

Coursera machine learning specialization coursework (python based, University of Washington).

machinelearningfortherapies

Classification models on treatment therapies to improve health status of patients

motion-tracking

Track the velocity of moving objects

practicalmachinelearning

Repository for Programming Assignment for Practical Machine Learning

predictivemodeldehydrationstatus

Built a predictive model using the random forest algorithm in R to classify dehydration status based on physiological parameters. This is a research project on wearable sensors for physiological measurements that I participated in at Johns Hopkins Lab. We performed 300 clinical trials under different IRBs at Johns Hopkins Hospital.

probabilityandstatistics

Side projects during my free time, just for my learning purpose, so please forgive me for my typo or errors.

projecthappinessreport

I built machine learning models to predict/classify countries based on the happiness score. This is my side project. Algorithms used: multiple linear regression, model selections (best subset selection, forward stepwise selection, LASSO), classification (KNN, decision tree).

protocentral_fdc1004_breakout

A breakout board for the Texas Instruments FDC1004 capacitance to digital converter

py

Repository to store sample python programs for python learning

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

rcheatsheets

RStudio Cheat Sheets

realtimeobjecttracking

Developed a program to track the movement of an object inside a microchannel and calculate speed in real-time. The input is real-time videos. The algorithms used in MATLAB are background subtraction, moving average filter, linear regression, and real-time data plotting.