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algorithm

audio_reader

da_project_1_test-a-perceptual-phenomenon

Used descriptive statistics and a statistical test to analyze the Stroop effect, a classic result of experimental psychology. Gave you a good intuition for the data and use statistical inference to draw a conclusion based on the results.

da_project_2_investigate-a-dataset-with-titanic

Investigated a dataset from Titanic survivors by NumPy and Pandas. Go through the entire data analysis process, started by posing a question and finished by sharing my findings.

da_project_3_data-wrangling-with-mongodb-openstreetmap

Downloaded map of San Jose from openStreermap and used data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity, to clean the OpenStreetMap data for San Jose.

da_project_4_explore-and-summarize-data-with-red-wine

Use R and apply exploratory data analysis techniques to explore a selected data set from red wine for distributions, outliers, and anomalies.

da_project_5_identify-fraud-from-enron-email

Played detective and put machine learning skills to use by building an algorithm to identify Enron Employees who may have committed fraud based on the public Enron financial and email dataset.

da_project_6_make-effective-data-visualization-titanic

Created a data visualization from a data set that tells a story or highlights trends or patterns in the data. Used either dimple.js or d3.js to create the visualization.

exploratory_data_analysis_of_reservations_on_turo

interview-practice

Answered interview questions about goal, project, coding and algorithm.

leetcode

linear-_algebra

predict_daily_bike_rental_ridership

Use Neural Network to predicting daily bike rental ridership.

pythonql

A Query Language extension for Python: Query files, objects, SQL and NoSQL databases with a built-in query language

sdc_1_project_1_finding_lane_lines

One of the first things we would like to do in developing a self-driving car is to automatically detect lane lines using an algorithm. In this project we will detect lane lines in images using Python and OpenCV. OpenCV means "Open-Source Computer Vision", which is a package that has many useful tools for analyzing images.

sdc_1_project_2_traffic_sign_classifier

Learned about deep neural networks and convolutional neural networks to classify traffic signs. You will train a model so it can decode traffic signs from natural images by using the German Traffic Sign Dataset. After the model is trained, you will then test your model program on new images of traffic signs you find on the web.

sdc_1_project_3_behavioral_cloning

Learned about deep neural networks and convolutional neural networks to clone driving behavior. You will train, validate and test a model using Keras. The model will output a steering angle to an autonomous vehicle.

sdc_1_project_4_advanced_lane_finding

Write a software pipeline to identify the lane boundaries in a video.

sdc_1_project_5_vehicle_detection_and_tracking

Write a software pipeline to detect vehicles in a video

sdc_2_project_1_pedestrian_tracking

Fuse noisy lidar and radar data together to track a pedestrian.

sdc_2_project_2_kidnapped_vehicle

Implement a particle filter to take real-world data and localize a lost vehicle.

sdc_2_project_3_lane_keeping

Implement a controller to keep a simulated vehicle in its lane. For an extra challenge, use computer vision techniques to identify the lane lines and estimate the cross-track error.

sql_cheat_sheet

This cheat sheet includes most function in SQL, enough for general data administration based on SQL.