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40-techniques-used-by-data-scientists

40 Techniques Used by Data Scientists

airlines-time-series-in-2029

Prediction of how many passengers in the 2029

amazon-japan-help-desk

Amazon Japan Help Desk on Twitter

analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

appliedpredictivemodeling

:exclamation: This is a read-only mirror of the CRAN R package repository. AppliedPredictiveModeling — Functions and Data Sets for 'Applied Predictive Modeling'. Homepage: http://appliedpredictivemodeling.com/

bankscrape

Web Scrapers for Financial Institutions

bayes-formula

bayes formula

bayes-pred

bitcoinforecast

Predict bitcoin price with deep learning

bitvision

Terminal dashboard for Bitcoin trading, forecasting, and charting

books

case-study-data-driven-decision-making-with-sql-

cashflows

Financial investment modeling and advanced engineering economics using Python

class

classification-metrics-in-scikit-learn

Understanding Data Science Classification Metrics in Scikit-Learn in Python

cleanfiletwo

Import the dataset and the package will clean the dataset. It will also subset the data into factors, numbers, and doubles. It uses the describe function in order to analyze the dataset. It will save the original, clean and RDATA files. After the function is run, you can load the RDATA to work in. After running the function, type: load("cleanmework.RData")

cleanmefile

The data file will take all the NULL and NA values. Save the original file and the clean to the c: drive. Create the data file into a dataframe. Explore the data with a summary, describe and histograms.

courses-intro-to-git

Introduction to Git for Data Science by Greg Wilson

courses-intro-to-python

Intro to Python for Data Science by Filip Schouwenaars

credit-card-marketing-model-

credit_card_marketing_model

A credit card is a payment card issued to users (cardholders) to enable the cardholder to pay a merchant for goods and services based on the cardholder's promise to the card issuer to pay them for the amounts so paid plus the other agreed charges.The card issuer (usually a bank) creates a revolving account and grants a line of credit to the cardholder, from which the cardholder can borrow money for payment to a merchant or as a cash advance. In other words, credit cards combine payment services with extensions of credit. Complex fee structures in the credit card industry may limit customers' ability to comparison shop, help ensure that the industry is not price-competitive and help maximize industry profits. Because of this, legislatures have regulated credit card fees.

cryptocurrency-analysis

Analysis and visualisation of the cryptocurrency market

dagdata

Data for the HarvardX courses: PH525x

data-analysis

Data Science Using Python

data-science-in-education

Book on data science (or data analysis) in education using R

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

data-visualization-with-seaborn

datacamp-community-tutorials

Tutorials for DataCamp (www.datacamp.com)

datascienceplus_creditcard

Cleaning & Modifying A Dataframe – Python for datascienceplus

demorgans-law

DEMORGANS LAW