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airbnb-ratings

Cohort 15 Capstone Project for the Certificate of Data Science at Georgetown University School of Continuing Studies.

awesome-customer-analytics

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

beautifulsoup_webscraping

A collection of Webscraping projects from Codecademy, DataJournalism.com, DataCamp, and Dataquest

data-scientist-with-python-datacamp

This is about learning data scientist with Python courses in DataCamp. All the answers given written by myself

dataquest-a-quest-2-data-science

dataquest-assignments

Dataquest assignments except guided projects

dataquest.io

Missions solved inside Data Science courses

dbm-statistics

DoubleClick Bid Manager utilities to create, update and download queries. More info about API: https://developers.google.com/bid-manager/guides/getting-started-api

digital-marketing

Built Neural network to predict sales and top 3 digital marketing platforms

digital-marketing-tracker

A google sheets automize tracker that collects data from Appnexus and Doubleclick marketing campaigns

digital_marketing

Acadamic code projects for Digital Marketing

django-react-boilerplate

A Django and React user authentication project boilerplate.

dma_team6_finalproject

Digital Marketing Analytics

dsmp-pre-work

Code repository for the Pre Work

evernote-clone

facebook-api-v3

A collection of functions for facebook and google marketing automation

facebookads-data-to-csv

This script was used to extract data from Facebook's Marketing API.

facebookmarketing-python

Facebook Marketing API wrapper written in python

fb_marketing_api

Implementing Facebook's marketing API using Python

google-ads-performance-downloader

A script for downloading performance and account structure from Google AdWords API

googleads-python-lib

The Python client library for Google's Ads APIs

googlebidder

Real Time Bidder For DoubleClick AdExchange

handling-program-flow-in-python-code-along-practice

This repository contains code for the code along session for Handling program Flow in Python Code

keywords

Keyword Development for Digital Marketing

leanote

Not Just A Notepad! (golang + mongodb) http://leanote.org

machine-learning-with-python

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

marketing-performance-report

This is one of the first and main programs i made for 99

medium-clone-on-node

clone Medium on Nodejs and Reactjs

medium-editor

Medium.com WYSIWYG editor clone. Uses contenteditable API to implement a rich text solution.

numpy

The fundamental package for scientific computing with Python.