Data Science - Introduction

 

Chapter 1 Introduction

Data science is a collection of techniques used to extract value from data. It is a useful  tool for any organization that collects, stores, and processes data as part of its operations. Data science techniques rely on finding useful patterns, connections, and relationships within data.

The use of the term science in data science indicates that the methods are evidence based, and are built on empirical knowledge, more specifically historical observations.

 Vast majority of data science practitioners today use a handful of very powerful techniques to accomplish their objectives: decision trees, regression models, deep learning, and clustering. A majority of the data science activity can be accomplished using relatively few techniques. However,  the long tail, which is made up of a large number of specialized techniques, is where the value lies, and depending on what is needed, the best approach may be a relatively obscure technique or a combination of several not so commonly used procedures. 

Artificial intelligence is about giving machines the capability of mimicking

human behavior, particularly cognitive functions. Examples would be: facial

recognition, automated driving, sorting mail based on postal code. In some

cases, machines have far exceeded human capabilities (sorting thousands of

postal mails in seconds) and in other cases we have barely scratched the

surface (search “artificial stupidity”). There are quite a range of techniques

that fall under artificial intelligence: linguistics, natural language processing,

decision science, bias, vision, robotics, planning, etc. Learning is an important part of human capability. In fact, many other living organisms can

learn.

Machine learning can either be considered a sub-field or one of the tools of

artificial intelligence, is providing machines with the capability of learning

from experience. Experience for machines comes in the form of data. Data

that is used to teach machines is called training data. Machine learning turns

the traditional programing model upside down

Data science is the business application of machine learning, artificial intelligence, and other quantitative fields like statistics, visualization, and mathematics. It is an interdisciplinary field that extracts value from data. In the

context of how data science is used today, it relies heavily on machine learning and is sometimes called data mining. Examples of data science user cases

are: recommendation engines that can recommend movies for a particular

user, a fraud alert model that detects fraudulent credit card transactions, find

customers who will most likely churn next month, or predict revenue for the

next quarter

Data science problems can also be classified into tasks such as: classification,

regression, association analysis, clustering, anomaly detection, recommendation engines, feature selection, time series forecasting, deep learning, and text

mining. This book is organized around these data science tasks. 

DATA SCIENCE ALGORITHMS

An algorithm is a logical step-by-step procedure for solving a problem.

A classification task can be solved using many different learning algorithms such as decision trees, artificial neural networks, k-NN, and even

some regression algorithms. The choice of which algorithm to use depends

on the type of dataset, objective, structure of the data, presence of outliers,

available computational power, number of records, number of attributes,

and so on. It is up to the data science practitioner to decide which algorithm

(s) to use by evaluating the performance of multiple algorithms. There have

been hundreds of algorithms developed in the last few decades to solve data

science problems

 In order to focus the appropriate amount of time on data

and algorithms, data science tools or statistical programing tools, like R,

RapidMiner, Python, SAS Enterprise Miner, etc., which can implement these

algorithms with ease, can be leveraged. These data science tools offer a library

of algorithms as functions, which can be interfaced through programming

code or configurated through graphical user interfaces.

Various Data Science Tasks and Multiple Algorithms for Each.

Classification:  Predict if a data point belongs to one of the predefined classes. The prediction will be based on learning from a known dataset

Decision trees, neural

networks, Bayesian

models, induction rules,

k-nearest neighbors

Regression Predict the numeric target label of a data point. The prediction will be

based on learning from a known dataset

Linear regression, logistic

regression

Anomaly detection:  Predict if a data point is an outlier compared to other data points in

the dataset

Distance-based,

density-based, LOF

Time series forecasting: Predict the value of the target variable for a future timeframe

based on historical values

Exponential smoothing,

ARIMA, regression

Clustering:  Identify natural clusters within the dataset based on inherit properties within the dataset

k-Means, density-based clustering (e.g., DBSCAN)

Association analysis

FP-growth algorithm, a priori algorithm

Recommendation engines:   Predict the preference of an item for a user

Collaborative filtering,  content-based filtering, hybrid recommenders