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5 Must read books for  Data Scientists

9/26/2018

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1) ​Data Science from Scratch: First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
  • Get a crash course in Python
  • Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
  • Collect, explore, clean, munge, and manipulate data
  • Dive into the fundamentals of machine learning
  • Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

 2) ​Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
  • Understand how data science fits in your organization—and how you can use it for competitive advantage
  • Treat data as a business asset that requires careful investment if you’re to gain real value
  • Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
  • Learn general concepts for actually extracting knowledge from data
  • Apply data science principles when interviewing data science job candidates

3) ​Data Smart: Using Data Science to Transform Information into Insight

​Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.

Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.

4) ​Think Like a Data Scientist: Tackle the data science process step-by-step

Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems.

By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice.​

5) ​Process Mining: Data Science in Action

​This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics.

After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. 

Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
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Deep Learning: Convolutional Neural Networks in Python

7/1/2018

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What you will learn in this course?

  • Understand convolution.
  • Understand how convolution can be applied to image effects.
  • Understand how convolution can be applied to audio effects.
  • Implement Gaussian blur and edge detection in code.
  • Implement a simple echo effect in code
  • Understand how convolution helps image classification.
  • Understand and explain the architecture of a convolutional neural network (CNN)
  • Implement a convolutional neural network in Theano
  • Implement a convolutional neural network in TensorFlow.
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About this course

This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset - which uses larger color images at various angles - so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But will show that convolutional neural networks, or CNNs, are capable of handling the challenge!

Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I'm going to show you how to build filters for image effects, like the Gaussian blur and edge detection.

Click the link or image below to access the course resources:
​Deep Learning: Convolutional Neural Networks in Python

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Scala and Spark for Big Data and Machine Learning

6/15/2018

1 Comment

 

What you will learn in this course?​

  • Use Scala for Programming
  • Use Spark to Process Large Datasets
  • Use Spark 2.0 DataFrames to read and manipulate data
  • Understand hot to use Spark on AWS and DataBricks
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About this course

Learn how to utilize some of the most valuable tech skills on the market today, Scala and Spark! In this course we will show you how to use Scala and Spark to analyze Big Data.

Scala and Spark are two of the most in demand skills right now, and with this course you can learn them quickly and easily! This course comes packed with content:
  • Crash Course in Scala Programming
  • Spark and Big Data Ecosystem Overview
  • Using Spark's MLlib for Machine Learning 
  • Scale up Spark jobs using Amazon Web Services
  • Learn how to use Databrick's Big Data Platform
  • and much more!

Click the link or image below to access the course contents:
​Scala and Spark for Big Data and Machine Learning

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1 Comment

Introduction to Artificial Intelligence ( AI ) for Beginners

6/11/2018

4 Comments

 

What will you learn in this course?

  • Understand how to manage machine learning and deep learning projects
  • Manage AI projects
  • Estimate resources required to complete an AI project
  • Make informed technical choices about your project's road-map
  • and lead your team to success.
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About this course

Are you a team-lead, manager, director, or vice-president responsible for delivering an AI project? If you need to learn the technology of AI quickly, this fast-paced course is for you.

In less than 2 hours, you will understand how artificial intelligence works, how machine learning algorithms compute models, how AI models are refined, and how multi-layer neural nets automatically identify features.
​

Click the Link or image below to access the course contents: 
​Introduction to Artificial Intelligence ( AI ) for Beginners

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4 Comments

How to create Scatterplots in R using simple,3D, ggplot and ggvis Methods

8/4/2017

0 Comments

 
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Load the Car dataset

library(datasets) cars<-cars head(cars) 
##   speed dist ## 1     4    2 ## 2     4   10 ## 3     7    4 ## 4     7   22 ## 5     8   16 ## 6     9   10 

Simple Scatter Plot

plot(cars$speed,cars$dist,main="Relation between Speed and Stopping distance of cars",xlab="Car Speed",ylab="Car Stopping distance",pch=20) 

plot of chunk unnamed-chunk-2

Add Some color and change the shape of points

plot(cars$speed,cars$dist,main="Relation between Speed and Stopping distance of cars",xlab="Car Speed",ylab="Car Stopping distance",pch=2, col="red") 

plot of chunk unnamed-chunk-3

Add a fitline to the scatter plot

plot(cars$speed,cars$dist,main="Relation between Speed and Stopping distance of cars",xlab="Car Speed",ylab="Car Stopping distance",pch=20)  #Regression Line  abline(lm(cars$dist~cars$speed),col="red")  # Lowess Line lines(lowess(cars$speed,cars$dist),col="yellow") 

plot of chunk unnamed-chunk-4

3D Scatter Plots

library(scatterplot3d) scatterplot3d(cars$speed,cars$dist,main="Relation between Speed and Stopping distance of cars in 3D",xlab = "Car Speed",ylab="Car Stopping Distance",color = "red",pch=20) 

plot of chunk unnamed-chunk-5

3D Scatter plots with colors and vertical drop lines

scatterplot3d(cars$speed,cars$dist,pch=20,highlight.3d = TRUE,type="h",main="Relation between Speed and Stopping distance of cars in 3D",xlab = "Car Speed",ylab="Car Stopping Distance") 

plot of chunk unnamed-chunk-6

Using ggplot2

library(ggplot2)  ggplot(cars,aes(x=speed,y=dist,fill=dist))+geom_point(shape=21,color="black",fill="blue")+ ggtitle("Relation between Speed and Stopping distance of cars")+labs(x="Car Speed",y="Car Stopping Distance") 

plot of chunk unnamed-chunk-7

Add regression lines

ggplot(cars,aes(x=speed,y=dist,fill=dist))+geom_point(shape=21,color="black",fill="blue")+ ggtitle("Relation between Speed and Stopping distance of cars")+labs(x="Car Speed",y="Car Stopping Distance")+geom_smooth(method = lm,color="darkred") 

plot of chunk unnamed-chunk-8

Using googleVis

library(googleVis)  op <- options(gvis.plot.tag='chart')  scatter_ggvis<-gvisScatterChart(cars,                                  options=list(legend="none",pointSize=5,title="Relation between Speed and Stopping distance of cars",vAxis="{title:'Car Stopping Distance'}",hAxis="{title:'Car Speed'}",width=800,height=500))  plot(scatter_ggvis)   

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