Statistics In Python

In the era of big data and artificial intelligence, data science and machine learning have become essential in many fields of science and technology. A necessary aspect of working with data is describing, summarising, and visually representing data. Statistics in python is a popular and widely used tool that will assist you in working with data.

There are many Python statistics libraries out there for you to work with, but in this book, you’ll be learning about some of the most popular and widely used ones:

  • Python’s statistics is a built-in Python library for descriptive statistics. You can use it if your datasets are not too large or if you can’t rely on importing other libraries.
  • NumPy is a third-party library for numerical computing, optimized for working with single- and multi-dimensional arrays. Its primary type is the array type called ndarray. This library contains many routines for statistical analysis.
  • SciPy is a third-party library for scientific computing based on NumPy. It offers additional functionality compared to NumPy, including scipy.stats for statistical analysis.
  • Pandas is a third-party library for numerical computing based on NumPy. It excels in handling labelled one-dimensional (1D) data with Series objects and two-dimensional (2D) data with DataFrame objects.
  • Matplotlib is a third-party library for data visualization. It works well in combination with NumPy, SciPy, and Pandas.

Data Science Interview Questions and Answers

164 data science interview questions and answers will help you to master the art of interviewing for a data science position, from job-specific technical questions to tricky behavioural inquiries and unexpected brainteasers and guesstimates. This book will prepare you for any job candidacy in the field – data scientist, data analyst, BI analyst, data engineer or data architect.

Its goal is to teach by example – not only by giving you a list of interview questions and their answers but also by sharing the techniques and thought processes behind each question and the expected answer. Once you read it, you’ll have all the knowledge and tools to succeed during the data science interview.

How to Use This Book for Best Results? Award yourself with enough time to work through the
questions. This way, you’ll really understand what they are asking and what information you should highlight for the best response. If studied well, this book will enhance both your technical and communication skills.

Regression Models For Data Science In R

This book is designed as a companion to the Regression Models Coursera class as part of the Data
Science Specialization, a ten-course program offered by three faculty, Jeff Leek, Roger Peng and
Brian Caffo, at the Johns Hopkins University Department of Biostatistics. The videos associated with this book can be watched in full here, though the relevant links to specific videos are placed at the appropriate locations throughout. Before beginning, we assume that you have a working knowledge of the R programming language.


If not, there is a wonderful Coursera class by Roger Peng, that can be found here. In addition, students should know the basics of frequentist statistical inference. There is a Coursera class here and a LeanPub book here. The entirety of the book is on GitHub here. Please submit pull requests if you find errata! In addition, the course notes can also be found on GitHub here. While most code is in the book, all of the code for every figure and analysis in the book is in the R markdown files (.Rmd) for the respective lectures.


Finally, we should mention swirl (statistics with interactive R programming). swirl is an intelligent
tutoring system developed by Nick Carchedi, with contributions by Sean Kross and Bill and Gina
Croft. It offers a way to learn R in R. Download swirl here. There’s a swirl module for this course!.
Try it out, it’s probably the most effective way to learn.

Power BI for Beginners: A Step-by-Step Training Guide

Power BI is a Business Intelligence tool developed by Microsoft. It helps you interactively
visualise your data and make intelligence-based business decisions as a result. Key features of Power BI:
• Quick set-up comparative to traditional BI
• Interactive visualisations
• Supports different data sources (Microsoft or otherwise)
• The ability to publish to the web (app.powerbi.com)
• Cloud-based, no on-premise infrastructure needed
• Scalable
• Accessibility – view the dashboards/reports on iPad, iPhone, Android, and Windows
devices Scheduled data refresh

In this how-to guide, the writer gives you an overview course and how it can be used to load, manipulate, model, and report on data to assist with your reporting requirements. The scenario we’ll run through is how to report on internet sales for the fictitious AdventureWorks bicycle company and add some common time intelligence measures, for example, period to date and period against previous period reporting. We will take you through the typical loading data, modelling data and then visualising the data.

Power BI Desktop is used to access data sources, shape, analyse and visualise data,
and publish reports. Once installed on your local computer, it lets you connect to data from different sources, transform, and visualise your data. Power BI Desktop is available for free via a direct download link here.

Contents

  1. Introduction
  2. Overview of Power BI
  3. Getting Started
  4. Connecting to Data Sources
  5. Modelling the Data – Creating Relationships
  6. Reporting on the Data – Creating Visualisations
  7. Conclusion
  8. Appendix

THE FIELD GUIDE TO DATA SCIENCE

The field guide to data science is a textbook for students who love data science. The writers of this textbook have a deeper understanding of the concepts at the heart of Data Science. Data is the byproduct of our new digital existence. Recorded bits of data from mundane traffic cameras to telescopes peering into the depths of space are propelling us into the greatest age of discovery our species has ever known. Every aspect of our lives, from life-saving disease treatments to national security, to economic stability and even the convenience of selecting a restaurant, can be improved by creating better data analytics through Data Science.

The Field Guide to Data Science provides Booz Allen’s perspective on the complex and sometimes mysterious Field of Data Science. We cannot capture all that is Data Science. Nor can we keep up – the pace at which this field progresses outdates work as fast as it is produced. As a result, writers have opened this field guide to the world as a living document to bend and grow with technology, expertise, and evolving techniques. If you find the guide to be useful, neat, or even lacking, then we encourage you to add your expertise, including:
› Case studies from which you have learned
› Citations for journal articles or papers that inspire you
› Algorithms and techniques that you love
› Your thoughts and comments on other people’s additions

The field guide to data science

Neural Networks And Deep Learning

Neural networks and deep learning were developed to simulate the human nervous system for machine learning tasks by treating the computational units in a learning model in a manner similar to human neurons. The grand vision of neural networks is to create artificial intelligence by building machines whose architecture simulates the computations in the human ner-
nervous system. This is obviously not a simple task because the computational power of the fastest computer today is a minuscule fraction of the computational power of a human
brain.

Related Post: A VISUAL INTRODUCTION TO DEEP LEARNING

Neural networks were developed soon after the advent of computers in the fifties and
sixties. Rosenblatt’s perceptron algorithm was seen as a fundamental cornerstone of neural
networks, which caused an initial excitement about the prospects of artificial intelligence.
However, after the initial euphoria, there was a period of disappointment in which the data-hungry and computationally intensive nature of neural networks was seen as an impediment to their usability.

Related post: Download Statistics And Machine Learning In Python

Eventually, at the turn of the century, greater data availability and increasing computational power lead to increased success of neural networks, and this area was reborn under the new label of “deep learning.” Although we are still far from the day that artificial intelligence (AI) is close to human performance, there are specific domains like image recognition, self-driving cars, and game-playing, where AI has matched or exceeded human performance.

It is also hard to predict what AI might be able to do in the future. For example, few computer vision experts would have thought two decades ago that any automated system could ever perform an intuitive task like categorizing an image more accurately than a human.

Neural networks are theoretically capable of learning any mathematical function with
sufficient training data, and some variants like recurrent neural networks are known to be
Turing complete. Turing completeness refers to the fact that a neural network can simulate
any learning algorithm, given sufficient training data.

The sticking point is that the amount of data required to learn even simple tasks are often extraordinarily large, which causes a corresponding increase in training time (if we assume that enough training data is available in the first place).

For example, the training time for image recognition, which is a simple task for a human, can be on the order of weeks even on high-performance systems. Furthermore, there are practical issues associated with the stability of neural network training, which are being resolved even today. Nevertheless, given that the speed of computers is expected to increase rapidly over time, and fundamentally more powerful paradigms like quantum computing are on the horizon, the computational issue might not eventually turn out to be quite as critical as imagined.

COMMON STATISTICAL DISTRIBUTIONS

Statistical Distributions are an important tool in data science. A distribution helps us to understand a variable by giving us an idea of the values that the variable is most likely to obtain.

Besides, when knowing the distribution of a variable, we can do all sorts of probability calculations, to compute probabilities of certain situations occurring.

In this article, I share 6 Statistical Distributions with intuitive examples that often occur in real-life data.

COMMON STATISTICAL DISTRIBUTIONS
COMMON STATISTICAL DISTRIBUTIONS

1. Normal or Gaussian distribution

COMMON STATISTICAL DISTRIBUTIONS

The Normal or Gaussian distribution is arguably the most famous distribution, as it occurs in many natural situations. A normal distribution shows the probability density for a population of continuous data (for example height in cm for all NBA players)

In other words, it shows how likely is it that any player from the NBA is of a certain height. Most players are around the mean/average height, fewer are much taller, or much shorter. A normal distribution is symmetrical on both sides of the mean.

2. T-Distribution

COMMON STATISTICAL DISTRIBUTIONS

Just like a normal distribution, a t-distribution is symmetrical around the mean, and the breadth is based on the deviation within the data. While a normal distribution works with a population – a t-distribution is designed for situations where the sample size is small. The shape of the T distribution becomes broader as the sample size decreases, to take into account the extra uncertainty we are faced with.

The shape of a t-distribution relates to the number of degrees of freedom which is calculated as the sample size minus one. As the sample size, and thus the degrees of freedom gets larger, the distribution tends towards a normal distribution – as with a larger sample we’re more certain about estimating the true population statistics.

3. Binomial Distribution

COMMON STATISTICAL DISTRIBUTIONS
COMMON STATISTICAL DISTRIBUTIONS

A Binomial Distribution can end up looking a lot like the shape of a normal distribution. The main difference is that instead of plotting continuous data, it instead plots a distribution of two possible discrete outcomes, for example, the results from flipping a coin.

Imagine flipping a coin 10 times, and from those 10 flips, noting down how many were “Heads”. It could be any number between 1 and 10. Now imagine repeating that task 1,000 times…

If the coin we are using is indeed fair (not biased to heads or tails) then the distribution of outcomes should start to look at the plot above. In the vast majority of cases, we get 4, 5, or 6 “heads” from each set of 10 flips, and the likelihood of getting more extreme results is much rarer!

4. Bernoulli Distribution

COMMON STATISTICAL DISTRIBUTIONS

The Bernoulli Distribution is a special case of the Binomial Distribution. It considers only two possible outcomes, success or failure, true or false.

It’s a really simple distribution, but worth knowing! In the example below we’re looking at the probability of rolling a 6 with a standard die.

If we roll a die many, many times, we should end up with a probability of rolling a 6, 1 out of every 6 times (or 16.7%) and thus a probability of not rolling a 6, in other words rolling a 1,2,3,4 or 5, 5 times out of 6 (or 83.3%) of the time!

5. Uniform Distribution

COMMON STATISTICAL DISTRIBUTIONS

A Uniform Distribution is a distribution in which all events are equally likely to occur. Below, we’re looking at the results from rolling a die many, many times.

We’re looking at which number we got on each roll and tallying these up. If we roll the die enough times (and the die is fair) we should end up with a completely uniform probability where the chance of getting any outcome is exactly the same.

6. Poisson Distribution

COMMON STATISTICAL DISTRIBUTIONS

A Poisson Distribution is a discrete distribution similar to the Binomial Distribution (in that we’re plotting the probability of whole numbered outcomes) Unlike the other distributions we have seen, however, this one is not symmetrical – it is instead bounded between 0 and infinity

The Poisson distribution describes the number of events or outcomes that occur during some fixed interval. Most commonly this is a time interval like in our example below where we are plotting the distribution of sales per hour in a shop.

Download Mostly Harmless Statistics

Mostly Harmless Statistics is a great book for students new to statistics and are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students. This book is an introductory level probability and statistics course with an intermediate algebra prerequisite.

The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples are provided for Microsoft Excel, TI-84 & TI-89 calculators. A separate document is provided on the website with examples in SPSS.

Table of Contents

Chapter 1 Introduction to Data

Chapter 2 Organizing Data

Chapter 3 Descriptive Statistics

Chapter 4 Probability

Chapter 5 Discrete Probability Distributions

Chapter 6 Continuous Probability Distributions

Chapter 7 Confidence Intervals for One Population

Chapter 8 Hypothesis Tests for One Population

Chapter 9 Hypothesis Tests & Confidence Intervals for Two Populations

Chapter 10 Chi-Square Tests

Chapter 11 Analysis of Variance

Chapter 12 Correlation and Regression

Chapter 13 Nonparametric Tests

A VISUAL INTRODUCTION TO DEEP LEARNING

Deep learning is the algorithm powering the current renaissance of artificial intelligence (AI). And its progress is not showing signs of slowing down. A McKinsey report estimates that by 2030, AI will potentially deliver $13 trillion to the global economy, or 16% of the world’s current GDP. This opens up exciting career opportunities in the coming decade.

But deep learning can be quite daunting to learn. With the abundance of learning resources in recent years has emerged another problem—information overload.

This book aims to compress this knowledge and make the subject approachable. By the end of this book, you will be able to build a visual intuition about deep learning and neural networks.

Who should read this book

If you are new to deep learning, or machine learning in general.

If you already know some background about deep learning but want to gain further intuition.

A VISUAL INTRODUCTION TO DEEP LEARNING
A VISUAL INTRODUCTION TO DEEP LEARNING

Download Data Analytics By Arthur Zhang

Data is important because you need information about certain aspects of your
business to determine the state of that aspect and how it affects overall business
operations. For example, if you don’t keep track of how many units you sell per
month, there is no way to determine how well your business is doing. There are
many other kinds of data that are important in determining business success that
will be discussed throughout this book data analytics by Arthur Zhang .


Collecting the data isn’t enough, though. The data needs to be analyzed and
applied to be useful. If losing a customer isn’t important to you, or you feel it
isn’t critical to your business, then there’s no need to analyze data. However, a
continual lack of appreciation for customer numbers can impact the ability of
your business to grow because the number of competitors who do focus on
customer satisfaction is growing. This is where predictive analytics becomes
important and how you employ this data will distinguish your business from
competitors. Predictive analytics can create strategic opportunities for you in the
business market, giving you an edge over the competition.

Download Data Analytics By Arthur Zhang

Table of Contents

CHAPTER 1: WHY DATA IS IMPORTANT TO YOUR BUSINESS

CHAPTER 2: BIG DATA

CHAPTER 3: DEVELOPMENT OF BIG DATA
CHAPTER 4: CONSIDERING THE PROS AND CONS OF BIG DATA

CHAPTER 5: BIG DATA FOR SMALL BUSINESSES? WHY NOT?

CHAPTER 6: IMPORTANT TRAINING FOR THE MANAGEMENT OF
BIG DATA

CHAPTER 7: STEPS TAKEN IN DATA ANALYSIS

CHAPTER 8: DESCRIPTIVE ANALYTICS

CHAPTER 9: PREDICTIVE ANALYTICS

CHAPTER 10: PREDICTIVE ANALYSIS METHODS

CHAPTER 11: R – THE FUTURE IN DATA ANALYSIS SOFTWARE

CHAPTER 12: PREDICTIVE ANALYTICS & WHO USES IT

CHAPTER 13: DESCRIPTIVE AND PREDICTIVE ANALYSIS
CHAPTER 14: CRUCIAL FACTORS FOR DATA ANALYSIS
CHAPTER 15: EXPECTATIONS OF BUSINESS INTELLIGENCE

CHAPTER 16: WHAT IS DATA SCIENCE?

CHAPTER 17: DEEPER INSIGHTS ABOUT A DATA SCIENTIST’S
SKILLS

CHAPTER 18: BIG DATA AND THE FUTURE

CHAPTER 19: FINANCE AND BIG DATA

CHAPTER 20: MARKETERS PROFIT BY USING DATA SCIENCE

CHAPTER 21: USE OF BIG DATA BENEFITS IN MARKETING

CHAPTER 22: THE WAY THAT DATA SCIENCE IMPROVES TRAVEL

CHAPTER 23: HOW BIG DATA AND AGRICULTURE FEED PEOPLE

CHAPTER 25: THE USE OF BIG DATA IN THE PUBLIC SECTOR

CHAPTER 26: BIG DATA AND GAMING

CHAPTER 27: PRESCRIPTIVE ANALYTICS