Microsoft Excel is the world’s most-used business intelligence tool. Its knowledge is even compulsory for an MBA degree and the business world depends greatly on it. Microsoft excel and business analysis for professionals is intended for Sales Managers, Financial Analysts, Business Analysts, Data Analysts, MIS Analysts, HR Executives and frequent Excel users.
It is written by Michael Olafusi a two-time Microsoft Excel MVP (most valuable professional) and a full-time Microsoft Excel consultant. He is the founder of UrBizEdge, a business data analysis and Microsoft Excel consulting firm.
He has trained hundreds of business professionals on Microsoft Excel and has used the experience gained from interacting with them both during such training and while consulting for companies to write this excellent guide for the busy professional who needs the improved work productivity Microsoft Excel provides.
Contents Preface Microsoft Excel: It’s more powerful and easier to… How Excel Handles What You Type Data Consistency, starting with the end in view Building Datasheets that can easily scale Sorting Filtering Data Cleaning Data Formatting Charts PivotTable and PivotChart Business Data Analysis Power Excel Formulas Named Range, Goal Seek and Scenario Manager Introduction To Excel VBA (macros)
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.
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.
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 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.
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
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.
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.
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.
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.
1. Normal or Gaussian distribution
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.
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
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
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
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
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.
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.
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.