Introduction To Call Centre Simulation Process Create Statistical Variables Required For Simulation Define Trajectories for Call Centre Departments Define Teams, Resources & Arrivals of Calls Run Call Centre Simulation & Store Results Plot Charts & Interpret Simulation Results

Welcome to the specialization course of Designing data-intensive applications. This course will be completed on four weeks, it will be supported with videos and exercises. By the end of this specialization, learners will be able to propose, design, justify and develop high reliable information systems according to type of data and volume of information, response time, type of processing and queries in order to support scalability, maintainability, security and reliability considering the last information technologies. Software to download: MySQL Workbench Rapidminer Hadoop framework Hortonworks MongoDB In case you have a Mac / IOS operating system you need to perform an action called VirtualBox.

Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance. To fill that need, this course is intended for learners who have little or no experience with R but who are looking for an introduction to this tool. By the end of this course, students will be able to import data into R, manipulate that data using tools from the popular tidyverse package, and make simple reports using R Markdown. The course is designed for students with good basic computing skills, but limited if any experience with programming.

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

In this 1.5 hour long project-based course, you will tackle a real-world prediction problem using machine learning. The dataset we are going to use comes from the U.S. Census Bureau; they recorded a number of attributes such as gender and occupation as well as the salary range for a sample of more than 32,000 Americans. We will fit a decision tree to this data, and try to predict the salary for a person we haven’t seen before. By the end of this project, you will have created a machine learning model using industry standard tools, including Python and sklearn. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this second MOOC in the Social Marketing Specialization - "The Importance of Listening" - you will go deep into the Big Data of social and gain a more complete picture of what can be learned from interactions on social sites. You will be amazed at just how much information can be extracted from a single post, picture, or video. In this MOOC, guest speakers from Social Gist, BroadReader, Lexalytics, Semantria, Radian6, and IBM's Bluemix and Social Media Analytics Tools (SMA) will join Professor Hlavac to take you through the full range of analytics tools and options available to you and how to get the most from them. The best part, most of them will be available to you through the MOOC for free! Those purchasing the MOOC will receive special tools, templates, and videos to enhance your learning experience. In completing this course you will develop a fuller understanding of the data and will be able to increase the effectiveness of your content strategy by making better decisions and spotting crises before they happen! MOOC 2 bonus content in the paid toolkit includes access to Semantria's analytics engine to extract some data on the markets you are developing and have it analyzed. As a student in this course, you are being provided the opportunity to access IBM Bluemix® platform-as-a-service trial for up to six months at no-charge with no credit card (up to a $1500 value). NOTE: By enrolling in this course, given access to IBM's Bluemix technology for one month for free as well as Lexalytics' Semantria tool. For those earning a Course Certificate, you will be given an additional five months of Bluemix and three months of Semantria at no cost with a special key code. By enrolling for a Course Certificate for this MOOC, you are acknowledging that your email will be shared with Lexalytics for the sole and express purpose of generating your individual key code. After the key code has been generated, Lexalytics will delete your email from its records. Additional MOOC 2 faculty include: * Steve Dodd (SVP Business Development, Effyis - dba BoardReader and Socialgist - Global Social Media Content Access) * Seth Redmore (CMO, Lexalytics, Inc.) * Chris Gruber (Social Media Analytics Solution Architect, IBM) * Russell Beardall (Cloud Architect, IBM) * Tom Collinger (Executive Director Spiegel Research Center and Senior Director Distance Learning, Medill Integrated Marketing Communications, Northwestern) * Tressie Lieberman (VP Digital Innovation, Taco Bell)

In less than one hour, you will learn how to connect to data, create key performance indicators, create sparkline charts, create a dashboard map, create dual axis charts and put it all together in a well-formatted and interactive dashboard. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will use GCP services to help graph trades, volume, and time delta from trade execution to see any patterns in the high volatility of the cryptocurrency market.

By the end of this project, you will learn about the concept of document similarity in textual analysis in R. You will know how to load and pre-process a data set of text documents by converting the data set into a corpus and document feature matrix. You will know how to calculate the cosine similarity between documents and explore and plot the output of your calculation.

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.