This course prepares participants to review, analyze, and make decisions based on results from business intelligence projects. The course covers reading and interpreting regression analysis, and gives participants the skills to critically analyze and identify potential limitations on analysis. The course also explores how to predict changes in business outcomes based on analysis and identifying the level of certainty or confidence around those predictions. This paves the way for future detailed courses in predictive analytics. If you would like Continuing Education Credit (e.g. CPE, CE, CPD, etc.) for this course, it is available if you take this course on the Illumeo dot com platform under course title: Business Intelligence – Applying and Using Data Analysis . Illumeo is certified to provide CPE in over two dozen different professional certifications covering finance, accounting, treasury, internal audit, HR, and more. However, in order to receive CPE credit the courses must be taken on an ‘approved-by-the-governing-body’ CPE platform, and for over two dozen corporate professional certifications, that is the Illumeo platform. Go to Illumeo dot com to learn more.

The purpose of this course is to summarize new directions in Chinese history and social science produced by the creation and analysis of big historical datasets based on newly opened Chinese archival holdings, and to organize this knowledge in a framework that encourages learning about China in comparative perspective. Our course demonstrates how a new scholarship of discovery is redefining what is singular about modern China and modern Chinese history. Current understandings of human history and social theory are based largely on Western experience or on non-Western experience seen through a Western lens. This course offers alternative perspectives derived from Chinese experience over the last three centuries. We present specific case studies of this new scholarship of discovery divided into two stand-alone parts, which means that students can take any part without prior or subsequent attendance of the other part. Part 1 (this course) focuses on comparative inequality and opportunity and addresses two related questions ‘Who rises to the top?’ and ‘Who gets what?’. Part 2 (https://www.coursera.org/learn/understanding-china-history-part-2) turns to an arguably even more important question ‘Who are we?’ as seen through the framework of comparative population behavior - mortality, marriage, and reproduction – and their interaction with economic conditions and human values. We do so because mortality and reproduction are fundamental and universal, because they differ historically just as radically between China and the West as patterns of inequality and opportunity, and because these differences demonstrate the mutability of human behavior and values. Course Overview video: https://youtu.be/dzUPRyJ4ETk

Everyone is talking about big data and GIS, but is anyone really doing it? In this course you’ll work with gigabytes of data to solve many different spatial and data related questions . All the software is free, but don't let that fool you: we'll be using the most effective open source products like Postgres and QGIS, and we'll even perform parallel processing with Manifold Viewer - I hope you have a multi-core computer to see how fast this stuff is! At the end of the course, you’ll understand: the principles behind big data geo-analytics and the role of statistics, databases, parallel processing, and hardware and software in support of big data geo-analytics. how to use open source software and Manifold GIS to perform parallel processing and manage spatial data. how to conduct a big data geo-analytics project by interrogating multi-gigabyte real world databases. And best of all, the software we use in this class is FREE and easy to set up - you'll do it all yourself! The course is taught by Dr. Arthur Lembo who is a Professor at Salisbury University and has worked in the GIS field for  over 30 years.

Ever felt that you’re missing out on the data race? Don’t know the difference between a variable and a vegetable? Then our Analytics for Beginners course is just for you! What is this course about? The motive of the course is to introduce absolute beginners, or novices to the fundamentals of analytics by using examples from daily life. Using a combination of real-life stories, interviews with analytics experts and journalistic articles presented in a breezy narrative style, Analytics for Beginners will ensure that you will never look at numbers and data in the same way again. How will you do that? By understanding how companies like Netflix, Amazon, Facebook or even great individuals like Archimedes or Florence Nightingale used analytics in their chosen spheres of work, the course will convey the relevance and importance of using analysis in everyday life. In terms of prior requirements, the viewer will only need a working knowledge of Excel, with logical and reasoning skills. A basic understanding of regression is also covered in the course. Mathematics skills are not required! Who is this course for? Are you a working professional or a stay-at-home mom? A young go-getter or an old has-been? A small business owner or a multi million dollar corporate hi-flier? The point is that none of it matters - because whoever you are and whatever you do, analytics can help you do it better! Why should I do this course? We’re not going to pretend that you can run logistic regressions and crack warehousing solutions at the end of this course. But what this course can do is open your eyes to the wonderful world of analytics opportunities and introduce you to some of its career possibilities. Do you use a case study? Keeping with the light-hearted tone of the course, the over-arching case study covered is based on the game of cricket. You can read up more about how the game is played, on its Wikipedia entry. The case study seeks to determine who is the best cricketer amongst Indian cricketing greats - Sachin Tendulkar, Rahul Dravid and Saurav Ganguly by using regression analysis. This ensures the overall flavor of the course remains light and fun, even if the analytics does require a little data work.

If you want to learn simple, effective techniques to create impactful Excel Dashboards and Data Analysis, this course is ideal. Updated in 2021 with full high definition video , this course teaches you practical skills you can apply in your work environment immediately. Just take a look at the recent comments and reviews! "Presentation is great! Very practical solutions for everyday business analysis challenges." "I walked away with a lot much practical knowledge. Really excited to apply this knowledge in my work. I have taken other courses from Ian and never disappointed." "One of the best courses I have taken on Udemy!!! Very easy to understand, not long and drawn out and focuses on what is relevant in the work place. Will definitely recommend it to my team." "Really enjoyed the course, so many practical tips, which are easily explained. Thanks" In this course, you will learn the BEST techniques and tools for turning data into MEANINGFUL analysis using Excel This course is lead by Ian Littlejohn - an international trainer, consultant and data analyst with over 125 000 enrollments & 100 000 students on Udemy. Ian specializes in teaching data analysis techniques, Excel Pivot Tables, Power Pivot, Microsoft Power BI, Google Data Studio & Amazon Quicksight & his courses average over 4.5 stars out of 5. **** Life-time access to course materials and practice activities.  **** The Complete Introduction to Business Data Analysis teaches you how to apply different methods of data analysis to turn your data into new insight and intelligence . The ability to ask questions of your data is a powerful competitive advantage , resulting in new income streams, better decision making and improved productivity.  A recent McKinsey Consulting report has identified that data analysis is one of the most important skills required in the American economy at the current time. During the course you will understand why the form of analysis is important and also provide examples of analysis using Excel. The following methods of analysis are included: Key Metrics Comparison Analysis Trend Analysis Ranking Analysis Interactive Dashboards Contribution Analysis Variance Analysis Pareto Analysis Frequency Analysis Correlations The Complete Introduction to Business Data Analysis is designed for all business professionals who want to take their ability to turn data into information to the next level . If you are an Excel user then you will want to learn the easy-to-use techniques that are taught in this course. This course is presented using Excel in Office 365.  However, the course is also suitable for: Excel 2013 Excel 2016 Excel 2019 Please note that this course does not include any complicated formulas, VBA or macros.  The course utilizes drag and drop techniques to create the majority of the different data analysis techniques.

The Problem Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist. And how can you do that? Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming) Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture The Solution Data science is a multidisciplinary field. It encompasses a wide range of topics. Understanding of the data science field and the type of analysis carried out Mathematics Statistics Python Applying advanced statistical techniques in Python Data Visualization Machine Learning Deep Learning Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is. So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2021. We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place. Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save). The Skills 1. Intro to Data and Data Science Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean? Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science. 2. Mathematics Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail. We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on. Why learn it? Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal. 3. Statistics You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist. Why learn it? This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist. 4. Python Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning. Why learn it? When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language. 5. Tableau Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science. Why learn it? A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers. 6. Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail. Why learn it? Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section. 7. Machine Learning The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow. Why learn it? Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines. ***What you get*** A $1250 data science training program Active Q&A support All the knowledge to get hired as a data scientist A community of data science learners A certificate of completion Access to future updates Solve real-life business cases that will get you the job You will become a data scientist from scratch We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it. Why wait? Every day is a missed opportunity. Click the “Buy Now” button and become a part of our data scientist program today.

PLEASE READ BEFORE ENROLLING: 1.) THERE IS AN UPDATED VERSION OF THIS COURSE: "PYTHON FOR DATA SCIENCE AND MACHINE LEARNING BOOTCAMP" 2.) IF YOU ARE A COMPLETE BEGINNER IN PYTHON-CHECK OUT MY OTHER COURSE "COMPLETE PYTHON MASTERCLASS JOURNEY"! CLICK ON MY PROFILE TO FIND IT. (PLEASE WATCH THE FIRST PROMO VIDEO ON THIS PAGE FOR MORE INFO) ********************************************************************************************************** This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science! You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data. You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers! By the end of this course you will: - Have an understanding of how to program in Python. - Know how to create and manipulate arrays using numpy and Python. - Know how to use pandas to create and analyze data sets. - Know how to use matplotlib and seaborn libraries to create beautiful data visualization. - Have an amazing portfolio of example python data analysis projects! - Have an understanding of Machine Learning and SciKit Learn! With 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science!

This is an introductory course designed to help business professionals and others learn predictive analytic skills that can be applied in a business setting. Since it is designed for business professionals it doesn't delve too deeply into the mathematics of the statistical models. We do the following case studies on Rapidminer software: B2B Churn of an office supply distributor, Market Basket Analysis of a retail computer store, Customer Segmentation of a customer database and Direct Marketing. The following models are used: Linear Regression, Logistic Regression, Association Rules, K-means Clustering and Decision Trees. Through these practical case studies we generate actionable business insights!

Data analysis is critical in business. Get ahead in your career with this important skill. Management depends on decision making and problem solving.   They depend on analytical findings. Not only do we need good sources of data, but we need skills that allow us to interpret and report the results. Discover techniques and best practices for analysis by learning the analytical process.

COURSE ABSTRACT This course aims to provide a comprehensive introduction to the SAS analytic software for Windows. Through a mixture of lectures and in-class examples, quizzes, and take-home assignments, students will gain experience using the SAS system for data manipulation, management and analysis. You will also expect A LOT of extracurricular learning materials for self-pace learning, treat it as a BONUS! Emphasis will be placed on the skills and techniques necessary for efficient data manipulation, management and analysis. It is designed for students with little to no background with SAS, and an understanding of the basic statistical concepts. This will be an excellent choice for your first SAS introduction course for your data analysis career. Plus, you will get a FREE course - SAS Data Issue Handling and Good Programming Practice (check out in the bonus lecture)!!! WHAT DO I EXPECT? A comprehensive course design from SAS basics to statistical analysis Many in-class examples, exercises and take-home assignment Master various techniques for data importing Solid understanding of variable attributes, and learn various character/numeric functions IF-THEN/ELSE statements Do loop and counter variables Master DATA step with Concatenation, Merge, etc. Exposed to several useful PROC step (PRINT, SORT, TRANSPOSE, etc.). Descriptive statistics procedures (MEANS, UNIVARIATE, FREQ) Hypothesis testing (UNIVARIATE, TTEST, ANOVA) Correlations (CORR) Regression (REG) PREREQUISITE COURSES AND KNOWLEDGE: No SAS background required; Basic knowledge of statistics is preferred.