Decision Trees

Decision Trees

Decision trees are, in general, a non-parametric inductive learning technique, able to produce classifiers for a given problem which can assess new, unseen situations and/or reveal the mechanisms driving a problem. They can be applied to both regression and classification problems.

Decision trees can be easy-to-understand with intuitively clear rules understandable to domain experts

Decision trees offer the ability to track and evaluate every step in the decision-making process. This is because each path through a tree consists of a combination of attributes which work together to distinguish between classes. This simplicity gives useful insights into the inner workings of the method.

Decision trees can handle both nominal and numeric input attributes and are capable of handling data sets that contain misclassified values

Decision trees can easily be programmed for use in real time systems.

They are relatively inexpensive computationally and work well on both large and small data sets

Decision trees are considered to be a non-parametric method. This means that decision trees have no assumptions about the space distribution and on the classifier structure

Validate Data in EXCEL using Validation List

Excel enables you to restrict the values a user can enter in a cell. By restricting values, you ensure that your worksheet entries are valid and that calculations based on them thereby are valid as well. During data entry, a validation list forces anyone using your worksheet to select a value from a drop-down menu rather than typing it and potentially typing the wrong information. In this way, validation lists save time and reduce errors.

To create a validation list, type the values you want to include into adjacent cells in a column or row. You may want to name the range. After you type your values, use the Data Validation dialog box to assign values to your validation list. Then copy and paste your validation list into the appropriate cells by using the Paste Special Validation option. You may want to place your validation list in an out of the way place on your worksheet or on a separate worksheet

Validation lists can consist of numbers, names of regions, employees, products, and so on.

Vamsidhar

www.pacegurus.com

Teaching Statistics

Being able to provide solid evidence-based arguments and critically evaluate claims based on data are important skills that all citizens should have. Hence the study of statistics worldwide at all educational levels is gaining more attention. The study of statistics provides students with tools, ideas and dispositions to react intelligently to information in the world around them. Reflecting this need to improve students’ ability to think statistically, statistical literacy and reasoning are becoming part of the mainstream school and university curricula in many countries. As a consequence, statistics education is becoming a thriving field of research and curricular development.

Statistics is vigorously gaining importance and recognition in today’s society thanks to the boom created by the buzz words Big Data and Analytics. Statistics is a central tool in moving science, economics, politics, schools, and universities forward. Quantitative information is omnipresent in media and in the everyday lives of citizens worldwide. Data are increasingly used to add credibility to advertisements, arguments, or personal and professional advice. Therefore, there is a growing public and policy consensus that being able to provide reliable and persuasive evidence-based arguments and critically evaluate data-based inferences are crucial skills that all citizens of the twenty-first century should have. All students consequently must become statistically literate and be able to reason statistically even at an informal level as part of their compulsory and lifelong education

Despite the increasing awareness of the importance of statistical literacy, statistics has been viewed by many students as difficult and unpleasant to learn. Many university instructors find statistics and research methods courses equally frustrating and unrewarding to teach. In schools, mathematics teachers often view statistics as a marginal strand in the mathematics curriculum and therefore minimize or ignore its teaching. Students equate statistics with mathematics and expect the focus to be on numbers, computations, formulas, and one right answer. They are uncomfortable with the messiness of data, the different possible interpretations based on different assumptions, and the extensive use of writing and communication skills. The dissatisfaction with students’ ability to think and reason statistically, even after formally studying statistics at the undergraduate and graduate level should lead to a re-examination of the field of statistics education

Many students still leave their course perceiving statistics as a set of tools and techniques that are soon forgotten. Even current methods of teaching continue to focus on the development of skills and have neglected to instill in their courses experiences that develop the ability to think statistically

There is a need for data driven, innovative approaches in teaching statistics keeping in mind the objective of statistics in the real world. This subject should be comfortable even to a students who fears mathematics because this subject is all about working with relationships and the use of technology simplifies the process further. All the statistics and business analytics courses designed by www.pacegurus.com keep in mind a student with very little knowledge of statistics, but has the attitude to learn, decipher from the data and make meaningful conclusions needed for the aspects of life. 

Bottom Line: Statistics is much much bigger than just a few calculations. It helps you in every walk of life. Don’t compare it with Maths and develop fear and allergy towards it. Start enjoying it……

Vamsidhar

www.pacegurus.com

Intermediate Statistics using SPSS - Workshop

Intermediate Statistics using SPSS - Workshop

www.pacegurus.com announces a 2-Day workshop on "Understanding Credit Derivatives" for the Corporate, Business Schools, any other learning institutions. Even individuals can get enrolled for one-one learning of the subject. The program would be conducted by VAMSIDHAR AMBATIPUDI at your campus. The detailed syllabus for the same can be found at

http://www.pacegurus.com/Intermediate-Statistics-SPSS.html

For further details do contact us at +91-9848012123. Request you to share the information with your friends and colleagues and do recommend PACEgurus for Finance, Investment, Actuaries, Risk Management, Statistics and Business Analytics trainings for your organization.

Integrating Strategy and Analytics

Integrating Strategy and Analytics

“The vast majority of strategic initiatives never succeed.”

“Organizations fail not because they have the wrong strategy but because they do not execute the strategy properly.”

Statements like these have been repeated so often they have become conventional wisdom. An entire industry of management consultants exists to help companies define the right strategy so that they won’t fail. Yet failure, or at least muddling through, is still much, much more common than outright success. Through analysis you can determine where execution works well and where it does not. With that information you can evaluate the solutions, determine which one(s) to implement

Organizations are complex places. Cognitively, people have a hard time focusing on too many things at once. A primary vehicle for reduced complexity is standard business processes. Unfortunately, simplification and standard processes are not enough. You have to recognize and manage the organizational complexity for successful strategy execution. The challenge for frontline and middle managers is how to deal with the competing strategic and operational objectives

Most analytics conducted today by the business and by HR are incomplete and cannot solve strategy execution problems on their own. You need a full causal model to diagnose the entire system and to understand what really drives behavior and performance. You need to know what drives performance in your organization to get strategy execution right. The problem with organizational analytics today is that they tell an incomplete story. Enterprise analytics and human capital analytics are conducted along parallel and separate tracks. Both attempt to determine why performance happens, yet each on its own can tell only part of the story. Without the complete story, we don’t really know the best ways to improve strategy execution and organizational effectiveness.

When you understand the link to the larger system, you can properly diagnose the root causes of behavior and motivation. It’s also the surest way to find changes to solve the problems instead of temporary solutions that only paper over the root causes

VAMSIDHAR AMBATIPUDI
www.pacegurus.com

Understanding Credit Derivatives - Workshop

Understanding Credit Derivatives - Workshop

www.pacegurus.com announces a 2-Day workshop on "Understanding Credit Derivatives" for the Corporate, Business Schools, any other learning institutions. Even individuals can get enrolled for one-one learning of the subject. The program would be conducted by VAMSIDHAR AMBATIPUDI at your campus. The detailed syllabus for the same can be found at

http://www.pacegurus.com/Understanding-Credit-Derivatives.html

For further details do contact us at +91-9848012123. Request you to share the information with your friends and colleagues and do recommend PACEgurus for Finance, Investment, Actuaries, Risk Management, Statistics and Business Analytics trainings for your organization.

Flexible Worksheet Consolidation

Though Excel provides built-in functionality to consolidate worksheets, it is a bit overly complex for most situations. This solution consolidate worksheets, without using Excel’s consolidation function, and also gives users more flexibility to change what is being consolidated. By using this approach to consolidate worksheets, we can instantly modify the composition of our consolidation by either adding or removing worksheets from the consolidated total

The one limitation to this approach is that each worksheet needs to be identically structured relative to the items that are going to be consolidated. Generally this requirement is not particularly onerous given the nature of worksheet consolidation. In addition to the line of business-level worksheets, we will need one worksheet that sums all the lines of business as a consolidated financial statement

Application

To illustrate, assume that we have a business consisting of three lines of business (LOB) titled LOB1, LOB2 and LOB3. Each LOB's P&L and balance sheet is additive to the consolidated financial statement. In our example, the leadership team is considering adding a fourth LOB and wants to be able to easily consolidate or exclude the fourth LOB, to better analyze LOB4’s impact on the consolidated financial statements. This will require five worksheets, one for each of the four LOBs and one for the consolidated business. We start by creating a template for our financial statement with the years going across the columns. Each of the LOBs and consolidation worksheets need to be structured the same, for all cells to be consolidated.

You could have consolidated as follows

2015 Consolidated Revenue = LOB1!F13 + LOB2!F13 + LOB3!F13 + LOB4!F13

The issue that arises is when you want to remove one of the LOBs from the consolidation, which means that every formula referencing LOB4 needs to be edited.

In order to make use of the flexible consolidation approach you will need to add two extra worksheets that establish the range. We usually name the two worksheets “Start” and “End”. We insert the individual LOB worksheets between them. So there are 6 sheets now (one for each LOB and one start and one end). The consolidation worksheet will be outside the start and end sheets.
Now the new formula in consolidation worksheet is Formula: =SUM(Start:End!F13)

Now, if we are asked to display the consolidation without LOB4, we simply drag the LOB4 worksheet beyond the “Start” and “End” bookend range. This approach will allow us to create some very sophisticated reporting and analysis spread sheets, while permitting addition and deletion of worksheets within our consolidation, with much less formula editing

The one caveat to this approach is that the bookend worksheets (Start and End) are part of the SUM function, which means those worksheets must be blank; otherwise the SUM function will include any data on those two worksheets

Vamsidhar Ambatipudi​
www.pacegurus.com

Manipulating Text Strings in EXCEL

An issue that we frequently come across with our clients is users that are a little overwhelmed when they need to rearrange text strings differently than from the way that the text string was imported from a source system. As long as the text string is delimited, such as with a coma, asterisk, blank space, or colon we can use Excel’s different text functions to get the text string parsed as required. We can break the full name into first name, middle name and last name or any other forms of manipulations quite comfortably and quite DYNAMICALLY if we know different formulas which can manipulate text data

There are five primary text functions that users need to learn, which are LEFT(), RIGHT(), FIND(), LEN() and MID().

LEFT() – This has only two possible parameters: LEFT(source, # characters). The source is the text cell to be parsed, and the # characters are the number of characters we want returned beginning from the left most character

RIGHT() – This works exactly the same as the LEFT(), just beginning from the opposite side of the text string

FIND() – Like the previous two functions, this function only requires two parameters: FIND(character(s) to be found, source string). The first parameter needs to be enclosed with double quotes and represents the string to be found. The second parameter is the cell address of the text string to be searched. The FIND() function will return the position number, within our string, of the first occurrence of the character(s) being sought

LEN() – The LEN() function returns the length of the referenced text string

MID() – The MID function has three required parameters: MID(source, starting position, number of characters). The MID() function will extract a number of characters from within a text string

Role of Analytics in innovation

Analytics plays a crucial role in modern corporate innovation. The outcomes from analytical models are used to drive new sales processes, to change customer experiences in order to avoid churn, and to identify triggers detecting fraud, risk, or any sort of corporate threat, as well as many other business issues. The knowledge from analytical models is commonly assigned to recognize customer Behavior, to predict an event, or to assess the relationship between events, impacting company actions and activities.

Analytics has three stages.

Stage 1 provides long-term informational insight, helping organizations analyse trends and forecast business scenarios. Data warehouse, data marts, and interactive visual analysis usually support this stage one purpose. Focus is towards identifying trends, historical event patterns, and business scenarios. This analysis is concerned with presenting information about past sales by region, branches, and products and, of course, changes that have occurred over time.

Stage 2 of Analytics maps out the internal and external environments that impact the market considerations, the customers’ Behavior and the competitor’s actions, as well as details about the products and services that the organization offers. How profitable are my products/services? How well have they been adopted by the target audience? How well do they suit the customer’s need? Statistical analyses support these tasks, with correlations, and association statistics methods.

Stage 3 focus is driven by to the company’s strategy. Model development is directed by core business issues such as cross sell, churn, fraud, and risk, and models are also deployed and used once the results are derived. Data mining models that use artificial intelligence or statistics commonly support these types of endeavours. Models are deployed to classify and predict some particular event and to recognize groups of similar Behavior within the customer base for subscribed business change

The three layers of analytics provide a foundation for data-driven innovation, both creating and delivering new knowledge and accessible information. In innovative organizations, access to analytically based answers is fundamental throughout the company. Data is seen as a corporate asset and analytical methods become intellectual property.

Innovation is a wonderful process. It continually evolves, allowing companies to remain ahead of competitors, ahead in the market, and ahead of its time. However, innovation has a price - intangible price—and maybe even a higher price than we could imagine. Innovation demands companies stay at the pinnacle of available technology and be on the leading edge of new business actions. But even more than this, innovation requires people to change their minds.

Innovation demands change. We must take a chance and address a particular situation and put into place something that may never have been tried before. Innovation in the context of new ideas means to try, and sometimes get it right and make things better, and sometimes not. Therefore, innovation is a trial-and-error process, and as such it is also a heuristic process.

Everything changes. The market changes, the consumer changes, the technology changes, and thus products and services must change as well. Analytical models raise the business knowledge regarding what has changed and what needs to change. The new knowledge delivered by analytics is about the company itself, the competitive environment, and the market, but mostly it is about the consumers/ constituents that the company serves

Analytics is geared toward understanding the average, to accurately forecast for the majority, to target most of the population at hand. What companies, analysts, and data miners need to bear in mind is how heuristic this process can be and, as a result, how they need to monitor and maintain all analytical models to reflect changing conditions.

IBM SPSS for Analytics

IBM SPSS for Analytics

IBM SPSS is a wonderful statistical analysis package. We can perform complex statistical analysis on research data in a few minutes which would have been impossible earlier without expert help and an enormous amount of time. SPSS allows us to undertake a wide range of statistical analyses relatively easily. However, we do need to know what analysis is appropriate to the data we have. So a certain amount of basic statistical knowledge is required before using SPSS.

In my experience as a teacher, statistics consultant and adviser to students, I saw many people experience some confusion when they first encounter the computer output from statistical applications. They often ask questions such as: Why so many tables? What do they mean? Which is my result? Is it significant? This is because the statistical applications print out a range of useful information with each analysis. Not all of this information is readily understandable to a new user. I have seen students on a daily basis who simply want a clear explanation of the SPSS output they have produced at a level they can understand.

There are two types of explanations: ones for the novice and ones for the expert. Often we hear a technical explanation and cannot understand it. We may even be tempted to ask: Can you say that again in English? This is one of the difficulties of learning statistics and understanding the output from statistical applications. The terminology may not be readily understandable. In many subject areas there are technical definitions that are not used in everyday communication. The same is true of statistics, with terms such as ‘general linear model’, ‘homogeneity of variance’ or ‘uni-variate analysis’. But the business meaning behind each of these terms could be very simple and understandable.

www.pacegurus.com provides training to corporate, B-Schools and research scholars on the key statistical tests, describe how to undertake them and explain the output produced by SPSS for these tests. The program will be rendered in a way that even a person with little or no statistical background can use SPSS for his/her research in a very effective manner. You can recommend me (VAMSIDHAR AMBATIPUDI) for your organization/College/Institution so that you can be trained in a simple and useful manner . You can call us on +91-9848012123 for further communication

Analytics vs. Analysis

Analysis and analytics are similar-sounding terms, but they are not the same thing. They do have some differences

Analysis is to understand the status quo that may reflect the result of their efforts to achieve certain objectives whereas Analytics to identify specific trends or patterns in the data under analysis so that they can predict or forecast the future outcomes or behaviours based on the past trends. Analysis can be defined as the process of dissecting past gathered data into pieces so that the current (prevailing) situation can be Understood. Analytics can be defined as a method to use the results of analysis to better predict customer or stakeholder behaviours

Even the dictionary meanings stress that Analysis is the separation of a whole into its component parts to learn about those parts whereas Analytics is the method of logical analysis

Analysis presents a historical view of the project performance as of the time of analysis. Analytics look forward to project the future or predict an outcome based on the past performance as of the time of analysis. BI Tools and SQL etc. are used heavily in analysis whereas Statistical, mathematical, computer science, sophisticated predictive analytics software tools are the base for Analytics. 

Forecasting - Role in Decision Making

Forecasting vs. Decision Making

Virtually every organization, public or private, operates in an uncertain and dynamic environment with imperfect knowledge of the future. Forecasting is an integral part of the planning and control system, and organizations need a forecasting procedure that allows them to predict the future effectively and in a timely fashion. Forecasting can be used as a tool to guide business decisions, even though some level of uncertainty may still exist. It can reduce the range of uncertainty surrounding a business decision

Applications of Forecasting in Business

Forecasting is a powerful tool that can be used in every functional area of business.

1. Production managers use forecasting to guide their production strategy and inventory control
2. Trends and availability of material, labor, and plant capacity play a critical role in the production process
3. Reliable forecasts about the market size and characteristics are used in making choices on marketing strategy and advertising plans and expenditures
4. Marketers use both qualitative and quantitative approaches in making their forecasts
5. Service sector industries such as financial institutions, airlines, hotels, hospitals, sport and other entertainment organizations all can benefit from good forecasts
6. Financial forecasting allows the financial manager to anticipate events before they occur, particularly the need for raising funds externally
7. Use of forecasts in human resource departments is also critical when making decisions regarding the total number of employees a firm needs

Forecasting as a tool in planning has received a great deal of attention in recent decades. Today, firms have a wide range of forecasting methodologies at their disposal ranging from intuitive forecasting to highly sophisticated quantitative methods. 

Sports Analytics

Management of structured historical data, application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers (personnel executives, coaches, trainers, etc. and enable them to help their organizations in gaining a competitive advantage on the field of play. Understanding the tools of sports analytics is important to create a competitive advantage.

Goals

1. Save the decision maker time by making all of the relevant information for evaluating players or teams or prospects efficiently available
2. Provide decision makers with novel insight. Analytic models allow decision makers to gain insight into teams and players that are not possible without advanced statistical analysis. Analytic models have many uses, but their core function is to turn raw data into reliable and actionable information.

Many teams across sports use analytic models to aid in their selection of players. Differences in player performances are the result of a variety of factors, such as team mates, system, opponents, and the player’s ability to perform

Now that leagues exist for almost all sports, it is essential to understand sports analytics and use it for winning.