Data Science Blog

 These are the  pillars  of story telling 1. Audience   Learning about your primary audience members is essential to crafting a meaningful story. Use the persona template to establish the characteristics of your key audience groups. As you continue developing your story, revisit your audience profile to ensure that your messages are on point     2. SOCIAL STYLEs®   SOCIAL STYLE® is a measurement of observable behavior that explains how people perceive and are affected by others' behavior. The SOCIAL STYLEs® model organizes behavior into four types (Analytical, Amiable, Expressive and Driving). Leveraging this knowledge can help you tailor your presentations and stories when you know the people with whom you will be working.     Insights   Though it might be tempting to delve into an explanation of all of the insights you’ve developed, remember that your story should speak to the business need your audience wants to address. As you choose which insights y

You will make up your own names for your SAS datasets and variables. These names must conform to these rules: no longer than 8 characters, start with a letter, and contain only letters, numbers, or underscores (_). SAS is not case-sensitive. You can use capital or lowercase letters in your SAS variables. However, when you specify filenames (as you do with the include and file SAS commands), you must type it exactly as it exists in UNIX. The DATA step The data step is used to describe and modify your data. Within the data step you tell SAS how to read the data and generate or delete variables and observations. The data step transforms your raw data into a SAS dataset. There are four statements that are commonly used in the DATA Step DATA statement names the dataset INPUT statement lists names of the variables CARDS statement indicates that data lines immediately follow. INFILE statement indicates that data is in a file and the name of the file. Generally, the data s

Today, I was asked in an interview. What is web analytics? Tell me all you know about Web Analytics. This may not be a perfect answer, but gives a direction to answer the same. Web analytics is the measurement, collection, analysis and reporting ofweb data for purposes of understanding and optimizing web usage.[1][dead link] Web analytics is not just a tool for measuring web traffic but can be used as a tool for business and market research, and to assess and improve the effectiveness of a web site. Web analytics applications can also help companies measure the results of traditional print or broadcast advertising campaigns. It helps one to estimate how traffic to a website changes after the launch of a new advertising campaign. Web analytics provides information about the number of visitors to a website and the number of page views. It helps gauge traffic and popularity trends which is useful for market research. There are two categories of web analytics; off-site and on

5 Assumptions of linear regressions are 1. A Line Describes the Data: the relationship really is linear (or, for practical purposes, approximately linear over the range of the population being studied). 2. Homoscedasticity: the standard deviations of the residuals does not vary with the values of the explanatory variables.  In other words, the dispersion of the data around the regression line must be the same along the entire line. 3. Normally Distributed Residuals at a Given X: often difficult to ascertain because there usually isn't enough observations in MMA datasets with the same value of the explanatory variable to get a good look at the distribution of the residuals.  This is typically true, due to the Central Limit Theorem, since the residual term is the total of a myriad of other, unidentified explanatory variables. Typically, this assumption is assessed by examining a histogram of all of the residuals. It must be remembered though that this is not an assessment of the act

Building Block Terms:         Page, Page Views, Visits, Unique Visitors, New Visitor, Repeat Visitor, Repeat Visitor & Returning Visitor Visit Characterization:         Entry Page, Landing Page, Exit Page, Visit Duration, Referrer, Internal Referrer, External Referrer, Search Referrer, Visit Referrer, Original Referrer, Click-through, Click-through Rate/Ratio, Page Views per Visit Content Characterization:         Page Exit Ratio, Single-Page Visits, Single Page View Visits (Bounces), Bounce Rate Conversion Metrics:         Event, Conversion Here very briefly are the definitions (the real gold is in the comments that you see in the document for each definition, make sure you  download it  and read it carefully): Page:  A page is an analyst definable unit of content. Page Views:  The number of times a page (an analyst-definable unit of content) was viewed. Visits/Sessions:  A visit is an interaction, by an individual, with a website consisting of one or more requests

The course is suitable for those who are already working in analytics to enhance their knowledge as well as for those with analytical aptitude and would like to start new career in analytics.  The participants of Executive Education are expected to have at least 5 years of work experience. The programme will be conducted live in the classroom at IIMB. The sessions will be beamed instantaneously across selected cities in India through Reliance Web World outlets using video conferencing facilities that allow a large number of geographically dispersed participants to participate in highly interactive sessions with the faculty. For More Info: http://www.iimb.ernet.in/sites/default/files/u181/Business%20Analytics%20Batch%204%20_2_..pdf

In today's highly competitive online marketplace, marketers need to use every advantage they can get. Typical web analytics are available to anyone who takes the time to look at them. Even so, a large number of marketers don't get real value from their data. After all, looking at data is only wasted time if it doesn't result in actionable insight. That's where business intelligence (BI) software comes in. It can help you look at your data in new ways, and draw actionable insights that allow you to more effectively and efficiently allocate resources. BI software can distill large amounts of data so you can implement successful strategies based on the information. By monitoring the right key performance indicators (KPIs), businesses can see trends that would otherwise be hidden. Dashboards with the right key performance indicators can act like an alarm clock, notifying you when certain KPIs are under or over-performing. Additionally, they can help small businesses