About a month ago, I posted a blog on “Technical Deconstruction.” I described this as a technique to break down aggregate data to distinguish between its contributing parts: these parts might contain unique characteristics compared to the aggregate. For instance, I suggested that it can be helpful to break down data by workday - that is to say, maintaining separate data for each day of the week. I said that the data could be further deconstructed perhaps by time period and employee: the…Continue
Added by Don Philip Faithful on April 14, 2018 at 8:00am — No Comments
The term “technical analysis” usually refers to the study of stock prices. A technical analyst might use real-time or closing prices of stocks to predict future prices. This is an interesting concept because of what is normally excluded from the analysis - namely, everything except prices. Given that the approach doesn’t necessarily consider the health or profitability of the underlying companies, a purely technical approach seems to offer guidance that is disconnected from reality. Yet…Continue
Added by Don Philip Faithful on March 17, 2018 at 3:00am — No Comments
In general, any expression of performance that applies to a department can, if the data system is configured properly, be stated in relation to individual workers. For instance, if # of sales contracts / # of customer enquiries = success rate, the success rate can be given for the entire dealership and also for each sales agent in that dealership. Due to the differences in performance between agents, it can be problematic to only make use of the aggregate. Some agents might be blamed for…Continue
Added by Don Philip Faithful on February 25, 2018 at 7:30am — No Comments
Time series forecasting is hardly a new problem in data science and statistics. The term is self-explanatory and has been on business analysts’ agenda for decades now: The very first practices of time series analysis and forecasting trace back to the early 1920s.
The underlying idea of time series forecasting is to look at historical data from the time perspective, define the patterns, and yield short or long-term predictions on how – considering the captured patterns – target…Continue
Added by Olexander Kolisnykov on February 14, 2018 at 1:58am — No Comments
I shared my story in a few blogs about returning to university to do a graduate degree. In my first class, I found myself being asked to define “ontology.” It was a course on the Geography of Disability. I returned to class the following week with some details. I said that strangely enough, this is not a word that can be found in all of my dictionaries. One dictionary listed “oncology,” which I believe is the study of cancerous tumours. My Collins Cobuild dictionary says, “Ontology is…Continue
Added by Don Philip Faithful on February 11, 2018 at 7:30am — No Comments
I have written in the past about the difference between market demand and operational capacity - and how difficult it is to determine what exactly is being measured in relation to either. Has the demand for a product declined, or is the organization simply less capable of satisfying it? For example, the fact there are no bananas in the grocery store does not mean that there is no demand for bananas; but the absence of revenues from the sale of bananas might be regarded, rather erroneously,…Continue
Added by Don Philip Faithful on December 9, 2017 at 10:30am — No Comments
Big Data tools not only simplify lengthy analytical procedures in any industry, but they also provide a competitive advantage to banks. With new regulations, banks are looking at ways to make compliance procedures more effective and accurate. Big Data in banking is slowly gaining momentum and becoming an inevitable necessity across the banking industry. As…Continue
Added by Deena Zaidi on November 2, 2017 at 11:00am — No Comments
Missing data present significant challenges to trend analysis of time series. Straightforward approaches consisting of supplementing missing data with constant or zero values or with linear trends can severely degrade the quality of the trend analysis, which significantly reduces the reliability of the trend analysis. …Continue
Added by Ted on October 31, 2017 at 7:30pm — No Comments
Added by Jeefri A. Moka on October 5, 2017 at 8:01pm — No Comments
Text analytics can be a bit overwhelming and frustrating at times with the unstructured and noisy nature of textual data and the vast amount of information available. "Text Analytics with Python" published by Apress\Springer, is a book packed with 385 pages of useful information based on techniques, algorithms,…Continue
Added by Dipanjan Sarkar on July 14, 2017 at 4:00am — No Comments
On the basis of regional analysis, the market is segmented into North America, Europe, Asia-Pacific and Rest of the World. North America region is generating highest market share in Core Banking Solutions Market owing to higher technology implementation. The implementation of core banking solution software by both small and medium enterprises is increasing rapidly. In US region, the implementation of core banking solution software in BFSI sector accounted for…Continue
Added by Sagar kadam on June 26, 2017 at 11:30pm — No Comments
During my childhood, our school librarian said that I was invited to attend a conference of writers. I felt honoured and privileged. I asked what the writers intended to ask me. She smiled and said that actually I would be asking the writers questions. Not quite sure why I would ask these people anything and why their thoughts would matter, I nodded anyways and at some point attended the most boring event imaginable for a young child. I thought I had died, I really did. I sat there…Continue
Added by Don Philip Faithful on May 7, 2017 at 6:00am — No Comments
The entire polling industry faced an existential crisis on the grim morning of November 9, 2016. The morning before, on Election Day, nearly every mainstream media outlet, from the New…Continue
Like it or not, data-driven artificial intelligence algorithms and other high-tech robotic applications are coming to fill our jobs. An analysis by PwC estimated that up to 38 percent of current American jobs could be taken over by machines within the next 15 years.
Even white-collar jobs aren’t safe, since algorithms are capable of governing sophisticated tasks for machines in ways that previously were unthinkable, such as writing or distributing pharmaceuticals. The transition has…
Multicollinearity (Collinearity) is not a new term especially when dealing with multiple regression models. This phenomenon of relationship in between one response variable with the set of predictor variables also include models like classification and regression trees as well as neural networks. Collinearity is infamously famous for inflating the variance of at least one estimated regression coefficient, which can cause the model to predict erroneously and in a business setup it can have an…Continue
Added by Sunil Kappal on March 6, 2017 at 10:00am — No Comments
Linear Model better known as linear regression is one of the most common and flexible analysis framework to identify relationship between two or more variables. The widely used linear model is represented by drawing the best fit line through a series of data points represented on a scatter plot.
For any budding business analyst this should be the starting point to understand how model works at the very core of its design.
Selecting the Variables in Deducer…Continue
Added by Sunil Kappal on February 28, 2017 at 7:00am — No Comments
As we all know CRISP DM stands for Cross Industry Standard Process for Data Mining is a process model that outlines the most common approach to tackle data driven problems. Per the poll conducted by KDNuggets in 2014 this was and “is” one of the most popular and widest used methodology. This method of gleaning insights out of the data is very dear to the industry experts and data miners.
As the title suggest I will align some of the most useful R packages with this most popular and…Continue
Six Sigma is a quantitative approach to problem solving - to solve certain types of problems. At the root of Six Sigma is an improvement methodology that can be described by the acronym DMAIC: define, measure, analyze, improve, and control . Those interested in reading up on Six Sigma might consider the book for dummies, which I found fairly succinct. Those wondering what I mean by "certain types of problems" should consider how to apply the approach to their own business circumstances. I…Continue
As per the largest market research firm MarketsandMarkets the speech analytics industry will grow to USD 1.60 billion by 2020 at a Compound Annual Growth Rate (CAGR) of 22% from 2015 to 2020. Today the omnichannel world consists of voice, email, chat, social channels, and surveys, and each channel has its own importance.
Therefore, it becomes inevitable for any customer centric organization to ignore the information that can be glean…Continue