online courses Archives - Page 10 of 16 - DexLab Analytics | Big Data Hadoop SAS R Analytics Predictive Modeling & Excel VBA

Category: online courses

ANZ uses R programming for Credit Risk Analysis

Posted on by Dexlab

ANZ uses R programming for Credit Risk Analysis

At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.

In the slides he made, one can see the following:

How R is used to fit models for mortgage loss at ANZ

A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.

One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.

A comparison in between R and SAS for fitting such non-standard models

Mr. Ooi also notes that SAS does contain various options for modelling variance like for instance, SAS PROC MIXED, PRIC NLIN. However, none of these are as flexible or powerful as R. The main difference as per Ooi, is that R modelling functions return as object as opposed to returning with a mere textual output. This however, can be later modified and manipulated with to adapt to a new modelling situation and generate summaries, predictions and more. An R programmer can do this manipulation.

 

Read Also: From dreams to reality: a vision to train the youngsters about big data analytics by the young entrepreneurs:

 

We can use cohort models to aggregate the point estimates for default into an overall risk portfolio as follows:

A comparison in between R and SAS for fitting such non-standard models
Photo Coutesy of revolution-computing.typepad.com

He revealed how ANZ implemented a stress-testing simulation, which made available to business users via an Excel interface:

The primary analysis is done in r programming within 2 minutes usually, in comparison to SAS versions that actually took 4 hours to run, and frequently kept crashing due to lack of disk space. As the data is stored within SAS; SAS code is often used to create the source data…

While an R script can be used to automate the process of writing, the SAS code can do so with much simplicity around the flexible limitations of SAS.

 

Read Also: Dexlab Analytics' Workshop on Sentiment Analysis of Twitter Data Using R Programming

 

Comparison between use of R and SAS’s IML language to implement algorithms:

Mr. Ooi’s R programming code has a neat trick of creating a matrix of R list objects, which is fairly difficult to do with IML’s matrix only data structures.

He also discussed some of the challenges one ma face when trying to deploy open-source R in the commercial organizations, like “who should I yell at if things do now work right”.

And lastly he also discussed a collection of typically useful R resources as well.

For people who work in a bank and need help adopting R in the workflow, may make use of this presentation to get some knowledge about the same. And also feel free to get in touch with our in-house experts in R programming at DexLab Analytics, the premiere R programming training institute in India.

 

Ref – https://www.r-bloggers.com/how-anz-uses-r-for-credit-risk-analysis/

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

A Lead to Future: Data Analytics in Pune

Posted on by Dexlab

A-Lead-to-Future-Data-Analytics-in-Pune
 

“Big data analytics will help bridge India’s tax gap.” Economic Times

“India’s Growing Big Data Future.” NDTV

“The Big News About Big Data.” NASSCOM

“Big data to boost job openings in 2017: Report” Tech Circle

“Big Data for the next green revolution.” Hindu Business Line

Continue reading “A Lead to Future: Data Analytics in Pune”

Tigers will be safe in the hands of Big Data Analytics

Posted on by Dexlab

Once again, good news is in the air for our very own ‘Big Cats’. The very recent reports on Tiger Census have proudly announced the incredible rise in the number from 1,706 to 2, 226 since 2010, when the counting started.

 
Tigers-will-be-safe-in-the-hands-of-Big-Data-Analytics
 

The previous years have seen the major downfall in the number owing to reasons like poaching, environmental degradation, dwindling habitats and of course man- nature conflict . But in contrast, the combined efforts put forwarded by local communities, conservationists and the Government has resulted in the upliftment, as stated by Marco Lambertini, Director General of WWF International.

Continue reading “Tigers will be safe in the hands of Big Data Analytics”

Apple Watch’s Strategy Analytics, Return to 1% Growth

Posted on by Dexlab

As per the latest research from strategy analytics, the global smart watch shipments of Apple has grown by 1 percent annually to hit the major record of 8.2 million units in the 4th quarter of the year 2016. The growth of apple watch drove and got dominated with 63 percent in global smart watch share of market and Samsung still continues to hold its second position.
 
Apple Watch’s Strategy Analytics, Return to 1% Growth

 

Neil Mawston, the Executive Director at Strategy Analytics stated on the issue by saying – the global shipments have grown by 1 percent annually from the pre-existing 8.1 million units in quarter 4 in 2015 to 8.2 million in quarter 4 in 2016. The market shows a marked growth in the fourth quarter for growth in smart watches industry after the past two consecutive quarters for declining volumes. The smart watch growth is also seen to be recovering ever so slightly due to new product launches from other company giants. Moreover, there is a seasonal demand for these gadgets, and a giant such as Apple is launching stringer demand in the major developed markets in the US and UK. Hence, the international smart watch shipments grew by 1 percent annually; from the previously existing 20.8 million in full-year 2015 to a record high of 21.1 million in 2016.

Continue reading “Apple Watch’s Strategy Analytics, Return to 1% Growth”

How to Create a Macro With MS Excel

Posted on by Dexlab

Did you know that with Excel you can now automate tasks by writing so called programs macros. In this tutorial, we will learn how do so, by learning to create a simple macro, which will executable after clicking a command button. To begin you must first turn on the developer tab:

How to Create a Macro With MS Excel

Developer tab:

Do the following steps to turn the developer tab on:

 

  1. First right click anywhere on the ribbon, and then click on Customize the Ribbon.

 

Continue reading “How to Create a Macro With MS Excel”

How to Simulate Multiple Samples From a Linear Regression Model

Posted on by Dexlab

In this blog post, we will learn how to simulate multiple samples efficiently. In order to keep the discussion, easy we have simulated a single sample with ‘n’ number of observations, and ‘p’ amount of variables. But in order to use the Monte Carlo method to approximate the distribution sampling of statistics, one needs to simulate many specimens with the same regression model.

 

How to Simulate Multiple Samples From a Linear Regression Model
How to Simulate Multiple Samples From a Linear Regression Model

 

The data steps in SAS in  most blogs have 4 steps mentioned for so. However, to simulate multiple samples, put DO loop around these steps that will generate, the error term and the response variable for very observation made in the model.

Continue reading “How to Simulate Multiple Samples From a Linear Regression Model”

We Take Immense Pride in Sponsoring the Ultimate CMO Challenge – Atharva’17

Posted on by Dexlab

We are back again with some exciting news for you! We, a team of consultants of DexLab Analytics are sponsoring Atharva – the Ultimate CMO Challenge 2017, which is to be held at the Delhi School of Economics, today.

 
We Take Immense Pride in Sponsoring the Ultimate CMO Challenge – Atharva’17
 

For detailed information, click on this link. DexLab Analytics is sponsoring “The Ultimate CMO challenge” by the Delhi School of Economics

 

The first round was held on 13th February, 2017, where an Initial Case Study was needed to be submitted online and a brief for solutions, in the form of 3-4 slides or 2-3 pages write-up was to be submitted by 19th February, 2017. The candidates who got selected were declared as shortlisted by 21st February, 2017. And within 27th February 2017, final solutions in the form of PPT (with maximum 15 slides) were submitted.

Continue reading “We Take Immense Pride in Sponsoring the Ultimate CMO Challenge – Atharva’17”

Understanding The Core Components of Data Management

Posted on by Dexlab

Understanding The Core Components of Data Management

Ever wondered why many organizations often find it hard to implement Big Data? The reason often is poor or non-existent data management strategies which works counterproductive.

Data cannot be delivered or analysed without proper technology systems and procedural flows data can never be analysed or delivered. And without an expert team to manage and maintain the setup, errors, and backlogs will be frequent.

Before we make a plan of the data management strategies we must consider what systems and technologies one may need to add and what improvements can be made to an existing processes; and what do these roles bring about in terms of effects with changes.

However, a much as is possible any type of changes should be done by making sure a strategy is going to be integrated with the existing business process.

And it is also important to take a holistic point of view, for data management. After all, a strategy that does not work for its users will never function effectively for any organization.

With all these things in mind, in this article we will examine each of the three most important non-data components for a successful data management strategy – this should include the process, the technology and the people.

2

Recognizing the right data systems:

There is a lot of technology implemented into the Big Data industry, and a lot of it is in the form of a highly specific tool system. Almost all of the enterprises do need the following types of tech:

Data mining:

This will isolate specific information from a large data sets and transform it into usable metrics. Some o the familiar data mining tools are SAS, R and KXEN.

Automated ETL:

The process of ETL is used to extract, transform, and also will load data so that it can be used. ETL tools also automate this process so that human users will not have to request data manually. Moreover, the automated process is way more consistent.

Enterprise data warehouse:

A centralised data warehouse will be able to store all of an organization’s data and also integrate a related data from other sources, this is an indispensible part of any data management plan. It also keeps data accessible, and associates a lot of kinds of customer data for a complete view.

Enterprise monitoring:

These are tools, which provide a layer of security and quality assurance by monitoring some critical environments, with problem diagnosing, whenever they arise, and also to quickly notify the team behind analytics.

Business intelligence and reporting, Analytics:

These are tools that turn processed data into insights, that are tailored to extract roles along with users. Data must go to the right people and in the right format for it to be useful.

Analytics:

And in analytics highly specific metrics are combined like customer acquisition data, product life cycle, and tracking details, with intuitive user friendly interfaces. They often integrate with some non-analytics tools to ensure the best possible user experience.

So, it is important to not think of the above technologies as simply isolated elements but instead consider them as a part of a team. Which must work together as an organized unit.

For business analyst training courses in Gurgaon and other developmental updates about the Big data industry, follow our regular uploads from DexLab Analytics.

 

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

How to Parse Data with Python

Posted on by Dexlab

How to Parse Data with Python

Before we begin with our Python tutorial on how to parse data with Python, we would like you to download this machine learning data file, and then get set to learn how to parse data.

The data set we have provided in the above link, mimics exactly the way the data was when we visited the web pages at that point of time, but the interesting thing about this is we need not visit the page even. We actually have the full HTML source code, so it is just like parsing the website without the annoying bandwidth use.

Now, the first thing to do when we start is to correspond the date to our data, and then we will pull the actual data.

Here is how we start:

import pandas as pd
import os
import time
from datetime import datetime

path = "X:/Backups/intraQuarter"

Looking for a Machine Learning course online? We have Big Data courses that will bring big dreams to reality.

As given above, we are importing the Pandas for the Pandas module, OS, that is so we can interact with the directories, date and time for managing the date and time information.

Furthermore, we will finally define the path, which is the path to the intraQuarter folder than one will need to unzip the original zip file, which you just downloaded from the website.

def Key_Stats(gather="Total Debt/Equity (mrq)"):
    statspath = path+'/_KeyStats'
    stock_list = [x[0] for x in os.walk(statspath)]
    #print(stock_list)

We began our functions, with the specification that we are going to try to collect all the Debt/equity values.

The path to the stats directory is Statspath.

To list all the contents in the directory, you can use stock_list which is a fast one-liner for the loop that uses os.walk.

Take up our Machine Learning training course with Python to know more about this in-demand skill!

Then the next step is to do this:

    for each_dir in stock_list[1:]:
        each_file = os.listdir(each_dir)
        if len(each_file) > 0:

Mentioned above is a cycling through of directory (which is every stock ticker). Then the next step is to list “each_file”, which is each file within that very stock’s directory. If in case the length of each_file which is in fact is a list of all of the files in the stock’s directory, is greater than 0 only then will we want to proceed. However, there are some stocks with no files or data:

            for file in each_file:

                date_stamp = datetime.strptime(file, '%Y%m%d%H%M%S.html')
                unix_time = time.mktime(date_stamp.timetuple())
                print(date_stamp, unix_time)
                #time.sleep(15)

Key_Stats()

Finally, at the end, we must run a loop that pulls the date_stamp, from each file. All our files are actually stored under their ticket, with a file name for the exact date and time from which the information is being taken out.

It is from there that we will explain to date-time what the format for our date stamp is, and then we will convert it to a Unix time stamp.

To know more about data parsing or anything else in python, learn Machine Learning Using Python with the experts at DexLab Analytics.


 
This post originally appeared on – pythonprogramming.net/parsing-data-website-machine-learning
 


.

Call us to know more

Gurgaon

+91 8676079880

Kolkata

+91 9903662244

Quick Links

Our Courses

Important dates

Copyright © 2024 DexLab Solutions Corporation | Privacy Policy | Legal Disclaimer | Terms and Conditions