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Asked on 02 Feb Financial Planning/Business Analytics Training

Hi, I am Mallikarjuna from Andhra Pradesh, I am doing research in Financial Economics. My objective is... read more

Hi, I am Mallikarjuna from Andhra Pradesh, I am doing research in Financial Economics. My objective is to forecast stock returns using different models like ARIMA, Artificial Neural Networks, Wavelets and Hybrid models and finding out the best model out of these. Is there anyone in this forum who has expertise knowledge in predictive analytics and time series forecasting using different packages in R programming or Mat lab ? Please reply as soon as possible.

I would be so grateful to your reply. Thank you

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Lesson Posted on 03 Jan Tuition/BTech Tuition/Big Data Analytics Financial Planning/Business Analytics Training IT Courses/MS Office Software Training

Data Filtering In Excel

iTech Analytic Solutions

"iTech Analytic Solutions" (iTAS) is ranked as No. 1 Analytic Training Center in Bangalore by ThinkVidya.com "iTech...

Filtering data in MS Excel refers to displaying only the rows that meet certain conditions (The other rows gets hidden). Using the store data, if you are interested in seeing data where Shoe Size is 36, then you can set filter to do this. Follow the below mentioned steps to do this. Place a cursor... read more

Filtering data in MS Excel refers to displaying only the rows that meet certain conditions (The other rows gets hidden).

Using the store data, if you are interested in seeing data where Shoe Size is 36, then you can set filter to do this. Follow the below mentioned steps to do this.

  • Place a cursor on the Header Row.

  • Choose Data Tab » Filter to set filter.

  • Click the drop-down arrow in the Area Row Header and remove the check mark from Select All, which unselects everything.

  • Then select the check mark for Size 36 which will filter the data and displays data of Shoe Size 36.

  • Some of the row numbers are missing; these rows contain the filtered (hidden) data.

  • There is drop-down arrow in the Area column now shows a different graphic - an icon that indicates the column is filtered.

Using Multiple Filters:

You can filter the records by multiple conditions i.e. by multiple column values. Suppose after size 36 is filtered, you need to have the filter where color is equal to Coffee. After setting filter for Shoe Size, choose Color column and then set filter for color.

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Lesson Posted on 03 Jan Financial Planning/Business Analytics Training IT Courses/MS Office Software Training Functional Training/Data Analytics

Using Ranges In Excel

iTech Analytic Solutions

"iTech Analytic Solutions" (iTAS) is ranked as No. 1 Analytic Training Center in Bangalore by ThinkVidya.com "iTech...

A cell is a single element in a worksheet that can hold a value, some text, or a formula. A cell is identified by its address, which consists of its column letter and row number. For example, cell B1 is the cell in the second column and the first row. A group of cells is called a range. You designate... read more

A cell is a single element in a worksheet that can hold a value, some text, or a formula. A cell is identified by its address, which consists of its column letter and row number. For example, cell B1 is the cell in the second column and the first row.

A group of cells is called a range. You designate a range address by specifying its upper-left cell address and its lower-right cell address, separated by a colon.

Example of Ranges:

  • C24: A range that consists of a single cell.

  • A1:B1: Two cells that occupy one row and two columns.

  • A1:A100: 100 cells in column A.

  • A1:D4: 16 cells (four rows by four columns).

Selecting Ranges:

You can select a range in several ways:

  • Press the left mouse button and drag, highlighting the range. Then release the mouse button. If you drag to the end of the screen, the worksheet will scroll.

  • Press the Shift key while you use the navigation keys to select a range.

  • Press F8 and then move the cell pointer with the navigation keys to highlight the range. Press F8 again to return the navigation keys to normal movement.

  • Type the cell or range address into the Name box and press Enter. Excel selects the cell or range that you specified.

Selecting Complete Rows and Columns:

When you need to select an entire row or column. You can select entire rows and columns in much the same manner as you select ranges:

  • Click the row or column border to select a single row or column.

  • To select multiple adjacent rows or columns, click a row or column border and drag to highlight additional rows or columns.

  • To select multiple (nonadjacent) rows or columns, press Ctrl while you click the row or column borders that you want.

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Lesson Posted on 03 Jan Financial Planning/Business Analytics Training IT Courses/MS Office Software Training Functional Training/Data Analytics

Using Styles In Excel

iTech Analytic Solutions

"iTech Analytic Solutions" (iTAS) is ranked as No. 1 Analytic Training Center in Bangalore by ThinkVidya.com "iTech...

With MS Excel 2010 Named styles make it very easy to apply a set of predefined formatting options to a cell or range. It saves time as well as make sure that look of the cells are consistent. A Style can consist of settings for up to six different attributes: Number format, Font (type, size,... read more

With MS Excel 2010 Named styles make it very easy to apply a set of predefined formatting options to a cell or range. It saves time as well as make sure that look of the cells are consistent.

A Style can consist of settings for up to six different attributes:

  • Number format,

  • Font (type, size, and color),

  • Alignment (vertical and horizontal),

  • Borders,

  • Pattern,

  • Protection (locked and hidden).

Now, let us see how styles are helpful. Suppose that you apply a particular style to some twenty cells scattered throughout your worksheet. Later, you realize that these cells should have a font size of 12 pt. rather than 14 pt. Rather than changing each cell, simply edit the style. All cells with that particular style change automatically.

Applying Styles:

Choose Home » Styles » Cell Styles. Note that this display is a live preview, that is, as you move your mouse over the style choices, the selected cell or range temporarily displays the style. When you see a style you like, click it to apply the style to the selection.

Creating Custom Style in MS Excel:

We can create new custom style in Excel 2010. To create a new style, follow these steps:

  • Select a cell and click on Cell styles from Home Tab.

  • Click on New Cell Style and give style name.

  • Click on Format to apply formatting to the cell.

After applying formatting click on OK. This will add new style in the styles. You can view it on Home » Styles.

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Lesson Posted on 03 Jan Financial Planning/Business Analytics Training Functional Training/Data Analytics IT Courses/MS Office Software Training

Adding Graphics In Excel

iTech Analytic Solutions

"iTech Analytic Solutions" (iTAS) is ranked as No. 1 Analytic Training Center in Bangalore by ThinkVidya.com "iTech...

Graphic Objects in MS Excel: MS Excel supports various types of graphic objects like Shapes gallery, SmartArt, Text Box, and WordArt available on the Insert tab of the Ribbon.Graphics are available in the Insert Tab. See the screenshots below for various available graphics in MS Excel 2010. Insert... read more

Graphic Objects in MS Excel:

MS Excel supports various types of graphic objects like Shapes gallery, SmartArt, Text Box, and WordArt available on the Insert tab of the Ribbon.Graphics are available in the Insert Tab. See the screenshots below for various available graphics in MS Excel 2010.

Insert Shape:

  • Choose Insert Tab » Shapes dropdown.

  • Select the shape you want to insert. Click on shape to insert it.

  • To edit the inserted shape just drag the shape with the mouse. Shape will adjust the shape.

Insert Smart Art:

  • Choose Insert Tab » SmartArt.

  • Clicking SmartArt will open the SmartArt dialogue as shown below in the screen-shot. Choose from the list of available smartArts.

  • Click on SmartArt to Insert it in the worksheet.

  • Edit the SmartArt as per your need.

Insert Clip Art:

  • Choose Insert Tab » Clip Art.

  • Clicking Clip Art will open the search box as shown in the below screen-shot. Choose from the list of available Clip Arts.

  • Click on Clip Art to Insert it in the worksheet.

Insert Word Art:

  • Choose Insert Tab » WordArt.

  • Select the style of WordArt, which you like and click it to enter a text in it.

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Lesson Posted on 14/07/2017 IT Courses/Data Science Financial Planning/Business Analytics Training

Learn Data Science In 8 Steps

Ranjit Mishra

I have Certificate Degree in Predictive Business Analytics from Northwestern University, USA. Have been...

8 Steps To Learn Data Science There have been a lot of surveys over the past few years on the educational background of data scientists. As a result, there have also been many different results. In the O’Reilly Data Science Salary Survey of 2014, about 28% of the respondents had a Bachelor’s... read more

8 Steps To Learn Data Science

There have been a lot of surveys over the past few years on the educational background of data scientists. As a result, there have also been many different results. In the O’Reilly Data Science Salary Survey of 2014, about 28% of the respondents had a Bachelor’s degree, while 44% had a Master’s degree and 20% had a Ph.D. Common fields that data scientists have as backgrounds are mathematics/Statistics, Computer Sciences, and Engineering. 

In general, you could conclude that the degree that you need to have completed to become a data scientist is usually a Master’s degree or Ph.D. The field that you come from is of less importance, but you have an advantage if you have a quantitative background.

Step 1. Get Good at Stats, Maths and Machine Learning:

The perspective on the definition of data science might have changed over the years, but data science has remained a somewhat technical occupation. A sound knowledge of statistics, mathematics, and machine learning are still considered a main requirement for anyone to do data science.

Getting up to speed with these three can be a pain, especially for those who have no technical background whatsoever. Luckily, you have more than enough qualitative resources to help you out on this: Khan Academy offers online courses on a variety of mathematics topics that will undoubtedly be of great value to you, but make sure to also take a look at the Linear Algebra course from MIT Open Courseware. For statistics, DataCamp, Udacity and OpenIntro’s material might help you, and for Machine Learning, you should keep an eye out for the content on DataCamp, Stanford Online and Coursera.

Step 2. Learn to Code:

Developing your hacking skills is also one of the things that you need to take into account still if you want to learn data science.

You can start by getting familiar with the computer science fundamentals: get to know the basic data structures and search algorithms. Then, step up to understanding how end-to-end development works: the stuff you will work on will be integrated with other systems, so it’s best to understand how development from beginning to end, from the requirements gathering and analysis to testing and maintaining code. When you have grasped this concept, it’s time to pick a language. You can go for an open source language or a commercial one. Things to take account in your decision are the learning curve, the industry you want to work in, the salary that comes with being proficient in the language.

Step 3. Understand Databases:

When you start out learning data science, you see that a lot of tutorials focus on you retrieving data from flat files. However, when you start working or when you get in touch with the industry itself, you see that most of the work happens through a connection with one or multiple databases.

And there are a lot of databases out there. Companies might work with commercial ones like Oracle or they might opt for open-source alternatives. The key to seeing the forest for the trees here is to understand how databases work. Learn about the why and how of databases and the what will come. Concepts that you should grasp and know your way around in are the Relational Database Management Systems (RDBMS) and data warehousing. That means that relational versus dimensional modeling should not hold any secrets for you, nor should SQL or the Extract-Transform-Load process (ETL) surprise you.

Step 4. Explore The Data Science Workflow:

A next phase in the learning process would be to explore the data science workflow. A lot of tutorials or courses focus on only one or two aspects of it, but lose the general overview of the process that you will need to go through once you’re working as a data scientist or in a data science team. It’s essential not to lose sight of the iterative process that data science is.

For data science beginners that know how to program, the easiest way to discover how the data science workflow works is by practicing your coding skills: get started on your journey with R or Python. There are several in-built packages and libraries in both R and Python that will make your coding life easier. 

Step 5. Gain Understanding of Big Data:

Big data might have been a hype, but it’s definitely out there, and it’s important to realize this and understand what it encompasses. Three things to learn about big data are:

  1. See why big data requires a different approach of data processing. The best approach to do this is probably by looking at big data use cases. You can read up on some here.

  2. Get familiar with the Hadoop framework: it’s widely used for distributed data storage and processing.

  3. Don’t forget about Spark. Getting the hang out of Spark in combination Scala is the way to go. And, even better, you kill two birds with one stone: you practice your coding skills and widen your view on data science.

Step 6. Grow, Connect and Learn:

Grow: Once you have gotten to this point where you already master the fundamentals, it’s time to grow: practice as much as you can by doing data science challenges, like the ones you find on Kaggleor DrivenData. They will definitely challenge you to put the theory into practice. Also, you should also let your intuition grow.

Connect: As a data science learner, you might fall into the pitfall of staying occupied with your learning and that of other learners, but it is equally important to connect to those who already have some more experience in the field. This way, you build up a network to fall back on in case you have questions, need advice or tips, or whatever. These people will motivate you to keep up the good learning and will challenge you to go even further.

Learn: Continuous learning and data science could be synonyms. The Kaggle and DrivenDatachallenges that have been mentioned above will teach you a thing or two about how data science is done in practice. Apart from these relatively small exercises, you might consider starting up a pet project and explore some things even on a deeper level.

 Step 7. Immerse Yourself Completely:

Just like a language bath, you’re in need of a data science bath. Depending on your skills and knowledge that you already have, you might consider a bootcamp, an internship or a job. A bootcamp is an amazing way of kickstarting or boosting your data science learning. As a plus, you meet a lot of people, and you have an opportunity to build or extend your network. Are you having trouble finding one? Check out Galvanize and Metis, but also don’t forget that your Meetup Groups might also organize bootcamps and workshops for the community!

Secondly, when you have already got the basics of data science under control, you should consider getting an internship. A lot of the big companies like Facebook, Quora and Amazon have looked for interns before, so this is a great place to start your search. Also, you can use your social channels or your network to get first-hand information on open positions for internships. Lastly, also take a look at startups: these smaller companies can be willing to let you learn on the job as long as you learn quickly. AngelList is worth checking out for startup jobs.

Step 8. Engage with The Community:

This last step is one that can be overlooked sometimes. Even when you have a job in data science or as a data scientist, you still need to remember that data science equals continuous learning. There are new advancements all the time, and it’s of key importance to stay informed and curious about what’s happening around you. So don’t hold back to contribute to discussions on social media, subscribe to a newsletter, follow the key people of the data science industry, listen to a podcast. Whatever you can do to engage with the community!

To stay up to date with the latest news, you can register to the following newsletters: the bimonthly KD Nuggets newsletter and Data Elixir or the Data Science Weekly newsletters. Next, follow some of the key people in the data science industry on Twitter. This will also keep you up to speed with the latest. Just some of the people that might interest you are DJ Patil, Andrew Ng, and Ben Lorica.

Join some communities online. LinkedIn, Facebook, Reddit. They all offer the possibility to connect with peers. You should take on the opportunity to become a member of one of those groups:

  • On LinkedIn, make sure to take a look at the “Big Data, Analytics, Business Intelligence”, “Big Data Analytics”, “Data Scientists” or “Data Mining, Statistics, Big Data, Data Visualization, and Data Science” groups.

  • At Facebook, the “Beginning Data Science, Analytics, Machine Learning, Data Mining, R, Python”, “Learn Python” groups might interest you.

  • Subreddits that you can keep an eye on are “/r/datascience”, “/r/rstats” and “/r/python”, among many others!

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Lesson Posted on 14/07/2017 IT Courses/SAP/Analytics products Tuition/BTech Tuition/Big Data Analytics Financial Planning/Business Analytics Training +1 Functional Training/Data Analytics less

Variations Of Random Forest In R

Ankit Katiyar

One of the important steps in using analytics to generate insights is model fitting. Typical projects involve a lot of data cleaning so that high accuracy is achieved on application of the model. Competitions are all about data cleaning and models. There are various models which can be fitted on data... read more

One of the important steps in using analytics to generate insights is model fitting. Typical projects involve a lot of data cleaning so that high accuracy is achieved on application of the model. Competitions are all about data cleaning and models. There are various models which can be fitted on data under different conditions. One of the most intuitive of those models is decision trees. Decision trees classify data into buckets based on “decisions” based on the feature values. Most of the competitions start with bench-marking based on results from ensemble of trees, known as random decision forests. Random Forests, as they are called, use ensemble of trees based and are the best examples of ‘Bagging’ techniques. R, the popular language for model fitting has made a variety of random forest packages available for use. Let’s discuss a few of them (in no way this list is exhaustive).

  • RandomForest: The ‘classic’ package in R which implements the most basic random forest logic and is really robust. The package is very user friendly and provides the user with the option to tune features such as number of trees and depth of trees. The package optionally provides the ability to derive feature importanceand proximity measures. Feature importance is based on the error increase when OOB data is changed while keep all other things same. On the other hand, Proximity measure is a matrix where (i, j) element indicates fraction of trees in which elements i and j fall in the same terminal node. The package can be used for classification or regression problems and can be learnt with ease
  • Cforest: This package is computationally more expensive and better than the randomForest package in terms of accuracy. cforest uses OOB data which means more information and higher accuracy. At the same time it is slower and can handle less data for the same memory. It then uses weighted average of the trees to get the final ensemble. However, the main cause for cforest having a more reliable predictions is the fact that it produces unbiased trees. randomForest have a drawback that the simple algorithm is invariably biased towards features with many cut points. There are features which are continuous or have many categories and can be preferred. Whenever you have large computational resources at your disposal, do use cforest for accuracy.
  • ObliqueRF: “Oblique” forests is an underrated, advanced yet useful concept which is based on separating trees using hyper planes instead of features. They can easily outperform randomForest especially in cases when all the features are discrete or we have spectral data. Just like randomForest, Oblique forests are also governed by subspace dimensions(or number of features) and ensemble size(or number of trees). However, since they make oblique cuts rather than orthogonal ones, recursive binary splits and ridge regression are also involved for splitting. I have seen a cool implementation of oblique random forests as the prize winning code in a kaggle competition! Hence oblique random forests sure pack a punch. ObliqueRF does end up having a higher bias and lower variance than randomForest.
  • ParallelForest: ParallelForest is an implementation to run randomForest using parallel computing. The package has functions grow.forest. Its pretty handy when there are millions of rows in the training set. A data set which took days for randomForest package to fit on was handled by ParallelForest in under an hour. However, there are still doubts on whether the accuracy is the same for both packages under all conditions and whether classification can be implemented using parallel processing. (Another package bigrf is also based on using multi-threading and caching for very large data but it was not built with the objective to speed up processing rather it is based on handling very large data).
  • RandomUniformForest: This package produces unpruned trees and are useful for regression, classification and unsupervised learning. If cforest is slower but more accurate than randomForest then randomUniformForest falls on the other end of being the faster but slightly less accurate version. The trees have lower correlation, thereby resulting in lower bias but higher variance. Moreover, they involve use of uniform distribution. Since we don’t care much about bias as perfectly randomized trees will cancel it out, randomUniformForests are useful in situations where the features themselves follow specified distributions
  • Randomforest SRC: Survival, Regression and Classification(SRC) are the three types of models this package provides a unified function for. Additionally, there are multivariate and unsupervised extensions as well as parallel processing through openMP. I have come to use this package whenever there is doubt on what should be the best approach for data model fitting. Coupled with missing value imputation, the package provides a first look kind of model useful for further exploration and deep dive analysis.
  • Ranger: Ranger comes to the rescue when you have high dimensional data and want a memory efficient yet fast implementation of randomForest. The word ranger came from RANdom forest GEneRator. The main purpose where I have used ranger is to build models quickly and find out optimal parameter values using parameter tuning.
  • Rborist: Rborist is a high performance implementation of randomForest. Compared to original randomForest, this package optimizes the algorithms such that model fitting is performed with less data movement within memory and create opportunities for scaling up performance. Hence, as the features increase, the processing time increases only linearly (as opposed to exponential increase expected for randomForests). The package also supports missing value imputation. Hence, in projects where we ourselves generate a lot of features, this package becomes seemingly more suitable.

Since the idea being first suggested in the 90’s Random forests have become a popular method of model fitting and are used in various forms. There are even more implementations such as rotationForests(based on fitting features over principal components), xgboost (extreme gradient boosting, a clever tree based technique that uses boosting) and rFerns (useful for comparing images) and regularized random forests. This article will be useful for those who have had gone through decision tree and basic random forest concepts and are willing to learn its different variations in R.

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Lesson Posted on 24/01/2017 IT Courses/Tableau IT Courses/Data Warehouse Financial Planning/Business Analytics Training

Tableau Hands on

EduGuys

Quality is what we believe in. We @EduGuys (Formerly Stay Ahead) working towards it. Real-time business...

This video explains about Parameters, Grouping, Top N parameter, Playing with Filters, Donut charts, Bullet Graphs, Gantt Charts and Box and Whisker charts in Tableau. This video will be very beneficial for Tableau Starters. All the Best!!! Stay AheadWe Build Network!!! read more

This video explains about Parameters, Grouping, Top N parameter, Playing with Filters, Donut charts, Bullet Graphs, Gantt Charts and Box and Whisker charts in Tableau. This video will be very beneficial for Tableau Starters.

All the Best!!!

Stay Ahead
We Build Network!!!

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Lesson Posted on 29/11/2016 IT Courses/Data Science Financial Planning/Business Analytics Training

What it takes to become a Data Scientist?

Dni Institute

We provide personalised Data Science and Advanced Analytics trainings. The trainers have worked with...

Most of the research organizations and industry leading publications suggested a huge shortage of persons with deep Data Science skills. Also, increasing number of candidates are aspiring to become a Data Scientist. If you spend some time searching - " What is Data Science?", "What skills are required... read more

Most of the research organizations and industry leading publications suggested a huge shortage of persons with deep Data Science skills. Also, increasing number of candidates are aspiring to become a Data Scientist. 

If you spend some time searching  - " What is Data Science?", "What skills are required to become a successful Data Scientist?", you will find volume of articles and with varied views on these questions. 

One extreme view suggests- Data Scientist is a person with skills and knowledge  of anything under the sun. From hacking skills to leadership skills, but we all have limited time if not the capacity to learn and master these skills. 

In our views one should have 3 skills which are foundation to Data Science and these are

  • Quantitative Skills : Knowledge of Statistical, Mathematical and Machine Learning Methods. In each of the Data Science project you will using these skills.  For example, you will be optimizing routes of shared cabs or finding next best interactions for a customer.
  • Tools and Technology: Data new oil for industry or data could be a competitive advantage. A data scientist needs to be able to access, manipulate and model the data for creating insights for Decisions. Some of the analytics tools are R, SAS , Python etc. Big Data and other database knowledge also becomes imperative. 
  • Functional and Domain Knowledge: Data Science facilitates better decisions. Decisions are linked to a context. So understanding of business context is important to build models or analytics which could be implemented

Problem solving, communication and interpersonal skills are probably common to any of the professional career steam. 

DNI Institute helps aspiring Data Scientists to acquire these skills and effectively. Happy Data Science Learning.

 

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Lesson Posted on 25/10/2016 Financial Planning/Stock Market Investment Financial Planning/Stock Market Trading Financial Planning/Business Analytics Training +1 Functional Training/Business Analysis Training less

Important points to keep in mind while trade

Nagaraj

I started my career in share market with so many losses, nearly I have lost 20 lakhs in 6 months. After...

? Do not over trade – Do not put all your money in share market. ? Do Not put all your money in single share or single sector – Put or divide your money in multiple shares or sectors. This may reduce the risk of heavy loss. ? Do not panic or fear –Think twice before making your trade/plan... read more

? Do not over trade – Do not put all your money in share market.

? Do Not put all your money in single share or single sector – Put or divide your money in multiple

shares or sectors. This may reduce the risk of heavy loss.

? Do not panic or fear –Think twice before making your trade/plan and once done stick to it, don’t panic

or fear.

? Accept loss – If your trade is going against you and if you are not sure about your trade then

immediately accept the loss and come out of your trade. It will save you from heavy loss.

? Right opportunity – do not fall in trade early, wait for right opportunity and then trade. It’s very

important.

“Wait, watch and then trade” you will get success.

? Everyday is not trading day – Do not force yourself to do trading everyday. It’s wrong if you are not sure

about the market movement for that day then it always wise decision on to be away from market and

not to trade.

? Keep you greediness away – Most of the people loose in share market due to greediness. Get satisfied

whatever profit you get and come out of that trade and wait for next opportunity. Don’t wait to take

huge for that single day.

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