sas training in delhi testimony from Jaideep

This is the feedback i would like to share on iisastr
‘Best place to learn SAS programme.Excellent technique,valuable and helpful learning material,good classroom practice and unlimited revision sessions.All in all great centre to be a part of.’

Thanks and regards
Jaideep Wadhwa

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required sas job profile in gurgaon

one of our client looking for sas profile in gurgaon,detail JD given below
Snapshot of JD:

End to end ETL solution knowledge. The analyst will continuously monitor/check Data Flow , Data Validity and Data cleaning.

Responsibilities:

Monitor and examine ETL process and identify automations.
Assess current model and think of creating new data validations.
Enhance the SAS Codes by digging into existing ones.

Requirements:

Experience with SAS(Base/Advanced) Coding.
Experience in Unix Scripting.
Experience in SAS EG/DI Tool.
Good knowledge of Excel and VBA will be appreciated.
Ability to think critically and properly assess the work.
Knowledge of incident and problem management frameworks.
Excellent communication skills
Willingness to work in 24×7 operational environment.

Key skills: SAS Base and Advanced, Unix, Excel
VBA will be added advantage.

send your resume to hr@iisastr.com

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SAS Certified Advanced Programmer for SAS 9 practice test

SAS Certified Advanced Programmer for SAS 9 practice test
 
visit the following link for online test http://www.blog.iisastr.com/sas-certified-advanced-programmer-for-sas-9-practice-test.html
 

SAS Certified Advanced Programmer for SAS 9 practice test

SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling practice test

Leaderboard: SAS Certified Advanced Programmer for SAS 9 practice test

maximum of 10 points
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Practice test for SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling

SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling

visit   the  link  to   start quiz   http://www.blog.iisastr.com/practice-test-for-sas-certified-statistical-business-analyst-using-sas-9-regression-and-modeling.html

SAS Certified Statistical Business Analyst Using SAS 9

SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling practice test

 

Leaderboard: SAS Certified Statistical Business Analyst Using SAS 9

maximum of 11 points
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sas training in delhi testimony from Pushpendra

Hi Sir,

The training material which we discussed in every class is very organised and you presented it very clearly. You have provided great depth on the topics that were covered and is able to discuss off-topic areas as well. Each of the sessions/classes were mix of presented material and hands-on coding of different topics and was done in easy understandable steps. Your expertise in SAS is reflected in all areas of the course.
Thanks
Pushpendra Yadav
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sas training in delhi testimony from Vaibhav Jain

The training provided at IISASTR institute is one of the best as per the industry standards for learning SAS. The deep understanding and work experience of Debendra Dhir sir helped me get the insights of Base SAS and understand the core concepts. The user friendly environment in classes, practice sessions and weekly assignments helped me gain the most of it. Being away from programming for a long time was challenging but with the support of Debendra Sir, it was a cake walk for me throughout the duration of my classes. The doubt sessions both after the classes and on email helped us learning the concepts thoroughly.

I wish IISASTR and Debendra Sir all the very best for future and keep helping professionals learn the field of analytics. Good Luck !!

Vaibhav Jain

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sas training in delhi testimony from Gaurav Nailwal

1. I found the sessions quite interactive .
2. focus is on practical knowledge and real life problems .
3. overall experience was good .

Gaurav Nailwal

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sas training in delhi testimony from ved piyush

Hello everyone,

My association with Mr. Debendra Dhir started when I undertook Base SAS classes from him in March of 2015. Mr. Dhir is a thorough professional and he knows SAS like the back of his hand. He explains everything very lucidly and makes sure that you have understood them by giving relevant assignments. I had no coding background nor had I learnt any language or software prior to SAS, but due to the earnest efforts and to the point teaching of Mr. Dhir, I recently cleared my Base SAS certification with 92% marks. He has a very unique methodology of teaching which is rooted more in the practical application of SAS with relevant emphasis on the requisite theoretical concepts. He explains the most complex of concepts with simple examples which strengthen the understanding. Mr Dhir also has the distinction of having the highest number of SAS certifications in India, so when you learn from one of India’s best, you can rest assured that you are in safe hands.

I would be going to the United States to pursue MS in Statistics from the University of Minnesota- Twin Cities in some time, and my acquaintance with SAS would indeed be of great help to me while I study there. I am thankful to Mr. Dhir for introducing me to the world of SAS which is simple yet extremely efficient.

Best Regards,

Ved Piyush.

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Base sas free practice test

BASE SAS certification practice test

Base SAS certification practice test

More detail  visit   sas training  in  gurgaon

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Financial analysis using Python training in delhi ncr

Financial analysis using Python  training in  delhi   gurgaon  noida.

Introduction and Installation of Python

• Introduction to Python
• Installing Python
• Different versions of Python
• Ways to launch Python
• Launching Python with GUI
• Launching Python from the Python command line
• Launching Python from our own DOS window
• Quitting Python
• Error messages
• Python language is case sensitive
• Initializing the variable
• Finding the help window
• Finding manuals and tutorials
• Finding the version of Python
Using Python as an Ordinary Calculator
• Assigning values to variables
• Displaying the value of a variable
• Error messages
• Can’t call a variable without assignment
• Choosing meaningful names
• Using dir() to find variables and functions
• Deleting or unsigning a variable
• Basic math operations – addition, subtraction,
multiplication, and division
• The power function, floor, and remainder
• A true power function
• Choosing appropriate precision
• Finding out more information about a specific built-in function
• Listing all built-in functions
• Importing the math module
• The pi, e, log, and exponential functions
• “import math” versus “from math import *”
• A few frequently used functions
• The print() function
• The type() function
• Last expression _ (underscore)
• Combining two strings
• The upper() function
• The tuple data type
Using Python as a Financial Calculator
• Writing a Python function without saving it
• Default input values for a function
• Indentation is critical in Python
• Checking the existence of our functions
• Defining functions from our Python editor
• Activating our function using the import function
• Debugging a program from a Python editor
• Two ways to call our pv_f() function
• Generating our own module
• Types of comments
• The first type of comment
• The second type of comment
• Finding information about our pv_f() function
• The if() function
• Annuity estimation
• Converting the interest rates
• Continuously compounded interest rate
• A data type – list
• Net present value and the NPV rule
• Defining the payback period and the payback period rule
• Defining IRR and the IRR rule
• Showing certain files in a specific subdirectory
• Using Python as a financial calculator
• Adding our project directory to the path
13 Lines of Python to Price a Call Option
• Writing a program – the empty shell method
• Writing a program – the comment-all-out method
• Using and debugging other programs
Introduction to Modules
• What is a module?
• Importing a module
• Adopting a short name for an imported module
• Showing all functions in an imported module
• Comparing “import math” and “from math import *”
• Deleting an imported module
• Importing only a few needed functions
• Finding out all built-in modules
• Finding out all the available modules
• Finding the location of an imported module
• More information about modules
• Finding a specific uninstalled module
• Module dependency
Introduction to NumPy and SciPy
• Installation of NumPy and SciPy
• Launching Python from Anaconda
• Examples of using NumPy
• Examples of using SciPy
• Showing all functions in NumPy and SciPy
• More information about a specific function
• Understanding the list data type
• Working with arrays of ones, zeros, and the identity matrix
• Performing array manipulations
• Performing array operations with +, -, *, /
• Performing plus and minus operations
• Performing a matrix multiplication operation
• Performing an item-by-item multiplication operation
• The x.sum() dot function
• Looping through an array
• Using the help function related to modules
• A list of subpackages for SciPy
• Cumulative standard normal distribution
• Logic relationships related to an array
• Statistic submodule (stats) from SciPy
• Interpolation in SciPy
• Solving linear equations using SciPy
• Generating random numbers with a seed
• Finding a function from an imported module
• Understanding optimization
• Linear regression and Capital Assets Pricing Model (CAPM)
• Retrieving data from an external text file
• The loadtxt() and getfromtxt() functions
• Installing NumPy independently
• Understanding the data types
Visual Finance via Matplotlib
• Installing matplotlib via ActivePython
• Alternative installation via Anaconda
• Understanding how to use matplotlib
• Understanding simple and compounded interest rates
• Adding texts to our graph
• Working with DuPont identity
• Understanding the Net Present Value (NPV) profile
• Using colors effectively
• Using different shapes
• Graphical representation of the portfolio diversification effect
• Number of stocks and portfolio risk
• Retrieving historical price data from Yahoo! Finance
• Histogram showing return distribution
• Comparing stock and market returns
• Understanding the time value of money
• Candlesticks representation of IBM’s daily price
• Graphical representation of two-year price movement
• IBM’s intra-day graphical representations
• Presenting both closing price and trading volume
• Adding mathematical formulae to our graph
• Adding simple images to our graphs
• Saving our figure to a file
• Performance comparisons among stocks
• Comparing return versus volatility for several stocks
• Finding manuals, examples, and videos
• Installing the matplotlib module independently
Statistical Analysis of Time Series
• Installing Pandas and statsmodels
• Launching Python using the Anaconda command prompt
• Launching Python using the DOS window
• Launching Python using Spyder
• Using Pandas and statsmodels
• Using Pandas
• Examples from statsmodels
• Open data sources
• Retrieving data to our programs
• Inputting data from the clipboard
• Retrieving historical price data from Yahoo! Finance
• Inputting data from a text file
• Inputting data from an Excel file
• Inputting data from a CSV file
• Retrieving data from a web page
• Inputting data from a MATLAB dataset
• Several important functionalities
• Using pd.Series() to generate one-dimensional time series
• Using date variables
• Using the DataFrame
• Return estimation
• Converting daily returns to monthly returns
• Converting daily returns to annual returns
• Merging datasets by date
• Forming an n-stock portfolio
• T-test and F-test
• Tests of equal means and equal variances
• Testing the January effect
• Many useful applications
• 52-week high and low trading strategy
• Roll’s model to estimate spread (1984)
• Amihud’s model for illiquidity (2002)
• Pastor and Stambaugh (2003) liquidity measure
• Fama-French three-factor model
• Fama-MacBeth regression
• Estimating rolling beta
• Understanding VaR
• Constructing an efficient frontier
• Estimating a variance-covariance matrix
• Optimization – minimization
• Constructing an optimal portfolio
• Constructing an efficient frontier with n stocks
• Understanding the interpolation technique
• Outputting data to external files
• Outputting data to a text file
• Saving our data to a binary file
• Reading data from a binary file
• Python for high-frequency data
• Spread estimated based on high-frequency data
• More on using Spyder
• A useful dataset

The Black-Scholes-Merton Option Model
• Payoff and profit/loss functions for the call and put options
• European versus American options
• Cash flows, types of options, a right, and an obligation
• Normal distribution, standard normal distribution, and cumulative standard normal distribution
• The Black-Scholes-Merton option model on non-dividend
paying stocks
• The p4f module for options
• European options with known dividends
• Various trading strategies
• Covered call – long a stock and short a call
• Straddle – buy a call and a put with the same exercise prices
• A calendar spread
• Butterfly with calls
• Relationship between input values and option values
• Greek letters for options
• The put-call parity and its graphical representation
• Binomial tree (the CRR method) and its graphical representation
• The binomial tree method for European options
• The binomial tree method for American options
• Hedging strategies
Python Loops and Implied Volatility
• Definition of an implied volatility
• Understanding a for loop
• Estimating the implied volatility by using a for loop
• Implied volatility function based on a European call
• Implied volatility based on a put option model
• The enumerate() function
• Estimation of IRR via a for loop
• Estimation of multiple IRRs
• Understanding a while loop
• Using keyboard commands to stop an infinitive loop
• Estimating implied volatility by using a while loop
• Nested (multiple) for loops
• Estimating implied volatility by using an American call
• Measuring efficiency by time spent in finishing a program
• The mechanism of a binary search
• Sequential versus random access
• Looping through an array/DataFrame
• Assignment through a for loop
• Looping through a dictionary
• Retrieving option data from CBOE
• Retrieving option data from Yahoo! Finance
• Different expiring dates from Yahoo! Finance
• Retrieving the current price from Yahoo! Finance
• The put-call ratio
• The put-call ratio for a short period with a trend
Monte Carlo Simulation and Options
• Generating random numbers from a standard normal distribution
• Drawing random samples from a normal (Gaussian) distribution
• Generating random numbers with a seed
• Generating n random numbers from a normal distribution
• Histogram for a normal distribution
• Graphical presentation of a lognormal distribution
• Generating random numbers from a uniform distribution
• Using simulation to estimate the pi value
• Generating random numbers from a Poisson distribution
• Selecting m stocks randomly from n given stocks
• Bootstrapping with/without replacements
• Distribution of annual returns
• Simulation of stock price movements
• Graphical presentation of stock prices at options’ maturity dates
• Finding an efficient portfolio and frontier
• Finding an efficient frontier based on two stocks
• Impact of different correlations
• Constructing an efficient frontier with n stocks
• Geometric versus arithmetic mean
• Long-term return forecasting
• Pricing a call using simulation
• Exotic options
• Using the Monte Carlo simulation to price average options
• Pricing barrier options using the Monte Carlo simulation
• Barrier in-and-out parity
• Graphical presentation of an up-and-out and up-and-in parity
• Pricing lookback options with floating strikes
• Using the Sobol sequence to improve the efficiency
Volatility Measures and GARCH
• Conventional volatility measure – standard deviation
• Tests of normality
• Estimating fat tails
• Lower partial standard deviation
• Test of equivalency of volatility over two periods
• Test of heteroskedasticity, Breusch, and Pagan (1979)
• Retrieving option data from Yahoo! Finance
• Volatility smile and skewness
• Graphical presentation of volatility clustering
• The ARCH model
• Simulating an ARCH (1) process
• The GARCH (Generalized ARCH) model
• Simulating a GARCH process
• Simulating a GARCH (p,q) process using modified garchSim()
• GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993)

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