NLP: Answer the questions by reading the notes after the questions (i.e. by reading the paper if available online)

Answer the questions by reading the notes after the questions (i.e. by reading the paper if available online):

Can you use RNN-LSTM for Text Classification?
Can you use RNN-LSTM for Text Summarization?
Can you use CNN for Text Classification?
Can you use CNN for Text Classification?
What does CNN stand for?
What does RNN stand for?
What does CNN-LSTM stand for?
What is more used in NLP tasks: CNN or RNN-LSTM?
What are the types of CNN?
What is CNN-Static?
What is CNN-Multichannel?
What is Glove?
What is Word2Vec?
How does text-summarization by RNN-LSTM compare with human made summaries?
How can you compare the quality of summarization between summary created by RNN-LSTM and human made summaries?
Which method provided better result for text classification (CNN or RNN-LSTM) according to this paper?
What is text encoding?
Can you encode text with your own method?
What will you prefer between Glove and Word2Vec? Why and When?
What are the different types of text encoders available?
What is special about RNN than simple NN?
What is Gated RNN?
How does Gated RNN compare with Simple RNN for text classification?

Resources: Cisco Security Certifications

Data sheets

Sayed Ahmed

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Resources including Research papers for Dietary pattern and Kidney Diseases

Resources including Research papers for Dietary pattern and Kidney Diseases

Also, includes topics related to data analysis

[1] The National Institute of Diabetes and Digestive and Kidney Diseases. What Is Chronic Kidney Disease? https://www.niddk.nih.gov/health-information/kidney-disease/chronic-kidney-disease-ckd/what-is-chronic-kidney-disease

[2] Jaimon T. K., Suetonia C. P., Shu N. W., Marinella R., Juan-Jesus C., Katrina L. C., and Giovanni F. M. S. Healthy Dietary Patterns and Risk of Mortality and ESRD in CKD: A Meta-Analysis of Cohort Studies

[3] Chen X, Wei G, Jalili T, Metos J, Giri A, Cho ME, Boucher R, Greene T, Beddhu S: The associations of plant protein intake with all-cause mortality in CKD. Am J Kidney Dis 67: 423–430, 2016 (26)

[4] Gutierrez O.M., Muntner P, Rizk D.V., McClellan W.M., Warnock D.G., Newby P.K., Judd S.E.: Dietary patterns and risk of death and progression to ESRD in individuals with CKD: A cohort study. Am J Kidney Dis 64: 204–213, 2014 (27)

[5] Huang X., Jime nez-Moleo J. J., Lindholm B., Cederholm T., Arnoldv J., Rise rus U., Sjogren P., Carrero J. J.: Mediterranean diet, kidney function, and mortality in men with CKD. Clin J Am Soc Nephrol 8: 1548–1555, 2013 (28)

[6] Muntner P, Judd S.E., Gao L, Gutierrez O.M., Rizk D.V., McClellanW., Cushman M., Warnock D.G.: Cardiovascular risk factors in CKD associated with both ESRD and mortality. J Am Soc Nephrol 24: 1159–1165, 2013 (29)

[7] Ricardo A.C., Madero M., Yang W., Anderson C., Menezes M., Fischer M.J., Turyk M., Daviglus M.L., Lash J.P.: Adherence to a healthy lifestyle and all-cause mortality in CKD. Clin J Am Soc Nephrol 8: 602–609, 2013 (30)

[8] Tsuruya K., Fukuma S., Wakita T., Ninomiya T., Nagata M., Yoshida H., Fujimi S., Kiyohara Y., Kitazono T., Uchida K., Shirota T., Akizawa T., Akiba T., Saito A., Fukuhara S.: Dietary patterns and clinical outcomes in hemodialysis patients in Japan: A cohort study. PLoS One 10: e0116677, 2015 (31)

[9] Ricardo A.C., Anderson C.A., Yang W., Zhang X., Fischer M. J., Dember L. M., Fink J. C., Frydrych A., Jensvold N. G., Lustigova E., Nessel L. C., Porter A. C., Rahman M., Wright Nunes J. A., Daviglus M. L., Lash J. P.; CRIC Study Investigators: Healthy lifestyle and risk of kidney disease progression, atherosclerotic events, and death in CKD: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis 65: 412–424, 2015 (17)

[10] Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey. https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Dietary.

[11] Health.gov. Shifts Needed to Align With Healthy Eating Patterns. https://health.gov/

dietaryguidelines/2015/guidelines/chapter-2/a-closer-look-at-current-intakes-and-recommended-shifts/

[12] Agricultural Research Service (ARS), USDA. Food Code Numbers and the Food Coding Scheme.

https://reedir.arsnet.usda.gov/codesearchwebapp/(gcp3kq55ssdyc445ry2k2rus\)/coding\_scheme.pdf

[13] The U.S. Department of Agriculture’s (USDA). Vegetable Subgroups. http://www.cn.nysed.gov/common/cn/files/Vegetable\%20Subgroups.pdf

[14] Centers for Disease Control and Prevention. Key Concepts About the USDA Food Coding Scheme. https://www.cdc.gov/nchs/tutorials/Dietary/SurveyOrientation/ResourceDietaryAnalysis/Info2.htm

[15] Health.gov. Appendix 3. USDA Food Patterns: Healthy U.S.-Style Eating Pattern. https://health.gov/dietaryguidelines/2015/guidelines/appendix-3/

[16] United states Renal Data System (USRDS). The 2018 USRDS Annual Data Report Reference Tables: https://www.usrds.org/reference.aspx

[17] United states Renal Data System (USRDS). 2018 ADR Chapters. https://www.usrds.org/2018/view/Default.aspx

[18] ARS, USDA. Food Surveys Research Group: Beltsville, MD . Documentation and Dataset: https:

//www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/wweia-documentation-and-data-setsY

[19] United states Renal Data System (USRDS). 2018 USRDS Annual Data Report: Executive Summary. https://www.usrds.org/2018/download/v1\_00\_ExecSummary\_18.pdf

[20] Health.gov, CDC. Dietary Guidelines for Americans 2015-2020. https://health.gov/dietaryguidelines/2015/resources/2015-2020\_dietary\_guidelines.pdf

[21] Health.gov, CDC. Dietary Guidelines for Americans. https://health.gov/dietaryguidelines/dga95/9DIETGUI.HTM

[22] Mortality and Causes of Death. https://www.usrds.org/2018/ref/ESRD\_Ref\_H\_Mortality\_2018.xlsx

[23] Tong A, Chando S, Crowe S, Manns B, Winkelmayer WC, Hemmelgarn B, Craig JC: Research priority setting in kidney disease: A systematic review. Am J Kidney Dis 65: 674–683, 2015

[24] Lin J, Fung TT, Hu FB, Curhan GC: Association of dietary patterns with albuminuria and kidney function decline in older white women: A subgroup analysis from the Nurses’ Health Study. Am J Kidney Dis 57: 245–254, 2011

[25] Taylor EN, Fung TT, Curhan GC: DASH-style diet associates with reduced risk for kidney stones.J Am Soc Nephrol 20: 2253–2259, 2009

[26] Liu, Hao-Wen; Tsai, Wen-Hsin; Liu, Jia-Sin; Kuo, Ko-Lin. 2019. "Association of Vegetarian Diet with Chronic Kidney Disease." Nutrients 11, no. 2: 279.

[27] Golaleh Asghari, Mehrnaz Momenan, Emad Yuzbashian, Parvin MirmiranEmail author and Fereidoun Azizi. Dietary pattern and incidence of chronic kidney disease among adults: a population-based study

[28] Tanushree Banerjee1 , Deidra C. Crews2 , Delphine S. Tuot3 , Meda E. Pavkov4 , Nilka Rios Burrows4 , Austin G. Stack5 , Rajiv Saran6,7 , Jennifer Bragg-Gresham6 and Neil R. Powe1,8 ; for the Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team9 Poor accordance to a DASH dietary pattern is associated with higher risk of ESRD among adults with moderate chronic kidney disease and hypertension

[29] Jacek R., Beata F., Aleksandra C., Anna G.The Effect of Diet on the Survival of Patients with Chronic Kidney Disease. Nutrients 2017, 9(5), 495; https://doi.org/10.3390/nu9050495

[30] National Kidney Foundation. One in Seven American Adults Estimated to Have Chronic Kidney

Disease. https://www.kidney.org/news/one-seven-american-adults-estimated-to-have-chronic-kidney-disease

[31] Hannah N. What are the leading causes of death in the US?. https://www.medicalnewstoday.com/articles/282929.php

[32] Davita. Five Stages of CKD. https://www.davita.com/education/kidney-disease/stages

[33] Goal: How to Identify the Most Important Predictor Variables in Regression Models https ://blog. minitab.com/blog/adventures-in-statistics-2/how-to-identify-the-most-important-predictor-variables-in-regression-models

[34] How to Interpret Regression Analysis Results: P-values and Coefficients https://blog.minitab.

com/blog/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-values-and-coefficients

[35] Regression Analysis: How to Interpret the Constant (Y Intercept) https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-to-interpret-the-constant-y-intercept

[36] How to Compare Regression Slopes: How to statistically test the difference between regression slopes and constants https://blog.minitab.com/blog/adventures-in-statistics-2/how-to-compare-regression-lines-between-different-models

[37] How Do I Interpret R-squared and Assess the Goodness-of-Fit? https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit

[38] How High Should R-squared Be in Regression Analysis? https://blog.minitab.com/blog/adventures-in-statistics-2/how-high-should-r-squared-be-in-regression-analysis

[39] How to Interpret a Regression Model with Low R-squared and Low P values https://blog.minitab

.com/blog/adventures-in-statistics-2/how-to-interpret-a-regression-model-with-low-r-squared-and-low-p-values

[40] Use Adjusted R-Squared and Predicted R-Squared to Include the Correct Number of Variables https://blog.minitab.com/blog/adventures-in-statistics-2/multiple-regession-analysis-use-adjusted-r-squared-and-predicted-r-squared-to-include-the-correct-number-of-variables

[41] How to Interpret S, the Standard Error of the Regression https://blog.minitab.com/blog/

adventures-in-statistics-2/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression

[42] What Is the F-test of Overall Significance in Regression Analysis?. https://blog.minitab.com/blog/adventures-in-statistics-2/what-is-the-f-test-of-overall-significance-in-regression-analysis

[43] Understanding Analysis of Variance (ANOVA) and the F-test. https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-analysis-of-variance-anova-and-the-f-test

[44] How to Compare Regression Slopes. https://blog.minitab.com/blog/adventures-in-

statistics-2/how-to-compare-regression-lines-between-different-models

[45] How to Present and Use the Results to Avoid Costly Mistakes, part 1. https://blog.

minitab.com/blog/adventures-in-statistics-2/applied-regression-analysis-how-to-present-and-use- the-results-to-avoid-costly-mistakes-part-1

[46] How to Identify the Most Important Predictor Variables in Regression Models. https://blog.

minitab.com/blog/adventures-in-statistics-2/how-to-identify-the-most-important-predictor- variables-in-regression-models

[47] How to Interpret your Regression Results http://sitestree.com/how-to-interpret-your-regression-results

[48] Fernandez-Prado R., Esteras R., Perez-Gomez M.V, Gracia-Iguacel C., Gonzalez-Parra E., Sanz A.B., Ortiz A., Sanchez-Nino M.D. Nutrients Turned into Toxins: Microbiota Modulation of Nutrient Properties in Chronic Kidney Disease. Nutrients. 2017 May; 9(5): 489. Published online 2017 May 12. doi: 10.3390/nu9050489 PMCID: PMC5452219 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5452219/

[49] Hsu, Y. H., Pai, H. C., Chang, Y. M., Liu, W. H., Hsu, C. C. (2013). Alcohol consumption is inversely associated with stage 3 chronic kidney disease in middle-aged Taiwanese men. BMC nephrology, 14, 254. doi:10.1186/1471-2369-14-254

[50] Rippe, J. M., Angelopoulos, T. J. (2016). Relationship between Added Sugars Consumption and Chronic Disease Risk Factors: Current Understanding. Nutrients, 8(11), 697. doi:10.3390/nu8110697

[51] Karalius V.P., Shoham D.A. Dietary sugar and artificial sweetener intake and chronic kidney disease: a review. Adv Chronic Kidney Dis. 2013; 20:157–164. doi: 10.1053/j.ackd.2012.12.005

[52] Jaimon T. Kelly, Suetonia C. Palmer, Shu Ning Wai, Marinella Ruospo, Juan-Jesus Carrero, Katrina L. Campbell and Giovanni F. M. Strippoli Healthy Dietary Patterns and Risk of Mortality and ESRD in CKD: A Meta-Analysis of Cohort Studies. CJASN February 2017, 12 (2) 272-279; DOI: https://doi.org/10.2215/CJN.06190616

[53] Jilian k. The 20 Best Foods for People With Kidney Problems. https://www.healthline.com/nutrition/best-foods-for-kidneys

[54] National Kidney Foundation (NKF), USA. Drinking Alcohol Affects Your Kidneys https://www.kidney.org/news/kidneyCare/winter10/AlcoholAffects

[55] Uehara, S., Hayashi, T., Kogawa Sato, K., Kinuhata, S., Shibata, M., Oue, K., Hashimoto, K. (2016). Relationship Between Alcohol Drinking Pattern and Risk of Proteinuria: The Kansai Healthcare Study. Journal of epidemiology, 26(9), 464–470. doi:10.2188/jea.JE20150158

[56] Nettleton, J. A., Steffen, L. M., Palmas, W., Burke, G. L., \& Jacobs, D. R., Jr (2008). Associations between micro albuminuria and animal foods, plant foods, and dietary patterns in the Multiethnic Study of Atherosclerosis. The American journal of clinical nutrition, 87(6), 1825–1836. doi:10.1093/ajcn/87.6.1825

[57] Jacobs, D. R., Jr, Gross, M. D., Steffen, L., Steffes, M. W., Yu, X., Svetkey, L. P., Sacks, F. (2009). The effects of dietary patterns on urinary albumin excretion: results of the Dietary Approaches to Stop Hypertension (DASH) Trial. American journal of kidney diseases : the official journal of the National Kidney Foundation, 53(4), 638–646. doi:10.1053/j.ajkd.2008.10. 048. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2676223/

[58] National Kidney Foundation. 37 Million American Adults Now Estimated to Have Chronic Kidney Disease. https://www.kidney.org/news/37-million-american-adults-now-estimated-to-have-chronic-kidney-disease

Sayed Ahmed

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Blog: http://bangla.saLearningSchool.com http://SitesTree.com,
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Some Research on CKD Disease, Chronic Kidney Disease

Some Research on CKD Disease, Chronic Kidney Disease. Just to keep track

These are research i.e. studies. These are not to be interpreted by everyone; however, only for the people who have the background to interpret. There should be 1000s of studies or 100’s of 1000s studies on this and related topic. Hence, do not study these and apply to yourselves.

  1. http://www.ncbi.nlm.nih.gov/pubmed/18262740
  2. http://www.ncbi.nlm.nih.gov/pubmed/19146934
  3. http://www.ncbi.nlm.nih.gov/pubmed/9255718
  4. http://www.ncbi.nlm.nih.gov/pubmed/17619305
  5. http://www.ncbi.nlm.nih.gov/pubmed/19545680
  6. http://www.ncbi.nlm.nih.gov/pubmed/2045012
  7. http://www.ncbi.nlm.nih.gov/pubmed/20951192
  8. http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZGTL200002007.htm
  9. http://umm.edu/health/medical/altmed/herb/goldenrod
  10. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2736774/
  11. http://www.ncbi.nlm.nih.gov/pubmed/?term=Couch+grass+kidney
  12. http://www.ncbi.nlm.nih.gov/pubmed/?term=goldenrod+kidney
  13. http://www.ncbi.nlm.nih.gov/pubmed/15339033
  14. http://www.ncbi.nlm.nih.gov/pubmed/21190603
  15. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062120/
  16. http://www.ncbi.nlm.nih.gov/pubmed/21505983
  17. http://www.ncbi.nlm.nih.gov/pubmed/22142357
  18. http://www.ncbi.nlm.nih.gov/pubmed/22434410
  19. http://www.sciencedirect.com/science/article/pii/S0144861712007357
  20. http://www.ncbi.nlm.nih.gov/pubmed/22455126
  21. http://www.ncbi.nlm.nih.gov/pubmed/22760215
  22. http://www.ncbi.nlm.nih.gov/pubmed/22944441
  23. http://www.ncbi.nlm.nih.gov/pubmed/2335959
  24. http://www.ncbi.nlm.nih.gov/pubmed/26237835
  25. http://www.ncbi.nlm.nih.gov/pubmed/26503560
  26. http://www.ncbi.nlm.nih.gov/pubmed/26712211
  27. http://www.sciencedirect.com/science/article/pii/S0378874113008222
  28. http://www.umm.edu/altmed/articles/horsetail-000257.htm
  29. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2695282/
  30. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177777/
  31. https://www.ncbi.nlm.nih.gov/pubmed/11887407
  32. https://www.ncbi.nlm.nih.gov/pubmed/15638071
  33. https://www.ncbi.nlm.nih.gov/pubmed/2335959
  34. https://www.ncbi.nlm.nih.gov/pubmed/24353832
  35. https://www.ncbi.nlm.nih.gov/pubmed/25172798
  36. https://www.ncbi.nlm.nih.gov/pubmed/25674203
  37. https://www.ncbi.nlm.nih.gov/pubmed/26612737

Sayed Ahmed

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://bangla.saLearningSchool.com http://SitesTree.com,
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USA and Canada Tax Treaty

Instructions for Completing Internal Revenue Service Tax Forms for Royalty Payments

https://www.upenn.edu/pennpress/about/taxforms.html

Instructions for Completing Internal Revenue Service Tax Forms for Royalty Payments

https://www.millerthomson.com/en/publications/communiques-and-updates/tax-notes/april-2013/overview-of-limitation-on-benefits-article-in/

Sayed Ahmed

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://bangla.saLearningSchool.com http://SitesTree.com,
Online and Offline Training: http://training.SitesTree.com

OOP concepts in PHP 5 in brief

OOP concepts in PHP 5 in brief

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OOP concepts in PHP 5 in brief

OOP concepts in PHP 5 in short

Why this short — note? if you are familiar with OOD and any OOP language such as Java/C++, this short note will give you enough information to start with PHP 5 OOP Class

  • Class definition starts with the keyword class, followed by a class name (non reserved word), followed by a pair of curly braces. The curly braces contain the definition of the classes members and methods
  • You can create objects based on the classes. $obj = new className()
  • You use $obj->methodName() to access a class method (public). You can use className::classMember to access class members (static): to use :: operator the method does not need to be declared static
  • Inside a class all class methods have access to $this variable to refer to the calling object (if called from/using an object)
  • member declaration: public $var = ‘a default value
  • Default value always is: constant expression
  • Class/Object Functions
  • A class can use extends keyword to inherit methods and members of another class
  • Multiple inheritance is not allowed
  • To avoid using a long list of includes in the beginning of php files, you can use __autoload() function to do the job for you
  • When you try to use an undefined class/interface an __autoload function is automatically called
  • function __autoload($class_name) {
    require_once $class_name . ‘.php’;
    }
  • Constructor syntax: void __construct ([ mixed $args [, $… ]] )
  • Parents’ constructors are not automatically called from children’s constructor. use explicit parent::__construct() instead
  • Destructor syntax: void __destruct ( void )
  • Destructor is called: 1. all references to the object are removed 2. the object is explicitly destroyed 3. in shutdown sequence
  • Parents’ destructors are not automatically called from children’s destructors. use explicit parent::__destruct() instead
  • Access modifiers for class members: public, protected or private: Public — accessible from anywhere. Protected — accessible from inherited and parent classes, within the class. Private — accessible within the class
  • No access modifier = public
  • :: — scope resolution operator — allows access to static, constant, and overridden members or methods of a class
  • Abstract classes: Introduced in PHP 5. You are not allowed to reate an instance of an abstract class.
  • Even if a class contains one abstract method, the class must bedeclared abstract
  • Abstract classes are just about signatures, they cannot define the implementation
  • A class inheriting from an abstract class, must have to implement all abstract methods. The abstract methods must be defined with the same/(less restricted) visibility
  • Interface: Just the method signatures. No method implementation inside interfaces
  • All interface methods must be public
  • Classes implementing interfaces must implement all methods. Classes use implements keyword to implement an interface
  • A class can not implement two interfaces having same class names
  • Interfaces can be extended using extends keyword
  • Interfaces can also have constants
  • Overloading: Overloading in PHP = dynamically “create” members and methods
  • overloading methods: invoked when interacting with non-declared/invisible members or methods
  • All overloading methods must be defined as public
  • In PHP, overloading is done through magic methods
  • The arguments of the magic methods can not be ‘passed by reference’
  • Member overloading methods: void __set ( string $name , mixed $value ), mixed __get ( string $name ), bool __isset ( string $name ), void __unset ( string $name )
  • Method overloading: mixed __call ( string $name , array $arguments ), mixed __callStatic ( string $name , array $arguments )
  • Object Iteration: Inside the class
  • foreach($this as $key => $value) {
    print “$key => $valuen”;
    }
  • Object Iteration: Outside class:
  • $class = new MyClass();
    foreach ($class as $key => $value) {
    print “$key => $valuen”;
    }
  • Patterns: Factory Pattern: allows the instantiation of objects at runtime
  • Patterns: Singleton: Helps in situations where only a single instance of a class is required that will be used by many other objects
  • Magic methods: have special meaning. __construct, __destruct (see Constructors and Destructors), __call, __callStatic, __get, __set, __isset, __unset, __sleep, __wakeup, __toString, __set_state and __clone
  • serialize() — applies to __sleep(). unserialize() applies to __wakeup()
  • final keyword: final members can not be overriden, final classes can not be extended
  • $copy_of_object = clone $object; : will create a clone of $object. Unless a __clone method defined, a shadow is created. __clone() method can define how the cloning will be done
  • Objects Comparison: == : two object instances are equal if they have the same attributes and values, and are instances of the same class.
  • Objects Comparison: === : Object variables are identical if and only if they refer to the same instance of the same class
  • Reflection APIs: to reverse-engineer classes, interfaces, functions and methods, extensions
  • Reflection APIs: Offer ways to retrieve doc comments for functions, classes and methods
  • Type Hinting: Functions can enforce parameters to be objects:
  • Late Static Bindings: to refer the called class in a context of static inheritance.

Sayed Ahmed

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://bangla.saLearningSchool.com http://SitesTree.com,
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PHP SQL Server Stored Procedure

/* prepare the statement resource */
$stmt=mssql_init("your_stored_procedure", $conn);

/* now bind the parameters to it */
mssql_bind($stmt, "@id", $id, SQLINT4, FALSE);
mssql_bind($stmt, "@name", $name, SQLVARCHAR, FALSE);
mssql_bind($stmt, "@email", $email, SQLVARCHAR, FALSE);

/* now execute the procedure */
$result = mssql_execute($stmt);

Another Example

$conn = mssql_connect($db_host,$db_user,$db_password);if ($conn===false){
echo 'Cannot connect.';
exit;
}

if (mssql_select_db("YourDatabase",$conn) === false) {
echo 'no database';
exit;
}

$proc = mssql_init('YourStoredProcedure',$conn);
mssql_bind($proc,'@ParameterOne',$ParameterOne,SQLVARCHAR);
mssql_bind($proc,'@ParameterTwo',$ParameterTwo,SQLVARCHAR);
mssql_bind($proc,'@ParameterThree',$ParameterThree,SQLVARCHAR);
if ($result = mssql_execute($proc)) {
if ($row = mssql_fetch_row($result)){
// process results
}
}

Sayed Ahmed

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://bangla.saLearningSchool.com http://SitesTree.com,
Online and Offline Training: http://training.SitesTree.com

Important Basic Concepts: Statistics for Big Data

Important Basic Concepts: Statistics for Big Data

Graphical : Exploratory Data Analysis (EDA) methods?
First of all, EDA is about exploring the data and understanding if the data will be good for the experiment and study. Graphs and plots can easily show the data patterns. The raw data can be difficult to understand for patterns and fitness, Graphs can easily show some information about the data.

Graphical Methods can be as follows:
1. Scatter Plots
2. Histograms
3. Box Plots
4. Normal Probability plots

Quantitative Exploratory Data Analysis Techniques:

1. Interval Estimation (Ranges)
2. Hypothesis testing (Null Hypothesis, Alternate Hypothesis)

1. Interval Estimation (Ranges): Create a range of values within which a variable is likely to fall. Confidence Interval (mean will be here) is an interval estimation.

2. Hypothesis testing: Test various propositions about a data

Example: Test that the mean age of Canadian Population is 53.

It’s a multi-step process. Steps can be as follows:

1. Test Null Hypothesis: Assume the Hypothesis is true
2. Alternate Hypothesis: Hypothesis that will be accepted if the null hypothesis is rejected
3. Significance Level: what level of significance the null hypothesis will be conducted (i.e. 95% of the time the average return of index investing is 6% for 10 years period)
4. Test Statistic: Numerical measure showing sample data is consistent with Null Hypothesis
6. Critical Value: If test statistic (numerical measure) is more extreme than critical value – null hypothesis is rejected
7. Decision: decision is made by considering Test Statistic and Critical value

Some Basic Probability Distributions:

Binomial Distribution: When the variable can have only one of two values

Poisson Distribution: Describe the likelihood of given number of events occurring during a time interval (customers to your shop in an hour)

Normal Distribution: Symmetrical data. probability that a variable will have a given distance from the mean on both lower and higher side is equal.

t distribution: Similar to Normal Distribution. Extreme large or extreme low values are highly likely. Shows too much variance. Useful when the sample size is small (it is also told when there is not variance, standard deviation)

Chi Square Test: Test to see if a population follows a particular distribution such as normal distribution.

The F distribution: To test if two datasets are from the same population (by using variances).

Related Concepts:

What is Z Score?
Probability of a particular score to be occurring in our normal distribution.
Helps to compare two values that are from two different normal distributions

Another definition: it is a measure on how a value is related to the mean.

Chi Square test for Normal Distribution:
Null Hypothesis: No relation exists between categorical variables. They are independent. If the Hypothesis is true, it is a normal distribution

What is p value in Chi Square test:
p value is just a significance. Helps to understand the significance of the result. A small p value means a strong evidence against the Null Hypothesis.

Reference: Anderson A., Semmelroth D., Statistics for Big Data

Sayed Ahmed

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://sitestree.com, http://bangla.salearningschool.com

Questions Answered by Exploratory Data Analysis (EDA)

Questions Answered by Exploratory Data Analysis (EDA)

What are the key properties of a Dataset (Center, Spread, Skew, probability distribution, correlation, outliers)

1. What is the center of the data (mean, median, mode)
2. How much spread is there in the data? (Variance, Standard deviation, Quartiles, Interquartile Range (IQR), Example: IQR = Q3 – Q1)
3. Is the data skewed? : Mean > Median = Positive, Mean = Median = Symmetrical, Mean < median = Negatively skewed
4. What distribution does the data follows? Is the data Normally distributed?
5. Are the elements in the Dataset uncorrelated? i.e. two variable move positively or negatively together or not; linearly or non-linearly or not
6. Does the center of the data change over time? Example: for time series data, does the mean change over time?
7. Does the spread of the dataset Change over time? Example: for time series data, does the variance change over time?
8. Are there outliers in the data?
9. Does the data conform to your assumptions? Normally Distributed, constant parameter, no outliers, close to normally distributed, members are independent or nearly independent, variance increases over time, or several outliers are there in the data

Reference: Anderson A., Semmelroth D., Statistics for Big Data


Sayed Ahmed

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://sitestree.com, http://bangla.salearningschool.com

Best Practices in Data Preparation

Best Practices in Data Preparation

1. Check data formats (Image, CSV, PC, Mac, mainframe, text, structured, unstructured)
2. Verify data types (numbers, text, floats, currencies, nominal, ordinal, interval, range)
3. Graph your Data (Scatter, Histogram, bar, line)
4. Verify the data (data accuracy, data makes sense)
5. Identify outliers ( Examples: very large or very small (than the rest))
6. Deal with missing values
7. Check your assumptions on data distribution (normal, poisson )
8. Backup and document – everything that you do

Reference: Anderson A., Semmelroth D.
Sayed Ahmed

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://sitestree.com, http://bangla.salearningschool.com