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Collection of SQL queries with Answer and Output Set 2

Here is a collection or a list of 30 SQL Queries with Answers as well as output. You can write your answer at the text box below each query any time you can see the table structure by clicking on Table Structure. And check your Answer by clicking on Answer. You can test your Skill in SQL. You can also go for an online Quiz in SQL in one of my previous posts: Click here for Quiz. More queries will be added to this post within few days, visit again!!!

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SQL Query collection: Set1 Set2 Set3 Set 4

Below is the Table Structure using which you have to form the queries:


1) Display THE NUMBER OF packages developed in EACH language.

Table Structure

Write your answer hereAnswer

SELECT DEV_IN AS LANGUAGE,COUNT(TITLE) AS NOOFPACK
FROM SOFTWARE
GROUP BY DEV_IN

2) Display THE NUMBER OF packages developed by EACH person.

Table Structure

Write your answer hereAnswer

SELECT NAME AS PRNAME,COUNT(TITLE)AS NOOFPACK
FROM SOFTWARE
GROUP BY NAME

3) Display THE NUMBER OF male and female programmer.

Table Structure

Write your answer hereAnswer

SELECT SEX,COUNT(NAME) AS NAME
FROM PROGRAMMER
GROUP BY SEX

4) Display THE COSTLIEST packages and HIGEST selling developed in EACH language.

Table Structure

Write your answer hereAnswer

SELECT DEV_IN AS LANGAUGE,MAX(SCOST) AS COSTPACK,MAX(SOLD) AS HIGHPACK
SFROM SOFTWARE
GROUP BY DEV_IN

5) Display THE NUMBER OF people BORN in EACH YEAR.

SELECT TO_CHAR(DOB,YY) AS YEAR,COUNT(NAME) AS PRNO
FROM PROGRAMMER
GROUP BY TO_CHAR(DOB,YY)

Table Structure

Write your answer hereAnswer

6) Display THE NUMBER OF people JOINED in EACH YEAR.

Table Structure

Write your answer hereAnswer

SELECT TO_CHAR(DOJ,YY) AS YEAR,COUNT(NAME) AS PRNO
FROM PROGRAMMER
GROUP BY TO_CHAR(DOJ,YY)

7) Display THE NUMBER OF people BORN in EACH MONTH.

Table Structure

Write your answer hereAnswer

SELECT SUBSTR(DOB,4,3) AS MONTHOFBIRTH,COUNT(NAME) AS PRNO FROM PROGRAMMER
GROUP BY SUBSTR(DOB,4,3)

8) Display THE NUMBER OF people JOINED in EACH MONTH.

Table Structure

Write your answer hereAnswer

SELECT SUBSTR(DOJ,4,3) AS MONTHOFJOIN,COUNT(NAME) AS PRNO
FROM PROGRAMMER
GROUP BY SUBSTR(DOJ,4,3)

9) Display the language wise COUNTS of prof1.

Table Structure

Write your answer hereAnswer

SELECT PROF1 AS LANGUAGE, COUNT(PROF1) AS PROF1COUNT
FROM PROGRAMMER
GROUP BY PROF1

10) Display the language wise COUNTS of prof2.

Table Structure

Write your answer hereAnswer

SELECT PROF2 AS LANGUAGE, COUNT(PROF2) AS PROF2COUNT
FROM PROGRAMMER
GROUP BY PROF2

11) Display THE NUMBER OF people in EACH salary group.

Table Structure

Write your answer hereAnswer

SELECT SALARY,COUNT(NAME) AS PEOPLE
FROM PROGRAMMER
GROUP BY SALARY

12) Display THE NUMBER OF people who studied in EACH institute.

Table Structure

Write your answer hereAnswer

SELECT SPLACE AS INSTITUTE,COUNT(NAME) AS PEOPLE
FROM STUDIES
GROUP BY SPLACE

13) Display THE NUMBER OF people who studied in EACH course.

Table Structure

Write your answer hereAnswer

SELECT COURSE AS STUDY,COUNT(NAME) AS PEOPLE
FROM STUDIES GROUP BY COURSE

14) Display the TOTAL development COST of the packages developed in EACH language.

Table Structure

Write your answer hereAnswer

SELECT DEV_IN AS LANGUAGE,SUM(DCOST) AS TOTCOST
FROM SOFTWARE
GROUP BY DEV_IN

15) Display the selling cost of the package developed in EACH language.

Table Structure

Write your answer hereAnswer

SELECT DEV_IN AS LANGUAGE,SUM(SCOST) AS SELLCOST
FROM SOFTWARE
GROUP BY DEV_IN

16) Display the cost of the package developed by EACH programmer.

Table Structure

Write your answer hereAnswer

SELECT NAME AS PRNAME,SUM(DCOST) AS TOTCOST
FROM SOFTWARE
GROUP BY NAME

17) Display the sales values of the package developed in EACH programmer.

Table Structure

Write your answer hereAnswer

SELECT NAME AS PRNAME, SUM(SCOST*SOLD) AS SALESVAL
FROM SOFTWARE
GROUP BY NAME

18) Display the NUMBER of packages developed by EACH programmer.

Table Structure

Write your answer hereAnswer

SELECT NAME AS PRNAME,COUNT(TITLE) AS TOTPACK
FROM SOFTWARE
GROUP BY NAME

19) Display the sales COST of packages developed by EACH programmer language wise.

Table Structure

Write your answer hereAnswer

SELECT SUM(SCOST) AS SELLCOST
FROM SOFTWARE
GROUP BY DEV_IN

20) Display EACH programmers name, costliest package and cheapest packages developed by Him/Her.

Table Structure

Write your answer hereAnswer

SELECT NAME PRNAME,MIN(DCOST) CHEAPEST,MAX(DCOST) COSTLIEST
FROM SOFTWARE
GROUP BY NAME

21) Display EACH language name with AVERAGE development cost, AVERAGE cost, selling cost and AVERAGE price per copy.

Table Structure

Write your answer hereAnswer

SELECT DEV_IN AS LANGUAGE,AVG(DCOST) AS AVGDEVCOST,AVG(SCOST) AS AVGSELLCOST,AVG(SCOST) AS PRICEPERCPY
FROM SOFTWARE
GROUP BY DEV_IN

22) Display EACH institute name with NUMBER of courses, AVERAGE cost per course.

Table Structure

Write your answer hereAnswer

SELECT SPLACE AS INSTITUTE,COUNT(COURSE) AS NOOFCOURS,AVG(CCOST) AS AVGCOSTPERCOUR
FROM STUDIES
GROUP BY SPLACE

23) Display EACH institute name with NUMBER of students.

Table Structure

Write your answer hereAnswer

SELECT SPLACE AS INSTITUTE,COUNT(NAME) AS NOOFSTUD
FROM STUDIES
GROUP BY SPLACE

24) Display names of male and female programmers.

Table Structure

Write your answer hereAnswer

SELECT NAME AS PRNAME,SEX AS SEX
FROM PROGRAMMER
ORDER BY SEX

25) Display the programmers name and their packages.

Table Structure

Write your answer hereAnswer

SELECT NAME AS PRNAME,TITLE AS PACKAGE
FROM SOFTWARE
ORDER BY NAME

26) Display the NUMBER of packages in EACH language.

Table Structure

Write your answer hereAnswer

SELECT COUNT(TITLE) AS NOOFPACK,DEV_IN AS LANGUAGE
FROM SOFTWARE
GROUP BY DEV_IN

27) Display the NUMBER of packages in EACH language for which development cost is less than 1000.

Table Structure

Write your answer hereAnswer

SELECT COUNT(TITLE) AS NOOFPACK,DEV_IN AS LANGUAGE
FROM SOFTWARE
WHERE DCOST<1000 GROUP BY DEV_IN

28) Display the AVERAGE difference BETWEEN scost and dcost for EACH language.

Table Structure

Write your answer hereAnswer

SELECT DEV_IN AS LANGUAGE,AVG(DCOST - SCOST) AS DIFF
FROM SOFTWARE
GROUP BY DEV_IN

29) Display the TOTAL scost, dcsot and amount TOBE recovered for EACH programmer for whose dcost HAS NOT YET BEEN recovered.

Table Structure

Write your answer hereAnswer

SELECT SUM(SCOST), SUM(DCOST), SUM(DCOST-(SOLD*SCOST))
FROM SOFTWARE
GROUP BY NAME
HAVING SUM(DCOST)>SUM(SOLD*SCOST)

30) Display highest, lowest and average salaries for THOSE earning MORE than 2000.

Table Structure

Write your answer hereAnswer

SELECT MAX(SALARY), MIN(SALARY), AVG(SALARY)
FROM PROGRAMMER
WHERE SALARY > 2000

SQL Query collection: Set1 Set2 Set3 Set 4

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The Computer Science Pipeline and Diversity Part 2 Some positive signs and looking towards the future

Posted by Maggie Johnson, Director of Education and University Relations, Google

(Cross-posted on the Google for Education Blog)

The disparity between the growing demand for computing professionals and the number of graduates in Computer Science (CS) and Information Technology (IT) has been highlighted in many recent publications. The tiny pipeline of diverse students (women and underrepresented minorities (URMs)) is even more troubling. Some of the factors causing these issues are:

• The historical lack of STEM (Science, Technology, Engineering and Mathematics) capabilities in our younger students; lack of proficiency has had a substantial impact on the overall number of students pursuing technical careers. (PCAST Stem Ed report, 2010)
• On the lack of girls in computing, boys often come into computing knowing more than girls because they have been doing it longer. This can cause girls to lose confidence with the perception that computing is a man’s world. Lack of role models, encouragement and relevant curriculum are additional factors that discourage girls’ participation. (Margolis 2003)
• On the lack of URMs in computing, the best and most enthusiastic minority students are effectively discouraged from pursuing technical careers because of systemic and structural issues in our high schools and communities, and because of unconscious bias of teachers and administrators. (Margolis, 2010)

Over the last 3-4 years, however, we have seen some significant positive signals in STEM education in general, and in CS/IT in particular.

• Math¹ and Science² results as measured by the National Assessment of Educational Progress (NAEP) have improved slightly since 2009, both in general and for female and minority students.
• Over the last 10 years, there has been an increase in the number of students earning STEM degrees, but the news on women graduates is not as positive.

“Overall, 40 percent of bachelors degrees earned by men and 29 percent earned by women are now in STEM fields. At the doctoral level, more than half of the degrees earned by men (58 percent) and one-third earned by women (33 percent) are in STEM fields. At the bachelors degree level, though, women are losing ground. Between 2004 and 2014, the share of STEM-related bachelors degrees earned by women decreased in all seven discipline areas: engineering; computer science; earth, atmospheric and ocean sciences; physical sciences; mathematics; biological and agricultural sciences; and social sciences and psychology. The biggest decrease was in computer science, where women now earn 18 percent of bachelors degrees (18 percent). In 2004, women earned nearly a quarter of computer science bachelors degrees, at 23 percent.” - (U.S. News, 2015)

• There has been a steady growth in investment in education companies, particularly those focused on innovative uses of technology.

• The number of publications in Google Scholar on STEM education that focus on gender issues or minority students has steadily increased over the last several years.

Results from Google Scholar, using “STEM education minority” and “STEM education gender” as search terms

• Successful marketing campaigns such as Hour of Code and Made with Code have helped raise awareness on the accessibility and importance of coding, and the diverse career opportunities in CS.
• There has been growth in developer bootcamps over the last few years, as well as online “learn to code” programs (code.org, CS First, Khan Academy, Codecademy, Blockly Games, PencilCode, etc.), and an increase in opportunities for K12 students to learn coding in their schools. We have also seen non-profits emerge focused specifically on girls and URMs (Technovation, Girls who Code, Black Girls Code, #YesWeCode, etc.)
• One of the most positive signals has been the growth of graduates in CS over the past few years.

Source: 2013 Taulbee Survey, Computing Research Association

So we are seeing small improvements in K-12 STEM proficiency and undergraduate STEM and CS degrees earned, a significant growth in investment in education innovation, more and more research on the issues of gender and ethnicity in STEM fields and increased opportunities for all students to learn coding skills online, through non-profit programs, through developer boot camps or in their schools.

However, an interesting, and potentially threatening development resulting from this positive momentum is the lack of capacity and faculty in CS departments to handle the increased number of enrollments and majors in CS. Colleges and universities, as a whole, aren’t adequately prepared to handle the surge in CS education demand - Currently there just aren’t enough instructors to teach all the students who want to learn.

This has happened in the past. In the 80’s, with the introduction of the PC, and again during the dot-com boom, interest in CS surged. CS departments managed the load by increasing class sizes as much as they possibly could, and/or they put enrollment caps in place and made CS classes harder. The effect of the former was some faculty left for industry while the effect of the latter was a decrease in the diversity pipeline.

“These kinds of caps have two effects which limit access by women and under-represented minorities:

• First, the students who succeed the most in intro CS are the ones with prior experience.
• Second, creating these kinds of caps creates a perception of CS as a highly competitive field, which is a deterrent to many students. Those students may not even try to get into CS.”

-(Guzdial, 2014)

If we allow the past to repeat itself, we may again find CS faculty leaving for industry and less diversity students going into the field. In addition, unlike the dot-com boom where interest in CS plummeted with the bust, it’s unlikely we will see a decrease in enrollments, particularly in the introductory CS courses. “CS+X”, which represents the application of CS in other fields, is illustrated by the following sample list of interdisciplinary majors in various universities:

• Yale: "Computer Science and Psychology is an interdepartmental major..."
• USC: "B.S in Physics/Computer Science for students with dual interests..."
• Stanford: "Mathematical and Computational Sciences for students interested in..."
• Northeastern: "Computer Science/Music Technology dual major for students who want to explore connections between..."
• Lehigh: "BS in Computer Science and Business integrates..."
• Dartmouth: "The M.D.-Ph.D. Program in Computational Biology..."

The number of non-major students taking CS courses, particularly the introductory ones, is growing, which makes the capacity issues worse.

At Google, we recently funded a number of universities via our 3X3 award program (3 times the number of students in 3 years), which aims to facilitate innovative, inclusive, and sustainable approaches to address these scaling issues in university CS programs. Our hope is to disseminate and scale the most successful approaches that our university partners develop. A positive development, which was not present when this happened in the past, is the recent innovation in online education and technology. The increase in bandwidth, high-quality content and interactive learning opportunities may help us get ahead of this challenging capacity issue.

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¹Average mathematics scores for fourth- and eighth-graders in 2013 were 1 point higher than in 2011, and 28 and 22 points higher respectively in comparison to the first assessment year in 1990. Hispanic students made gains in mathematics from 2011 to 2013 at both grades 4 and 8. Fourth- and eighth-grade female students scored higher in mathematics in 2013 than in 2011, but the scores for fourth- and eighth-grade male students did not change significantly over the same period. (Nation’s Report Card)

²The average eighth-grade science score increased two points, from 150 in 2009 to 152 in 2011. Scores also rose among public school students in 16 of 47 states that participated in both 2009 and 2011, and no state showed a decline in science scores from 2009 to 2011. A five-point gain from 2009 to 2011 by Hispanic students was larger than the one-point gain for White students, an improvement that narrowed the score gap between those two groups. Black students scored three points higher in 2011 than in 2009, narrowing the achievement gap with White students. (Nation’s Report Card)

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A PPT Presentation on Web 2 0

Few Points which are covered in this PPT are as fallows:

• Intro to Web
• Terminology
• What is Web 2.0
• History of Web 2.0
• Need for Web 2.0
• What makes the Difference?
• Then & Now
• Current Scenario
• Characteristics
• Web-based applications and desktops
• The Future of Close (web3.0)

Submitted by: Elton Jain
College: KC College, Mumbai

Team Members:

• Elton Jain
• Nayab Shaikh
• Rishabh

Download the ppt file here:

• web2.0 ppt

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Text to Speech for low resource languages episode 2 Building a parametric voice

Posted by Alexander Gutkin, Google Speech Team

This is the second episode in the series of posts reporting on the work we are doing to build text-to-speech (TTS) systems for low resource languages. In the previous episode, we described the crowdsourced data collection effort for Project Unison. In this episode, we describe our work to construct a parametric voice based on that data.

In our previous episode, we described building TTS systems for low resource languages, and how one of the objectives of data collection for such systems was to quickly build a database representing multiple speakers. There are two main justifications for this approach. First, professional voice talents are often not available for under-resourced languages, so we need to record ordinary people who get tired reading tedious text rather quickly. Hence, the amount of text a person can record is rather limited and we need multiple speakers for a reasonably sized database that can be used by others as well. Second, we wanted to be able to create a voice that sounds human but is not identifiable as a real person. Various concatenative approaches to speech synthesis, such as unit selection, are not very suitable for this problem. This is because the selection algorithm may join acoustic units from different speakers generating a very unnatural sounding result.

Adopting parametric speech synthesis techniques is an attractive approach to building multi-speaker corpora described above. This is because in parametric synthesis the training stage of the statistical component will take care of multiple-speakers by estimating an averaged out representation of various acoustic parameters representing each individual speaker. Depending on number of speakers in the corpus, their acoustic similarity and ratio of speaker genders, the resulting acoustic model can represent an average voice that is indistinguishable from human and yet cannot be traced back to any actual speakers recorded during the data collection.

We decided to use two different approaches to acoustic modeling in our experiments. The first approach uses Hidden Markov Models (HMMs). This well-established technique was pioneered by Prof. Keiichi Tokuda at Nagoya Institute of Technology, Japan and has been widely adopted in academia and industry. It is also supported by a dedicated open-source HMM synthesis toolkit. The resulting models are small enough to fit on mobile devices.

The second approach relies on Recurrent Neural Networks (RNNs) and vocoders that jointly mimic the human speech production system. Vocoders mimic the vocal apparatus to provide a parametric representation of speech audio that is amenable to statistical mapping. RNNs provide a statistical mapping from the text to the audio and have feedback loops in their topology, allowing them to model temporal dependencies between various phonemes in human speech. In 2015, Yannis Agiomyrgiannakis proposed Vocaine, a vocoder that outperforms the state-of-the-art technology in speed as well as quality. In 2013, Heiga Zen, Andrew Senior and Mike Schuster proposed a neural network-based model that mimics deep structure of human speech production for speech synthesis. The model has further been extended into a Long Short-Term Memory (LSTM) RNN. This allows long term memorization, which is good for speech applications. Earlier this year, Heiga Zen and Hasim Sak described the LSTM RNN architecture that has been specifically designed for fast speech synthesis. The LSTM RNNs are also used in our Automatic Speech Recognition (ASR) systems recently mentioned in our blog.

Using the Hidden Markov Model (HMM) and LSTM RNN synthesizers described above, we experimented with a multi-speaker Bangla corpus totaling 1526 utterances (waveforms and corresponding transcriptions) from five different speakers. We also built a third system that utilizes LSTM RNN acoustic model, but this time we made it small and fast enough to run on a mobile phone.

We synthesized the following Bangla sentence "??? ???? ????? ??????? ??????" translated from “This is an example sentence in Bangla”. Though HMM synthesizer output can sound intelligible, it does exhibit some classic downsides with a voice that sounds buzzy and muffled. With the LSTM RNN configuration for mobile devices, the resulting audio sounds clearer and has improved intonation over the HMM version. We also tried a LSTM RNN configuration with more network nodes (and thus not suitable for low-end mobile devices) to generate this waveform - the quality is slightly better but is not a huge improvement over the more lightweight LSTM RNN version. We hypothesize that this is due to the fact that a neural network with many nodes has more parameters and thus requires more data to train.

These early results are encouraging for several reasons. First, they confirm that natural-sounding speech synthesis based on multiple speakers is practically possible. It is also significant that the total number of recordings used was relatively small, yet were able to build intelligible parametric speech synthesis. This means that it is possible to collect training data for such a speech synthesizer by engaging the help of volunteers who are not professional voice artists, for a short period of time per person. Using multiple volunteers is an advantage: it results in more diverse data, and the resulting synthetic voice does not represent any specific individual. This approach may well be the foundation for bringing speech technology to many more traditionally under-served languages.

NEXT UP: But can it say, “Google”? (Ep.3)

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Moore’s Law Part 2 More Moore and More than Moore

This is the second entry of a series focused on Moore’s Law and its implications moving forward, edited from a White paper on Moore’s Law, written by Google University Relations Manager Michel Benard. This series quotes major sources about Moore’s Law and explores how they believe Moore’s Law will likely continue over the course of the next several years. We will also explore if there are fields other than digital electronics that either have an emerging Moores Law situation, or promises for such a Law that would drive their future performance.

--

One of the fundamental lessons derived for the past successes of the semiconductor industry comes for the observation that most of the innovations of the past ten years—those that indeed that have revolutionized the way CMOS transistors are manufactured nowadays—were initiated 10–15 years before they were incorporated into the CMOS process. Strained silicon research began in the early 90s, high-?/metal-gate initiated in the mid-90s and multiple-gate transistors were pioneered in the late 90s. This fundamental observation generates a simple but fundamental question: “What should the ITRS do to identify now what the extended semiconductor industry will need 10–15 years from now?”
- International Technology Roadmap for Semiconductors 2012

More Moore
As we look at the years 2020–2025, we can see that the physical dimensions of CMOS manufacture are expected to be crossing below the 10 nanometer threshold. It is expected that as dimensions approach the 5–7 nanometer range it will be difficult to operate any transistor structure that is utilizing the metal-oxide semiconductor (MOS) physics as the basic principle of operation. Of course, we expect that new devices, like the very promising tunnel transistors, will allow a smooth transition from traditional CMOS to this new class of devices to reach these new levels of miniaturization. However, it is becoming clear that fundamental geometrical limits will be reached in the above timeframe. By fully utilizing the vertical dimension, it will be possible to stack layers of transistors on top of each other, and this 3D approach will continue to increase the number of components per square millimeter even when horizontal physical dimensions will no longer be amenable to any further reduction. It seems important, then, that we ask ourselves a fundamental question: “How will we be able to increase the computation and memory capacity when the device physical limits will be reached?” It becomes necessary to re-examine how we can get more information in a finite amount of space.

The semiconductor industry has thrived on Boolean logic; after all, for most applications the CMOS devices have been used as nothing more than an “on-off” switch. Consequently, it becomes of paramount importance to develop new techniques that allow the use of multiple (i.e., more than 2) logic states in any given and finite location, which evokes the magic of “quantum computing” looming in the distance. However, short of reaching this goal, a field of active research involves increasing the number of states available, e.g. 4–10 states, and to increase the number of “virtual transistors” by 2 every 2 years.

More than Moore
During the blazing progress propelled by Moore’s Law of semiconductor logic and memory products, many “complementary” technologies have progressed as well, although not necessarily scaling to Moore’s Law. Heterogeneous integration of multiple technologies has generated “added value” to devices with multiple applications, beyond the traditional semiconductor logic and memory products that had lead the semiconductor industry from the mid 60s to the 90s. A variety of wireless devices contain typical examples of this confluence of technologies, e.g. logic and memory devices, display technology, microelectricomechanical systems (MEMS), RF and Analog/Mixed-signal technologies (RF/AMS), etc.

The ITRS has incorporated More than Moore and RF/AMS chapters in the main body of the ITRS, but is uncertain whether this is sufficient to encompass the plethora of associated technologies now entangled into modern products, or the multi-faceted public consumer who has become an influential driver of the semiconductor industry, demanding custom functionality in commercial electronic products. In the next blog of this series, we will examine select data from the ITRS Overall Roadmap Technology Characteristics (ORTC) 2012 and attempt to extrapolate the progress in the next 15 years, and its potential impact.

The opportunities for more discourse on the impact and future of Moore’s Law on CS and other disciplines are abundant, and can be continued with your comments on the Research at Google Google+ page. Please join, and share your thoughts.

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Download Cool apple mac logo wallpaper for Desktop 2

Set-1 Set-2 Set -3

Set-1 Set-2 Set -3

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