Thematic Books Display - Data Science and Artificial Intelligence - 4th July 2022

 

Books on Data Science in our Library

B27206 ANALYTICS IN A BIG DATA WORLD: THE ESSENTIAL GUIDE TO DATA SCIENCE AND ITS APPLICATIONS   

By BAESENS, BART
NEW DELHI/WILEY INDIA/2014
005.74 BAE

 

B26162 CORE PYTHON PROGRAMMING-2nd. ed.   
By RAO, R. NAGESWARA
NEW DELHI/DREAMTECH/2018
005.133 RAO

 

 B24667 BIG DATA AND ANALYTICS   

By ACHARYA, SEEMA/CHELLAPPAN, SUBHASHINI
NEW DELHI/WILEY INDIA/2015
005.74 ACH/CHE

 

 B23315 TOO BIG TO IGNORE: THE BUSINESS CASE FOR BIG DATA   

By PHIL, SIMON
NEW DELHI/WILEY INDIA/2013
005.74 PHI

  

B22963 HADOOP IN PRACTICE   

By HOLMES, ALEX
NEW DELHI/DREAMTECH/2013
004.36 HOL

 

MEB1175 HANDS ON DATA SCIENCE AND PYTHON MACHINE LEARNING: PERFORM DATA MINING AND MACHINE LEARNING EFFICIENTLY USING PYTHON AND SPARK   

By KANE, FRANK
MUMBAI/PACKT/2017
005.74 KAN

 

 MEB1169 APPLIED PREDICTIVE ANALYTICS: PRINCIPLES AND TECHNIQUES FOR THE PROFESSIONAL DATA ANALYST   

By ABBOTT, DEAN
NEW DEIHI/WILEY/2014
005.74 ABB

 

MEB1167 DATA SCIENCE FROM SCRATCH: FIRST PRINCIPLES WITH PYTHON   

By GRUS, JOEL
MUMBAI/SHROFF/2015
005.74 GRU

 

 MEB1165 DOING DATA SCIENCE: STRAIGHT TALK FROM THE FRONTLINE   

By SCHUTT, RACHEL/ONEIL, CATHY
MUMBAI/SHROFF/2014
005.74 SCH/ONE

 

MEB1150 DATA SCIENCE AND BIG DATA ANALYTICS:DISCOVERING,ANALYZING,VISUALIZING AND PRESENTING DATA   

By EMC EDUCATION SERVICES
NEW DELHI/WILEY/2015
005.74 EMC

 

 MEB1147 INTRODUCING DATA SCIENCE:BIG DATA,MACHINE LEARNING, AND MORE,USING PYTHON TOOLS   

By CIELEN, DAVY/MEYSMAN, ARNO D.B./ALI, MOHAMED
NEW DELHI/DREAMTECH/2016
005.74 CIE/MEY

Why demand for math skills is surging in the technology world

Till about a decade ago, if you specialised in mathematics, you would end up being a teacher or a researcher. That has changed dramatically. Specialists in the field in India are now among the most sought after in the financial and technology world. Many of today’s leading digital technologies – artificial intelligence, machine learning, data science, big data, cyber security – need strong foundational knowledge of mathematics.

“Data science is based on three skill sets – a background in math or statistics, exposure in computer science, and business or domain knowledge,” says Ashok Kalidas, head of data science & innovation at research firm Kantar’s analytics practice. “Where a math graduate adds value is in the ability to understand the mathematics behind the models and innovate on top of that.”

Kalidas says AI and deep learning solutions can be implemented using software, but you need mathematics to understand the inner workings of these solutions. “A mathematics graduate will be able to interpret why you are getting certain types of results, whether it makes sense, how to modify some of it and go to the next level,” he says. Only mathematics will tell you under what circumstances a solution would work and in what circumstances it would not.

Swaminathan Padmanabhan, senior director of data science at software firm Freshworks, says that mathematics helps create unique and more effective ML models than those built using offthe-shelf libraries and automation platforms.

Chakra Mantena, MD and head of technology at Morgan Stanley India, says the company hires mathematics students in the fields of financial modelling and quantitative roles, which involve sitting with traders and helping them decide what to buy or sell.

“The products we help clients trade in are complex fixed income and derivative products which involve pricing them and projecting cash flows. And when you are doing risk analysis, how a certain trade may play out, some of the math involved is intense, and an engineer may not have had exposure to it,” he says.

This means topics such as probability, functional analysis, topology, algebraic geometry, number theory, and graph theory assume great importance. “People who have knowledge in algorithms, probability, linear algebra, statistical methods tend to get absorbed in the finance sector and research labs,” S Dharmaraja, head of the department of mathematics at IIT Delhi, says.

The growing need for math professionals is reflecting in developments at India’s leading educational institutions. The student intake at IIT-Madras’s mathematics department has more than doubled to 30 now, from 12 in 2018. Chennai Mathematical Institute (CMI), which counts Ford, Crisil, Adobe and Credit Suisse among its recruiters, has seen pay packages double to Rs 15.5 lakh this year, from Rs 7.7 lakh six years ago.

“This year, the number of applications increased by almost 60% – to 1,200, from around 700 in the previous year – for the course which is focused on providing jobready skills in data science,” Madhavan Mukund, deputy director and dean of CMI, says.

Salman Abdul Moiz, chairman of the placement guidance and advisory bureau at the University of Hyderabad, points out that until 2016-17, a majority of the students from the school of mathematics and statistics would pursue careers in academics, but in the last two to three years, around 60% of the students are getting recruited by tech firms.

Anupam Kaura, president of HR at ratings & research firm Crisil, says a degree in math is important for his firm because the quantitative, statistical and analytical skills are applied to design, review and refine complex time series models to provide insights into business problems of customers, including regulatory compliances.

“Given that you need to look at a lot of data structures, run statistical tests, regression tests, these are roles for which an ISI (Indian Statistical Institute) graduate is more skilled than an engineer,” he says. Crisil usually hires annually from ISI Kolkata, CMI, Centre for Modelling and Simulation - Pune, Indira Gandhi Institute of Development Research, besides the top IITs.

Many educational technology companies are hiring those with Master’s in math not just for teaching math but subjects like data science concepts, computer science algorithms, applied mathematics. “If you look at data scientists in research and in MNCs in technology and finance, they are all PhDs in mathematics,” says Kaushik Banerjee, VP and business head of staffing firm Teamlease.

Cloud software firm Zoho has hired pure math freshers to its research team for AI & ML-led projects. Shailesh Kumar Davey, co-founder and director of engineering, says such talent helps the engineering team by translating complex research concepts to simpler terms, thereby helping the latter develop good code. “Digital marketing roles also require candidates with knowledge of stats & math,” he says.

India needs a lot more of such skills. Neeraj Sharma, VP of HR at logistics software platform FourKites, says despite hiring from the top statistics schools, they face a skills gap for analytics roles. Data science jobs today, he says, require a knowledge of both statistics and computing techniques.

Source: https://timesofindia.indiatimes.com/articleshowprint/80484247.cms

AI Boom In India: AICTE Will Launch B.Tech In AI; IBM Will Create AI Curriculum For Class 12th

As per the reports coming in, the All India Council for Technical Education (AICTE) has approved Bachelors of Technology(B. Tech) course in Artificial Intelligence(AI) and data science to fulfill the requirements coming for AI skills from different sectors,as informed by Anil Dattatraya Sahasrabudhe, AICTE chairman Chennai on Saturday.


Another report is coming from Bengaluru wherein the Central Board of Secondary Education(CBSE) has also announced for the addition of AI as an elective subject for students of classes 9 -12.

Why AI And Data Science?


Recently a news came from Public Sector Banks like SBI, IDBI hiring for specialized skills. According to analysts, there is a constant requirement of skilled workforce across all sectors and some of these positions remain vacant because of a lack of skilled manpower to fill these positions.


India stands among the top 5 countries in the world when it comes to AI-driven startups as the future prediction for 2025, most products will use this technology. It is also expected that companies will emerge across sectors with the use of AI in their products.


While companies are doing their part in the digitization of India, the education institutions in the country also has to gear up their game to enhance knowledge and prepare future generations to handle these requirements efficiently.


In IT companies also AI professional’s demand is growing exponentially. In September Infosys was in headlines for hiring resources for AI, UX and Automation technologies.

AICTE B.Tech Course In AI And Data Science

Sahasrabudhe said that a committee is formed to assess the need for offering degree programs in technology has approved AI and Data science courses as part of big transformation, During the session organized by Education Promotion Society for India (EPSI).

While talking about other technologies like the Internet of Things, Blockchain, and Cyber Security, he said that it was decided that they won’t need a full-fledged degree programs in these streams right now, but they can be offered as specialization.

He also informed that they have approved a semester-long training program and also made it compulsory for faculty members to improve the quality of education provided by AICTE.

AICTE constantly working on the quality of education it is providing to society. Some time back they have initiated a program in which the faculty promotions were supposed to be decided by the feedback provided by their students.

AI As Elective For CBSE Students

CBSE has announced to add AI as an elective subject for students in classes 9 – 12 and the curriculum of the subject will be decided by IBM India with the help of other subject experts.

According to reports, IBM will conduct a pilot project in 1,000 schools in various cities in the country. They are considering Bengaluru, Delhi, Kolkata, Bhubaneswar, Hyderabad and Chennai cities to start with before finalizing the curriculum and planning to embed it in CBSE curriculum from the next academic year. The pilot will be launched coming Wednesday in Delhi. (reference TOI)

The idea of launching AI in CBSE curriculum was proposed by Niti Ayog, the government’s think-tank.

Source: https://trak.in/tags/business/2019/09/17/ai-boom-in-india-aicte-will-launch-b-tech-in-ai-ibm-will-create-ai-curriculum-for-class-12th/ (Accessed on September 18, 2019)

A Review Of Google's Colab And CoCalc for Collaborative Data Science

As part of my on-going series on learning data science and reviewing the latest tools, I ended up needing to work on data analysis with people in different countries. While big companies have their own internal tools for sharing code among teams, there were less available for students and freelancers. Fortunately, two such tools, Google Colab and CoCalc, are emerging to help data scientists collaborate online (Disclosure, I am a contractor with the tech policy nonprofit, Tech4America). 

Google's Colaboratory (Colab, for short) began as a research project with makers of the popular online programming notebook, Jupyter. The features have recently ramped up, as machine learning and other data science needs have become more commonplace.

Read the full article at:

https://www.forbes.com/sites/gregoryferenstein/2019/08/18/a-review-of-googles-colab-and-cocalc-for-collaborative-data-science/#7c6878a46f02 (Accessed on August 20, 2019)

Five Factors Shaping Data Science

As data science evolves, key challenges are driving organizations to seek innovative solutions to compete in the new AI-driven economy.

Five Factors:

1. Making data actionable for data science

2. Shortage of data science talent

3. Time-to-value must accelerate

4. Business users need transparency

5. Improving the operationalization process

To read the full article, please visit:

https://www.informationweek.com/big-data/five-factors-shaping-data-science/a/d-id/1335387 (Accessed on August 16, 2019)

What Skills Do Data Scientists Need

There is currently a huge demand for data scientists, which is a top-trending job with attractive salaries. But what are the skills and tools that employers are looking for. It's a few years since we asked What is a Data Scientist and How Do I Become One? The answer given back in 2015 is still valid as a starting point: Similar to a business/data analyst, data scientists combine knowledge of computer science and applications, modelling, statistics, analytics and math to uncover insights in data. But what does this mean in terms of the skillset a data scientist should acquire. The question How to Become More Marketable as a Data Scientist has been tackled by the research team at CV Compiler, a company which provides guidance on creating a convincing resume to developers and others in the software industry. For an analysis of the skills required by data scientists the CV Compiler team looked at 300 Data Science vacancies from StackOverflow, AngelList, and similar websites. Then using their own text analytics tool, they identified the terms which were mentioned the most frequently and created this chart: It needs to be noted that the research represents the preferences of employers, rather than of data scientists. I would have expected to see "Machine Learning" near the top of the list because looking at job descriptions you discover that Machine Learning Engineers work in Data Science teams and that Data Science Interns can expect to "gain valuable AI/ML skills". Perhaps the two terms are so intertwined that knowledge of  Machine Learning is assumed. While R is frequently referred to as "the language of data science, Python outnumbering it in job vacancies makes sense in that Python a general-purpose language and currently trending when it comes to popularity. I'm surprised to see Scala quite so high and the complete absense of Julia both from the table and from the blog report write up where other skills and tools that gain substantial number of mentions are discussed. For example, while Big Data is in the table with 221 mentions, the term Data Mining, used for "collecting big data" isn't in the table despite but the fact that it had 128 mention in job vacancies is reported. While SQL comes high in the list, and ETL (Extract, transform, load) is in the table, there's no mention anywhere Mongo DB or No SQL. On the other hand mentions of the open source  Apache Spark outnumber those of Hadoop. Commenting on this Andrew Stetsenko writes: According to the 2018 Big Data Analytics Market Study, Big Data adoption in enterprises soared from 17% in 2015 to 59% in 2018. Thus the popularity of Big Data tools also grew. [In addition to Spark and Haddoop] the most popular ones are MapReduce (36), and Redshift (29) .....some employers still expect candidates to be familiar with Apache Pig (30), HBase (32), and similar technologies. HDFS (20) is still being mentioned in vacancies as well. As with Compiler CV's earlier report on the skills needed by JavaScript developers, the figures in brackets are the number of mentions. Stetsenko also mentions the importance of data visualization, mentioned in 55 job vacancies and notes: It’s crucial that you could represent the outcomes of your work in a format, understandable to any team member or a customer. As for the data visualization tools, employers prefer Tableau (54). The fact that Computer Vision and NLP (Natural Language Processing) make it into the table serves to emphasize that AI and Data Science are inextricably linked and that knowledge of AI tools such as Tensorflow is well worth acquiring.  Source: https://www.i-programmer.info/news/197-data-mining/12988-what-skills-do-data-scientists-need.html (Accessed on August 7, 2019)

How data sciences enable life-long learning

Today, corporates often talk of 'life-long learning'. Just the college degree isn't enough- the future of work is about up-skilling every few years. Over the last few years, companies helping people become life-long learners have started up. While some create the content themselves, others curate it. One company that has built a next-generation enterprise learning product is Degreed. The company connects employees in an enterprise to learning resources such as courses, videos, articles, books, and podcasts among others; it assesses the skills one has and those that would be required for a chosen domain.

Business Today recently spoke to Chris McCarthy, Chief Executive Officer of Degreed, to understand how the company curates content and measures skills. Turns out, technology and data play a crucial role.    

"We collate the history of what an employee did in the company or outside. We collect every data point we can find about an individual- it can be in the human resources systems, the learning management systems, the spreadsheets. Also, what they do in their spare time and which device is used for learning", McCarthy says. "We can then identify the gaps in his portfolio. We pull the learning material in and make it a personalised experience. It says this is what you have to learn, here is what you might be interested in, here is what people like you have also learnt", he adds.

This is similar in some ways to Amazon's recommendation engine many of us are familiar with- because you read book X, you might be interested in book Y.

If an employee wants to graduate to being a product manager in a company, Degreed identifies the skills required. There is a target skill level and the employee's skill level. "I can see how I stack-up. We have data sciences underpinning every recommendation so we can say these are the contents that are popular among product managers," McCarthy says.   

HR heads, the CEO says, often want to know what's going on in their industry and how they could better train employees. Degreed can use data from its 300-odd customers to draw conclusions and build skills graphs around what skills are popular and what is in-demand. This could help companies make smart decisions.

"There are four different ways we assess the skills. The first is self-assessment by the employee, which takes a minute. The second is asking the boss or a manager for an assessment, which takes a few more minutes. Then there is a 20-minute version and an eight-hour version. It is based on how rigorous you want to get", McCarthy says. 

Source: https://www.businesstoday.in/buzztop/buzztop-technology/how-data-sciences-enable-lifelong-learning/story/334363.html (Accessed on May 3, 2019)

Should Data Scientists Offer Their Skills For Free?

Data Science is one of those domains that is growing at a breakneck speed. However, the industry is in the need for more skilled professionals. Being a vast domain, the number of professionals is less compared to other technology domain. This also means that if you are you are a data scientist, you are in high demand and you are a part of a community of people who has the superpowers of extracting meaningful insights out of scattered data.

However, there is a question that has emerged in the world of Data Science — while experts from other technological fields are offering their skills for free to the ones in need, should data scientists also do the same? The answer is something that cannot be flagged as a ‘no’ or a ‘yes’ as it completely depends on the person. So, there are a few points we would like to put out why and why not it is okay for data scientists to work for free.

To read the full article, please visit: https://www.analyticsindiamag.com/should-data-scientists-offer-their-skills-for-free/ (Accessed on May 2, 2019)

Train schoolgirls to help more women take up data science

‘Train schoolgirls to help more women take up data science’ 

Read more at:
http://timesofindia.indiatimes.com/articleshow/68342249.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst