What are you favourite data science books?

think_bayes

Recently someone on my the blog asked me about some book ideas on data science and today I saw this post on ebooks available on data science, so here you go.

Please visit the link for the freely available ebooks. http://blog.paralleldots.com/data-scientist/list-must-read-books-data-science/

A good shout out for the good folks at parralledots. (I like the name!!)

My pick, definitely the think Bayes, and think stats. What are your favourite data science books?

 

The Need for Numerical Computation

Have been doing numerical computation ever since I am in the industry but somehow never got to read the following essay. Found it by chance on google. 

A must read for anyone interested in Numerical computation

The Need for Numerical Computation

Everyone knows that when scientists and engineers need numerical answers to mathematical problems, they turn to computers. Nevertheless, there is a widespread misconception about this process. The power of numbers has been extraordinary. It is often noted that the scientific revolution was set in motion when Galileo and others made it a principle that everything must be measured. Numerical measurements led to physical laws expressed mathematically, and, in the remarkable cycle whose fruits are all around us, finer measurements led to refined laws, which in turn led to better technology and still finer measurements.

The day has long since passed when an advance in the physical sciences could be achieved, or a significant engineering product developed, without numerical mathematics.

Computers certainly play a part in this story, yet there is a misunderstanding about what their role is. Many people imagine that scientists and mathematicians generate formulas, and then, by inserting numbers into these formulas, computers grind out the necessary results. The reality is nothing like this.

 

Read more by following this link.  http://people.math.umass.edu/~johnston/M552S16/NAessay.pdf

 
 

 

 
 

Machine learning: Thou aimest high.

Was reading the book Why Nation fails by by Daron Acemoglu and found this anecdote. This reminded me of similar thoughts, many have advocated about artificial intelligence and machine learning.

In 1583 William Lee returned from his studies at the University of Cambridge to become the local priest in Calverton England. Elizabeth I (1558–1603) had recently issued a ruling that her people should always wear a knitted cap. Lee recorded that “knitters were the only means of producing such garments but it took so long to finish the article. I began to think. I watched my mother and my sisters sitting in the evening twilight plying their needles. If garments were made by two needles and one line of thread, why not several needles to take up the thread.”

This momentous thought was the beginning of the mechanization of textile production. Lee became obsessed with making a machine that would free people from endless hand-knitting. He recalled, “My duties to Church and family I began to neglect. The idea of my machine and the creating of it ate into my heart and brain.” Finally, in 1589, his “stocking frame” knitting machine was ready. He travelled to London with excitement to seek an interview with Elizabeth I to show her how useful the machine would be and to ask her for a patent that would stop other people from copying the design.

He rented a building to set the machine up and, with the help of his local member of Parliament Richard Parkyns, met Henry Carey, Lord Hundson, a member of the Queen’s Privy Council. Carey arranged for Queen Elizabeth to come see the machine, but her reaction was devastating. She refused to grant Lee a patent, instead observing, “Thou aimest high, Master Lee. Consider thou what the invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment, thus making them beggars.”

Crushed, Lee moved to France to try his luck there; when he failed there, too, he returned to England, where he asked James I (1603–1625), Elizabeth’s successor, for a patent. James I also refused, on the same grounds as Elizabeth. Both feared that the mechanization of stocking production would be politically destabilizing. It would throw people out of work, create unemployment and political instability, and threaten royal power. The stocking frame was an innovation that promised huge productivity increases, but it also promised creative destruction.

I am not smart enough to know if the apprehensions are right or wrong, but as a machine learning enthusiast, I am fascinated with the field. 

Deep learning, AI, ML are tools like knife and hammer that we are now beginning to understand better and put them to practical use.
Exciting times.

The Best Asset

At one of his Berkshire Hathaway annual meetings, Warren Buffett said:
“The most important investment you can make is in yourself. Very few people get anything like their potential horsepower translated into the actual horsepower of their output in life. Potential exceeds realization for many people…The best asset is your own self. You can become to an enormous degree the person you want to be.”

Switch from Discrete Mathematics to Probability, Statistics

From the book Foundations of Data Science by John Hopcroft and Ravindran Kannan

Computer science as an academic discipline began in the 60’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata,regular expressions, context free languages, and computability.

In the 70’s, algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on applications.

There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks, which are by far the largest such structures, presents both opportunities and challenges for theory

All this entails, there is the switch from discrete mathematics to more emphasis on probability and statistics.