Monthly Archives: September 2018

Internet, Software, Trends

History & Future of Information Technology

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Late 1970s – Ethernet

Late 1980s & Early 1990s – Internet

Late 1990s – Y2K was the trend and revolution of easy operating systems like Windows with word processing and spreadsheets happened. Also, LINUX. ERP, CRM, BPM, HRM and similar software pick up in market.

Early 2000s – Internet boom, Email, Browsers, Dot Com, Start of E-Commerce, Java, boom in Portals, Scanning solutions, Enterprise Content Management, Search and associated technologies

Post 2011- Shift to Mobiles, GPS, Location tracking, cameras, virtual / augmented reality on mobiles and so on. Lot of growth for companies like Google / Apple.

Post Mobiles – Cloud, Machine Learning, Artificial Intelligence, Analytics, IoT, Beacons, Business Intelligence & BigData

2010/11 onwards – RPA, SAAS/PAAS/IAAS, Facebook, Social Media, Agile, DevOps, Crypto currency, Focus on Internet, Network & Cloud security

Time is now right to have an AI / ML based System in Enterprise IT ecosystem that can create and manage systems on it’s own or at least most of it at Enterprise / Technical & Solution Architecture level along with automated creation, deployment and maintenance of software systems using Cloud

Reach out to me at neil@techandtrain.com if you want to discuss Data Science / R / Java / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for your managers / executives on Data Science / R / Java / AWS / Excel / etc.

Programming

Learning R Programming – Part 1

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As per Glassdoor Top 5 skills in Data Science for job openings are:

  1. Python
  2. R
  3. SQL
  4. Hadoop
  5. Java

Most Java developers know SQL, Hadoop & Java to a good extent in today’s environment, two important skills Python & R should be learned by the Java developer / architect / manager if s/he wants to contribute / work in Data Science area. In this article you will find a structured step by step approach for learning programming in R.

  1. To start with install R and familiarize yourself with R Console & R Script interface. You can run commands on both but it’s best to write multiple commands and try them out in script editor. Use short cut CTRL + R to run your commands in R Script editor.
  2. Explore menu options like Package -> Install / Load / Choose CRAN Mirror. By default many commands for statistics, visualization, etc. are given in R by default. Big set of libraries are already loaded into R by default and 100s more are available. Select any mirror to download new packages and install / load them step by step. You will need working internet connection. Learn how to set working directory. You can see default libraries available in R using library()
  3. From there move on to various R Objects / Data Types – Explore Data Frame, Vector, List, Matrices, Arrays and Factors. Try out examples for the same.
  4. Next step learn to load / read and write datasets by commands like read.csv / write.csv. You can also read / write excel sheets but for it you will need other packages. See the basic commands like summary, structure & fix to analyze / edit your dataset
  5. Next step – go through various categories of operators (logical, mathematical, relational) and concepts like pipe %>%, constants, rules for naming identifiers followed by various statistical functions directly available in R. You can get help on a command by using ?<COMMAND>. Also, learn to create functions and use conditions like if
  6. By now you should revise basics of statistics & various visualization charts which are taught typically in Year 1 / Semester 1 of MBA. Explore various default commands for statistics built into R by default. Some examples – mean, variance, standard deviation, etc.
  7. Learn to manipulate / read / write datasets using subset, sample_n & sample_frac and using dplyr package which has commands like select & filter among others
  8. Check various types of default visualization commands in R for various charts like barplot & pie. Post this learn how to use ggplot2 package
  9. You will get many datasets at kaggle.com and various websites like stock exchanges – NSE / BSE, RBI, Open Data websites of various Governments and others
  10. Explore top 20 packages of R categorized by various areas as given below.

An advantage of learning R is that you will become better at statistics & data science. It’s much simpler than Java in terms of syntax and structure and is influenced by open source languages / scripting like Linux, etc.

Reach out to me at neil@techandtrain.com if you want to discuss R, conduct a training for MBA / BE / MCA / MSc students in R or want to conduct a workshop for your managers / executives on Data Science / R / Java / etc.

References:

Top 10 skills for Data Science – Glassdoor Economic Research

Top 20 packages in R