Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Learn R – How to Add New Column to Data Frame

This article represents concepts and code samples on how to add new columns to a data frame using R programming language. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Lets create a student data frame. Following is the code: # Create non-empty data frame with column names # Assign names to x x <- c( “Calvin”, “Chris”, “Raj”) # Assign names to y y <- c( 10, 25, 19) # Create a non-empty data frame with column names # Assign x to “First Name” as column name # Assign y to “Age” as column name student <- data.frame( …

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Posted in Big Data. Tagged with , .

AngularJS – Sublime Template for Hello World

This article represents sublime snippet code sample which you could use to create an auto-complete hello world template to quickly get started with Hello World code for AngularJS. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the key points described later in this article: Why Sublime Snippet for AngularJS Hello World How to Create HelloNG Snippet? Code Samples   Why Sublime Snippet for AngularJS Hello World Many a time, while starting on new AngularJS app for doing quick POC or testing purpose, I ended up creating boilerplate code or copied and pasted minimum AngularJS app code …

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Posted in Javascript, Web. Tagged with .

Learn R – How to Convert Columns from Character to Factor

This article represents different ways in which one or more columns in a data frame could be converted to factor when working with R programming language. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos. Following are the key points described later in this article: Convert single column to factor Convert multiple columns to factor Following data frame, df, is used in the code sample below: In above data frame, both diagnosis and param_d are character vectors. One could quickly check classes of all columns using the following command: Convert Single Column to Factor Following is demonstrated the code samples …

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Posted in Big Data.

Data Science – How to Scale or Normalize Numeric Data using R

This article represents concepts around the need to normalize or scale the numeric data and code samples in R programming language which could be used to normalize or scale the data. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos. Following are the two different ways which could be used to normalize the data, and thus, described later in this article: Why Normalize or Scale the data? Min-Max Normalization Z-Score Standardization Why Normalize or Scale the data? There can be instances found in data frame where values for one feature could range between 1-100 and values for other feature could …

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Posted in AI, Big Data, Data Science. Tagged with , .

Learn R – How to Append Rows to Data Frame

This article represents concepts and code samples on how to append rows to a data frame when working with R programming language. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos. Following are the key points described later in this article: How to append one or more rows to an empty data frame How to append one or more rows to non-empty data frame For illustration purpose, we shall use a student data frame having following information: How to Append one or more rows to an Empty Data Frame Following code represents how to create an empty data frame and …

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Posted in Big Data. Tagged with , .

Learn R – How to Create Data Frame with Column Names

This article represents code in R programming language which could be used to create a data frame with column names. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the key points described later in this article: Create empty dataframe with column names Create non-empty dataframe with column names Create an Empty Dataframe with Column Names Following is the code sample: Following gets printed:   Create non-empty Dataframe with Column Names Following is the code sample: Following gets printed. Note the column names such as “First Name” and “Age”  

Posted in Big Data. Tagged with , .

Learn R – How to Extract Rows & Columns from Data Frame

This article represents command set in R programming language, which could be used to extract rows and columns from a given data frame. When working on data analytics or data science projects, these commands come very handy in data cleaning activities.  This article is meant for beginners/rookies getting started with R and wanting to know or see examples of extracting information from a data frame. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the key points described later in this article: Commands to extract rows and columns Command to extract a column as data frame Command …

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Posted in Big Data. Tagged with , .

Data Science – How to Load Data included with R

This article represents different ways in which data from different R packages could be loaded. One of the important aspect of getting on aboard with Data Science is to play with data as much as possible while one is going through the  learning phase. When doing that, some of the key activities include data loading, data extraction, data wrangling/munging etc. This is where I found that loading data from different R packages is one of the key to get access to these data sets and hence, decided to write this quick article. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for …

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ReactJS – Online Playground for JSX-HTML Expertise

This article represents quick introduction to online playground for JSX to HTML conversion and vice-versa. Very handy for those, especially the Javascript beginners, to get a hang on component-oriented programming related with ReactJS which is at the heart of it. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.   Following are the key points described later in this article: HTML to JSX Compiler JSX to HTML Compiler Following are two online tool or playground to enhance familiarity with JSX. HTML to JSX Compiler: This Online playground for HTML-JSX compiler would help you to write HTML on one end, …

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Posted in Javascript, Web. Tagged with , , .

AngularJS – How to Handle XSS Vulnerability Scenarios

This article represents different scenarios related with XSS (cross-site scripting) and how to handle them appropriately using AngularJS features such as SCE ($sceProvider) and sanitize service ($SanitizeProvider). Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Do visit the page, how to prevent XSS attacks in Angular 2.*, Angular 4.* or Angular 5.*, if you are looking forward for handling XSS vulnerabilities in latest version of Angular apps. You may also want to check the page, Top 10 Angular Security Best Practices vis-a-vis vulnerability issues. Following are the key XSS-related scenarios described later in this article: Escape HTML completely …

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Posted in Application Security, Javascript, Web. Tagged with , , , .

AngularJS – Two Ways to Initialize an Angular App

This article represents code samples along with related concepts for two different ways in which Angular app can be defined. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the key points described later in this article: Automatic Initialization Manual Initialization   Automatic Initialization All that is needed for automatic initialization (as of current AngularJS version) is to define “ng-app” on an element and you should be all set. Take a look at following code sample. Pay attention to some of the following: ng-app=”HelloApp” defined on div element ng-controller=”HelloCtrl” defined on the same element. It could as …

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Posted in Javascript, Web. Tagged with .

Machine Learning – How to Predict Software Developers Productivity

This article represents my thoughts on how machine learning techniques could be used to solve one of the most popular problem of software industry such as whether a software developer is productive or not. Of all the effort that I have made to solve this problem using traditional programming techniques (rules-based), I could say that there is no definitive way of finding a concrete solution. As a matter of fact, I created a tool, AgileSQM to capture the software quality metrics (SQM) such as code coverage, duplication, complexity and infer from the trending data whether a software developer is productive. However, I soon hit the road-block in terms of acceptance …

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Java – Top 10 Java-based Web Frameworks for 2014-2015

This article represents an analysis on Java-based web frameworks that emerged in the top 10 list this year 2014, and  worth consideration for your next project starting this year or next year (2015). I have done data analysis based on following: Job openings (as of today) on a very popular website, indeed.com Discussion threads (for this year) on a very popular Q&A based website, stackoverflow.com Responses on a very popular social bookmarking website, reddit.com Based on the analysis of top 10 frameworks, I have listed the top 5 frameworks which emerged as clear winner. Please feel free to comment/suggest if I missed to mention one or more important frameworks. Also, …

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Posted in Java, Web. Tagged with .

How to use Sonar Dashboard to Report on Software Code Quality

This article represents methods one could adopt to read the sonar dashboard and gather data appropriately to monitor and control the software code quality. The primary reason why I am writing this blog is the fact that I have come across several team leads who asked me the questions related with data on sonar dashboard and what all they could do with it. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are three different aspects of software quality which could be tracked using Sonar Dashboard: Maintainability (testability, reusability & modularity) Usability (readability and understand-ability) Security Following are …

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Posted in Software Quality. Tagged with .

Productivity – Top 3 Javascript IDEs/Text Editors to Consider

This article represents the top 3 Javascript IDE/Text Editors that you may want to explore for your next project for Javascript related development. The way the top 3 editors are chosen is the number of votes (thumbs up) given by different users in one of the Javascript IDE/Text Editor related discussion thread on one of the very popular social bookmarking website, as of today. Interestingly, newer editors such as Brackets and Atom are catching attention of some of the users. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are different Javascript Text Editors (at times, also called …

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Posted in Javascript, Web. Tagged with .

Data Science – Examples of Machine Learning Problems

This article represents different classification of machine learning problems along with some of the examples taken from real world problems. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following is listed different categories which covers 80% of machine learning problems: Classification Clustering Regression   Machine Learning – Classification Problems Simply speaking, if the answer to problems consists of discrete values such as some of the following, the problem can be termed as classification problems. These are called as “Logistic Regression” problems. Yes or no,. e.g., 1 or 0. Finite set of values representing multi-classification problems Mathematically speaking, if “h(x)” …

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