Skip to main content

About Us

About Us

Hello,

Currently, there is only me who is managing all of this. I am a Data Scientist who in 2020 graduated as Computer Engineer from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan, and in 2022 graduated as Data Scientist from The John Hopkins University.

I have more than 3 years of working experience as a Data Scientist. I have been programming in R daily for over 3 years and that's where I do most of my data analysis. I build automated trading strategies in my spare time.

I have a lot of experience with Tableau and SQL. Working in a diverse set of fields has given me experience with many different types of data and obtaining it from many various sources. Thereby making me an excellent data miner, and knowing how to transform that data efficiently according to my needs.


With this platform and many others including @Youtube.com, @Facebook, @Instagram and @LinkedIn, I'll be sharing a lot of learning content starting with the Data Science Specialization with R to Data Analysis using powerful tools i.e. Power BI, Tableau, SQL queries and much more.

So stay tuned, and keep following daily updates because I'll help you get start your career as a Data Scientist and help you all along your career.
Keep sharing my content, Like and Comment.

Thanks!


Follow us at:

Comments

Popular posts from this blog

Mastering Debugging in R: Essential Tools and Techniques

The Beginner’s Guide to Debugging Tools in R: Debugging is an essential part of programming in any language, including R. When your code doesn't work as expected, it can be frustrating and time-consuming to find and fix the issue. Fortunately, R provides a variety of debugging tools that can help you identify and fix issues in your code more efficiently. In this blog post, we'll explore some of the most useful debugging tools in R, along with examples of how to use them. The browser() function:  The browser() function is a built-in debugging tool in R that allows you to pause the execution of your code and inspect the values of variables at that point. To use the browser() function, simply insert it into your code where you want to pause the execution. For example: my_function <- function(x) {                                              y <- x * 2  ...

Mastering Simulation in R Programming: A Beginner to Intermediate Guide

The Beginner’s Guide to Simulation in R: Simulation is the process of generating artificial data based on a set of assumptions or models. R programming provides a variety of functions and packages for simulating different types of data. In this blog post, we will cover the basics of simulation in R programming, including the most commonly used functions, distributions, and simulations using linear models. Functions for Simulation in R R programming provides various functions for simulation, such as: runif() – used to simulate data from a uniform distribution rnorm() – used to simulate data from a normal distribution rexp() – used to simulate data from an exponential distribution rgamma() – used to simulate data from a gamma distribution rpois() – used to simulate data from a Poisson distribution rbeta() – used to simulate data from a beta distribution rbinom() – used to simulate data from a binomial distribution rcauchy() – used to simulate data from a Cauchy distribution Distributio...

Mastering R Data Types: Matrices, Factors, Missing Values, Data Frames, and Names Attribute

The Beginner’s Guide to R Data Types: R is a programming language that is widely used for data analysis and statistical computing. It has a powerful set of data structures, including vectors, lists, and data frames, that allow users to work with data in a flexible and efficient way. Matrices A matrix is a two-dimensional array in R that can contain elements of any data type. You can create a matrix using the matrix() function. For example: # Create a matrix with 3 rows and 2 columns  my_matrix <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, ncol = 2) Factors A factor is a type of variable in R that represents categorical data. Factors are stored as integers, where each integer corresponds to a level of the factor. You can create a factor using the factor() function. For example: # Create a factor with three levels: "low", "medium", "high"  my_factor <- factor(c("low", "high", "medium", "high", "low")) Missin...