## Python for Stock Market Analysis: Exploring Technical Trend Indicators

Introduction Hello and welcome back everyone to our second part of the new blog series [Python for Stock Market Analysis](). In the last part, we explored different [types of moving averages]() like Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA) and explored other moving metrics like Moving Median and Moving Variance. […]

## R Exercise: Getting Started With ggplot2 in R

Getting Started with ggplot2 in R Grammar A grammar provides a foundation for understanding different types of graphics. A grammar may also help us on what a well-formed or correct graphic looks like, but there will still be many grammatically correct but nonsensical graphics. This is easy to see by analogy to the English language: […]

## Social Network Analysis in R

Social Network Analysis Definition Social networks are simply networks of social interactions and personal relationships. Think about our group of friends and how we got to know them. Maybe we met them while ago from our schooling, or maybe we met them through a hobby or through our community. In fact, 72% of all Internet […]

## Creating Awesome Data Dashboard with Plotly in Streamlit: Clustering

This is a continuation of our previous blog entitled as Creating Data Dashboards With Streamlit and Python. Adding A Clustering Functionality Before diving into the clustering functionality in our existing app, please make sure you are following previous part. Or you can grab all the codes from below: import streamlit as st import numpy as […]

## R Exercise: Working with PCA and Dimensionality Reduction

Check the data mtcars with head and save a new data as mtcars.subset after dropping two non-numeric (binary) variables for PCA analysis data <- mtcars head(data) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 […]

## Creating Data Dashboard with Plotly in Streamlit

Data Dashboard are getting highly popular because the need to get insights from data is getting increased. One of the few ways we find the insights from the data is via dashboards. And for Data Analysts, there are options like tableau. But not all of them are for free. However, we can make some cool […]

## R Exercise: Working with Outliers and Fitting Different Machine Learning Models

Extract the Nepal COVID-19 data from Wikipedia to fit the following models with daily deaths as dependent variable and time as independent variable First plot the daily deaths by time and distribute the three outliers (added deaths around timeline of 400) before fitting the following models in the outlier adjusted data on training and testing […]

## R Exercise: Working with Data from JSON API and Performing EDA

Import any data from JSON API This was prepared on Google Colab. Please refer to this link for running R in Google Colab. We are using data from https://data.askbhunte.com/api/v1/covid. We are going to use below packages. jsonlite : It implements a bidirectional mapping between JSON data and the most important R data types. RCurl : […]

## Walkthrough of Statistical Analysis in Data Science

What is Statistical Analysis in Data Science? This blog starts from definition to explaining and experimenting with different part of statistics. Introduction Statistics is very important field and there are lots of definitions and use cases available and noting few. Statistics is a systematic collection of data on measurements or observations, often related to demographic […]

## Plotting Interactive plots with Plotly and Cufflinks

Plotting High Quality Plots in Python with Plotly and Clufflinks Interactive Plot This blog contains static images and is not rendering interactive plots thus we request you to visit this interactive blog. Introduction Hello everyone, in this blog we are going to explore some of most used and simplest plots in the data analysis. If […]

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