Graphic design python turtle 🐢

By Kajal Vishwakarma -
from turtle import * import colorsys bgcolor('black') pensize(0) tracer(50) h=0 for i in range(300): c=colorsys.hsv_to_rgb(h,1,1) h+=0.9 color(c) forward(300) left(100) fd(i) goto(0,0) down() rt(90) begin_fill() circle(0) end_fill() rt(10) for j in range(5): rt(30) done() Please follow my blog and subscribe my channel for more videos and newly updates 👍👍👍👍👍 import turtle as t import colorsys t.bgcolor('black') t.tracer(100) h=0.4 def draw(ang,n): t.circle(5+n,60) t.left(ang) t.circle(5+n,60) for i in range(200): c=colorsys.hsv_to_rgb(h,1,1) h+=0.005 t.color(c) t.pensize(2) draw(90,i*2) draw(120,i*2.5) draw()
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Statistical Analysis of Data in machine learning

By Kajal Vishwakarma -

 Statistical Analysis of Data

Statistical data analysis is a procedure of performing various statistical operations. it is a kind of quantitative research which seeks to quantify the data, and typically, applies some from of statistical analysis. quantitative data basically involves descriptive data, such as survey data and observational data.

statistical data analysis generally involves some from of statistical tools, which a layman cannot perform without having any statistical knowledge. there are various software packages to perform statistical data analysis. this software includes statistical analysis system (SAS), statistical packages for the social science (SPSS), stat soft, etc. Data in statistical data analysis consists of variable(s). sometimes the data is univariate or multivariate. Depending upon the number of variables, the researcher performs different statistical techniques.

If the data in statistical data analysis is multiple in numbers, then several multi-variates can be performed. these are factor statistical data analysis, discriminant statistical data analysis, etc. Similarly, if the data is singular in number, then the univariate statistical data analysis is performed. This includes t-test for significance, z-test, f-test, ANOVA one way, etc.

The data in statistical data analysis is basically of 2 types, namely, continuous data and discreet data. The continuous data is the one that cannot be counted. For example, intensity of a light can be measured but cannot be counted. The discreet data is the one that can be counted. For example, the number of bulbs can be counted.

Whether we are working with large data volumes or running multiple permutations of our calculations, statistical computing has become essential for today's statistician. Popular statistical computing practices includes-

  1. Statistical Programming
  2. Econometrics
  3. Operations Research
  4. Matrix Programming
  5. Statistical Visualization
  6. Statistical Quality Improvement

  1. Statistical Programming:- From traditional analysis of variance and linear regression to exact methods and statistical visualization techniques, statistical programming is essential for making data-based decisions in every field.

  1. Econometrics:- Modeling, forecasting and simulating business processes for improved strategic and tactical planning. This method applies statistic to economics to forecast future trends.

  1. Operations Research:- Identify the actions that will produce the best results- based on many possible options and outcomes. Scheduling, simulation, and related modeling processes are used to optimize business processes and management challenges.  

  1. Matrix Programming:- Powerful computer techniques for implementing your own statistical methods and exploratory data analysis using row operation algorithms.

  1. Statistical Visualization:- Fast, interactive statistical analysis and exploratory capabilities in a visual interface can be used to understand data and build models.

  1. Statistical Quality Improvement:- A mathematical approach to reviewing the quality and safety characteristics for all aspects of production.





















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