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Skillset, topics, projects and virtual internships for DS

This post is for those who are beginner and do not have any idea about topics that they need as a beginner DATA SCIENCE/ DATA ANALYST.  I am also facing the same problem before a year ago and till date I have some relevant knowledge about data science and also have some projects.  People are saying the we need so many skills like Mathematics, Programming language, some cloud concepts too. Actually they are right. Being a Data Scientist is not like being a web developer or a front-end developer that have limited skill set.  In this post I will tell you the exact topics that you need to learn at beginner level. MATHEMATICS Descriptive Statistics, distributions, hypothesis testing and regression analysis. Bayesian Thinking, conditional probability, priors, maximum likely hood. Vectors and matrices Matrices operations Eigenvalues and eigenvectors Linear and non linear functions Multivariable calculus  PROGRAMMING LANGUAGE(Python or R)    Data types, String operations, Expressions and varia
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Continuous Vs Categorical Variables | Neccessary tools for Visualization of variables

In a dataset, we have two type of variables.
1. Qualitative Variable
2. Quantitative Variable

Qualitative Variable 

Qualitative variable are the group of values that represent any category that can be related to any aspects. It is also known as Categorical Variable.

Ex. Suppose we have a dataset having numbers of person having different different races( face colour) for example fair, white, Asia, dark and other.
So we assign and integer number for every categories like 

Fair = 1
White = 2
Asian = 3
Dark = 4
Othet = 5

 So we assign different - different integers with different different categories. And that's how we deals with categorical varible in our dataset.

Advantage of converting categories in to integers

Easy to apply mathematical functionality on thr categories. 
We can easily summarise our dataset with the help of frequency distribution. 
In frequency distribution, we havr two options.
1. Count
2. Percentage

Just take a look of following pictures. 
In this table every categories are shown with count as well as proportion. 
And It is convenient when and only we convert every categories in to interger.

Visualization for Categorical data.

We can use Barchart and Pie chart for categorical data, for for better undertanding, go for bar chart.


Quantitative Variable 

Those variable in which, we can easily perform mathematical operation called quantitative variable. 

There are two types of quantitative variable. 
1. Continuous variable 
2. Discrete variable

Continuous variables are those that contain all types of numbers including rational numbers.
Ex  1, 1.1, 3.4 etc.

Discrete varibles are those that don't contain rational numbers, only intergers are included in it.
Ex 1, 2, 3 .....and so on.

And for Visualization of quantitative variable, we use histograms, boxplot, frequency ploygon etc.

Histogram example. 

Tnank you. 

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