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How to Show Data science Project in Resume

In any project, the Data analyst role is to deal with data. The data for data science projects come from multiple sources. This post will explain how to put in data science project in Resume.
Data Science project for Resume The first step for an interview of any project is you need Resume. You need to tell clearly about your resume.

In interviews, you will be asked questions about your project. So the second step is you need to be in a position explain about project.

The third point is you need to explain the roles you performed in your data science project. If you mention the roles correctly, then, you will have 100% chance to shortlist your resume. Based on your experience your resume can be 1 page or 2 pages.
How to show Technologies used in Data science projects In interviews, again they will be asked how you used different tools to complete your data science project.

So, you need to be in a position to explain about how you used different options present in the tools. Sometime…

Python Matrix Vs COBOL Arrays You Need to Know Logic Differences

Your most looking information where Python matrix and COBOL arrays differ, in this post, I am giving complete information. The Logic is different in both the languages. The way of definition and accessing element in an array or matrix is different.
python arrays

Python Matrix Vs COBOL Array. In reality both Array and Matrix are the same

What are Arrays

Arrays are storing data structure to store data in one or more dimensional form. You can access the data for further processing in your application program.

One Dimensional Array

In general, one dimensional array is a row of elements either numeric or Strings separated by commas. Here, each element is separated by comma. This is key concept.

>>> a = ['Srini',25,33,42]

Two Dimensional Arrays

In the case of Two dimensional array data stored in Tabular form and you can access whichever tuple you want.

Real use of multi dimensional array is to give input in Tabular form and can access particular tuple as you want.

>>> b = [['Srini',25,33,42],['Ramu',44,67,57]]

Python Matrix

In Numpy Python, matrix is a method, where you will get row data in the form of matrix
>>> a = np.matrix('1 2; 3 4')
>>> print(a)
[[1 2]
 [3 4]]
The above example is just to show input rows of data in the form of matrix.

One Dimension Matrix


>>> a = ['Srini',25,33,42]

Two dimension Matrix


>>> b = [['Srini',25,33,42],['Ramu',44,67,57]]

Reading Array in Python


>>> b[0]  
Result will be as below.
>>> ['Srini',25,33,42]                                                               
In the above two dimensional array, the first element is '0' and second element is '1' and so on. In Python accessing tuple has many ways. Whatever element you need you can access with the following syntax.

a[0] => This means first element of an Array 'a'

a[0][1] => This means in the first element of an 'a' array access first column.

a[-1] => This means access last element in 'a' array

Note: In Python an array element starts with 0, 1, 2 and so on

COBOL Arrays

Array definition in COBOL is different. First you need create an array using a definition as below. Below is 2 dimensional array. Why I am saying 2 dimensional is it has 2 OCCURS clauses.
01 StateSalesTable.
   02 State OCCURS 50 TIMES.
      03 StateBranchCount   PIC 9(5).
      03 StateMonthSales    PIC 9(5)V99 OCCURS 12 TIMES.
After the definition is created, you can now store data using COBOL program. Then you can access whichever tuple you want using index and PERFORM statement. Examples given here on multi dimensional arrays in COBOL really good to understand quickly.

Note: In COBOL when you define index then array element starts with 0, 1, 2 and so on

References

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I am practicing Hyperledger. This is one of the top listed blockchains. This architecture follows R3 Corda specifications. Sharing the interview questions with you that I have prepared for my interview.

Though Ethereum leads in the real-time applications. The latest Hyperledger version is now ready for production applications. It has now become stable for production applications.
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