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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

How to Show Data Science Project in Resume Correctly

The data scientist resume should have mentioned the project correctly. Here are my ideas on how to show the project in the resume.

Resume: How to Show Data Science Project


How to Show Data Science Project?

1. Preparation of Resume

The first step for an interview for any project is you need Resume. You need to tell clearly about your resume. 


2. Answering about Project. 

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


3. Answering your Project Role. 

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 a 100% chance to shortlist your resume.

Based on your experience your resume can be 1 page or 2 pages

4. Technologies in the Resume.

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 how you used different options present in the tools. 

Sometimes, in interviews, they may ask about the specific role in Tools specifically you used. You should be in a position to answer these questions too.

How to Prepare Resume. 

  1. Write a clear description.
  2. Write a specific role.
  3. Explain Tools. 
  4. Write about data flow.

1. Write a Clear Description.  In any data science project, you will find a few things like Client name, the expectation of the client, and what you are going to deliver. These things you need to present clearly in your Resume. 


2. Write Specific Roles. Roles. To convince your interviewer, you need to tell about your team roles and your specific role. In general, you can find the following roles:
  • Architect.
  • Data scientist.
  • Business Analyst.
  • Development team.
  • Testing Team.
  • Integration testing team.
  • Production release team.

3. Explain Tools. You need to present all the Tools your project is using and your specific tools. Then, in the face-to-face interview, you need to tell what options you used to achieve what.

For example, I used some integration tool, to receive data to the development region, and to send out after unit testing. If you explain, these key points, I can say, 100% sure, you will be selected.

4. Write about data flow. You need to explain how data is coming, is it in the sequential data set or document data. Something you need to tell clearly.

You also need to tell, after unit testing, which forms you will send the data to the next region. If you know this flow correctly, then you can convince easily your interviewer.

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