Gain insights into performing predictive data analysis using Python. Learn about tools like NumPy, Pandas, Matplotlib, and Seaborn, and applying techniques to real-world financial and advertisement projects.

seaborn.tar.gz

seaborn

In this course, you will learn how to perform predictive data analysis using Python. The ideal audience is those who want to start their careers as data analysts. The main goal of this course is to show you how to use statistics to draw useful insights from data which can help in predicting future behavior or patterns.

Beyond that, you’ll learn all the tools of the trade that data scientists use everyday including: NumPy, Pandas, Matplotlib, and Seaborn. You’ll learn not only how to extract meaningful insights from data, but you’ll also learn how to create stunning visualizations that you can use for reports.

Various datasets of real-world scenarios are used in each lesson to get you accustomed to handling any type of data. At the end of the course, you will work on two real-world projects that demonstrate how data analysis techniques are being used in the financial and advertisement sector to generate revenue.

Predictive Data Analysis with Python

def getMinMax(arr):
  
  res = []
  
  for i in range(arr.shape[0]): # Traverse over each row
    
    res.append(arr[i].min()) # Store minimum element in list
    
    res.append(arr[i].max()) # Store maximum element in list
    
  return res

# Test Code
arr = np.random.randint(1,100, size=(5,5))

print("The Original Array:")
print(arr)

res_arr = getMinMax(arr)

print("\nThe Resultnt list with min & max values:")
print(res_arr)

# Explanation

A function `getMinMax` is declared with `arr` passed to it as a parameter.

* On __line 3__, a list variable `res` is declared to store the minimum and maximum values.

* On __line 5__, the `shape[0]` function of the `NumPy` is used to get the row count of the array. The loop is then used to traverse over each row to find the required information. On every iteration, a row is processed to find the min and max elements.

* On __line 7__, the min() function of the `NumPy` array is used to find the minimum element of the row, which is then stored in the list.

* On __line 9__, the max() function of the `NumPy` array is used to find the maximum element of the row, which is then stored in the list.

* On __line 11__, the `res` with all the minimum and maximum elements of each row of the array is returned.

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Let’s review these concepts with a quiz in the next lesson.

This lesson gives a detailed review of the solution to the challenge in the previous lesson.

Getting Started

Numpy for Python

Pandas for Python

Statistics for Data Analysis

Data Wrangling

Visualizing the Data

Data Scraping

Project #1

Project #2

Conclusion

Predictive Data Analysis Exam

Solution: Find Min and Max from a 2-D NumPy Array

Solution