Technical Know Hows

  Information Security controls : Special Tools and rules which guards our data to ensure it is not exposed publicly and protects them from black hat hackers. Types of Security Controls Firewall : provides a barrier between trusted & untrusted network. Firewall can be classified into 2 types hardware & software. Hardware firewall can be configured into switches, routers & hubs which protects all devices connected to this components by acting as a traffic guard from the outside world. In other case software firewall works on individual device level which scans for inbound & outbound traffics. Antivirus : Detects harmful software, virus & malware. Password encryption : Useful in encrypting the password with public key, private key or both to enable seamless secure transfer and storage of passwords. 2 Factor Authentication : Adding an extra layer of protection between application and user by using unique code for authentication. Always ensure regular updates, backu...

Matplotlib is a library for creating plots ,graphs and charts. It draws its influence from Matlab, and it works efficiently with NumPy and Pandas for good graphic visualization which enhances your data analysis projects. We start with importing matplotlib.pyplot library and using %matplotlib inline You can view the documentation in more detail here: * [ Documentation ]( https://matplotlib.org/tutorials/introductory/pyplot.html ) import matplotlib.pyplot as plt #The below line helps in presenting plots in Jupyiter notebook. %matplotlib inline Lets create some data for our first graph, A plot from two numpy arrays `x` `y` using `plot()`. import numpy as np x=np.arange( 0 , 10 ) y=x** 3 + 10 result = plt.plot(x, y) plt.xlabel( "X" ) plt.ylabel( "Y" ) plt.title( "Example" ) plt.suptitle( "Basic Plot from Matplotlib" ) plt.show() In the above code , 1 . We called the plot () function to plot the graph between the data we have created. 2 . W...

We will be looking at light weight module called Statistics module which has the common methods of analysis in it. We will look at 1. Mean 2. Median 3. Quantiles 4. Variance 5. Correlation 6. Standard Deviation 7. Covariance 8. Normal Distribution Plot We will import the random module to get the data for our analysis and usage import random, statistics import seaborn as sns Look at the methods of the module using the dir() method on the statistics module. dir (statistics) We will create some random data for our purposes using the random module. x =[random.random() for x in range( 10 )] y =[random.random() for x in range( 10 )] x,y sns .distplot (x) This is the plot that we see from the random data that we created. sns .distplot (y) Calculating the mean for our set of data. statistics .mean (x),statistics .mean (y) Calculating the median for our set of data. statistics .median (x),statistics .median (y) Calculating the quantiles for our set of data. It will give the 0.25,0.5,0.75 qua...