Python is the main programming language of choice for the data science industry. This course will cover the key ideas of using Python.

Python Interview

Introduction

Python as a Programming Language

Python as Statistical Software

Python Packages

Why Python?

Data structures in Python attempt to be more intuitive and flexible than traditional data structures in other programming languages.

Basic Data Types

Iterables

Functions and Classes

Find the Missing Number

Find Bigrams

Merge Sorted Lists

Permutation Palindrome

Equivalent Index

Data Structures

As we said in the first section of this course, a major benefit of using Python for data science in comparison to other programming languages is the availability of a large number of useful packages that are distributed under a free license. 

numpy

scipy

matplotlib

pandas

sklearn

Over 100 Dollars

Good Grades and Favorite Colors

Generate Normal Distribution

Random Seed Function

Complete Addresses

Common DS Packages

Let's try some hard Python questions that you would see in tougher data science interviews and many machine learning interviews.

Max Width

Scrambled Tickets

Bernoulli Sample

Sum to N

Check Normality

Pool Matching

Python Questions: Hard

Other than SQL, Python is the most common language used in data science, due to its easy-to-read syntax and its robust collection of packages created specifically for data scientists. 

Learn the basics of Python and how to approach coding and problem solving

The scipy package specializes in “scientific computation” and provides functionality for various numerical implementations of calculus techniques. For example, numerical integration is done via the quad function:

from math import exp, pi
from scipy.integrate import quad

def f(x):
 return pow(x,2)

quad(f,0,1)

scipy also provides for the implementation of probability distribution functions:

<h1>normal distribution</h1>

from scipy.stats import norm

norm.cdf(0)

Python Interview

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