Numpy: Introduction

Minerva Singh
A free video tutorial from Minerva Singh
Bestselling Udemy Instructor & Data Scientist(Cambridge Uni)
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Complete Data Science Training with Python for Data Analysis

Beginners python data analytics : Data science introduction : Learn data science : Python data analysis methods tutorial

12:49:50 of on-demand video • Updated July 2019

  • Python data analytics - Install Anaconda & Work Within The iPytjhon/Jupyter Environment, A Powerful Framework For Data Science Analysis
  • Python Data Science - Become Proficient In Using The Most Common Python Data Science Packages Including Numpy, Pandas, Scikit & Matplotlib
  • Data analysis techniques - Be Able To Read In Data From Different Sources (Including Webpage Data) & Clean The Data
  • Data analytics - Carry Out Data Exploratory & Pre-processing Tasks Such As Tabulation, Pivoting & Data Summarizing In Python
  • Become Proficient In Working With Real Life Data Collected From Different Sources
  • Carry Out Data Visualization & Understand Which Techniques To Apply When
  • Carry Out The Most Common Statistical Data Analysis Techniques In Python Including T-Tests & Linear Regression
  • Understand The Difference Between Machine Learning & Statistical Data Analysis
  • Implement Different Unsupervised Learning Techniques On Real Life Data
  • Implement Supervised Learning (Both In The Form Of Classification & Regression) Techniques On Real Data
  • Evaluate The Accuracy & Generality Of Machine Learning Models
  • Build Basic Neural Networks & Deep Learning Algorithms
  • Use The Powerful H2o Framework For Implementing Deep Neural Networks
English [Auto] In this very brief lecture I will provide you with a very brief introduction to NUMP by which is an important and a rather basic package for Python based data science and century Let us just look at the Wikipedia of them by an stands for numerical pipeline and what it does is as opposed to the conventional pipe and data structures. It has a lot of support for multidimensional arrays and matrices and allows for implementing high high level mathematical functions on matrices and arrays and matrices arrays in a manner of speaking are a building block for data science and the Keatinge about numbed by is that it provides us with the array. And that's a fast and efficient way of storing homogenous data and this can be either rank 1 or rank to you know unidimensional or multidimensional. And there's a plethora of mathematical functions out there that we can implement on the arrays. But first I'm going to look at the importation conventions now that we are working with Jupiter the Anaconda's system. All of these packages things like NUMP by Panoz. They come installed but we have to import them before we can use them. So when I say import I'm biased and B it is going to import the number by package as a and b and whenever I want to essentially use NUMP by for something very specific I have to specify you know things like and pede art Ray or you know and P-Dog zero. So when I want to refer to them by specific functionality then I have to append this by whichever function I'm alluding to. Otherwise it won't work. So anti-dote I. Now I. Is a kind of a by function and if I want to call it then I have to say N.P. door. And instead of importing umpires and beef I just imported NUMP by then instead of MP I would have to specify NUMP by anyone Y and specify the whole thing. So that is why it's more convenient to say and b array and we are going to use similar conventions throughout this closely. I want to import Panoz which is another very important data science library. I can either say import panels or import and others speedy and this is just one of the most common ways of alluding to these specific data science packages. And as you can see over here when I put in a very simple array you know that it is of the type list and this is because a list is a very common byte and data structure. It is useful for a lot of operations but it is frankly not conducive for data science so when I say P-doc of a it becomes a pie chart and the array object and it is these NUMP by dark and we are the objects that we are going to work with throughout this section and see the different functions that we can implement on them by dark in the area. And this includes everything from basic arithmetic operations to solving a system of linear equations to basic statistics. So essentially now you can go right ahead. Important buys and B. And we will get started from the next lecture on what's.