Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Data Engineering with Python

Name: Data Engineering with Python
Rating: 3.8 (62 reviews)

Learn the skills to become a Data Scientist [ Data Science A - Z ]

Created byAcademy of Computing & Artificial Intelligence

Last updated 8/2020

English

English [Auto],

What you'll learn

Python Programming Basics For Data Science
Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)
Unsupervised Learning - Clustering, K-Means clustering
KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step

Course content

13 sections • 63 lectures • 14h 10m total length

Downloading and Setting up Python and PyCharm IDE6:15
Learn how to download and install Python and PyCharm IDE, set up Anakonda with notebooks, and configure a development environment for Python programming and machine learning basics.
Python For Absolute Beginners : Setting up the Environment : Anaconda4:13

Python For Absolute Beginners - Variables - Part 15:14
Python For Absolute Beginners - Variables - Part 24:37
Explains variables and string indexing, along with concatenation in Python, showing zero-based indexes and negative indexing, with examples like Facebook and name, and highlights index out of range errors.
Python For Absolute Beginners - Variables - Part 33:31
Python For Absolute Beginners - Lists3:13
Python For Absolute Beginners - Lists Part 23:27
Explore list operations in Python for absolute beginners: create mixed lists of numbers and strings, append and insert items at specific indices, and remove elements while understanding zero-based indexing.
Python For Absolute Beginners - Lists Part 33:11
Explore Python lists for absolute beginners, covering indexing, popping, and key operations like min, max, sum, mean, and sort.
Python - Conditions - if, if-else and elif Part 111:22
Explore boolean types and expressions, and master Python conditional statements using if, if-else, and elif, with emphasis on indentation, boolean operators, and designing with pseudocode and flowcharts.
Python - Conditions - if, if-else and elif Part 220:58
Explore Python conditional expressions and boolean logic, using if, if-else, and elif to evaluate comparisons (==, !=, <, <=, >, >=), handle indentation, and build simple decision structures.
Python - Relational Operators Boolean operators -13:18
Explore data engineering with Python by mastering relational and boolean operators, including and, or, not; evaluate expressions and perform boundary testing for robust data validation.
Python For beginners - Loops #Iteration11:03
Master loops and iteration in Python for beginners, exploring while loops, indentation, flowcharts, and the relationship between sequence, selection, and repeated execution with practical examples.
Python Programming Tutorial : Loops part 1 #Guess the number program13:11
Python Programming Tutorial : Loops part 2 #Getting a random number1:00
Explore Python programming examples and learn to obtain a random number by importing the random module and generating a value from 1 to 20.
Python Programming Tutorial : Loops part 1 #Guess the number program #Modified5:53
Explore how loops and if conditions drive a guess the number program in Python, using random numbers, input, and a while loop to compare guesses with a hidden number.
Python program to Find the Class Average17:24
learn to write a Python program that collects school scores until -9, sums them, counts entries, and computes the class average while guarding against division by zero.
Python : Functions : Demonstration10:46
learn how to define and call Python functions, use input parameters, and reuse modular code to perform tasks like calculating class averages.
Pass by reference vs value9:15
Python Function - Arguements (Required, Keyword, Default)13:40
Python: For Loops #Iteration # Repetition11:48
Python File Handling - Part 14:00
Learn to use Python's open function to read, write, and create files, choose text or binary modes, and handle the file object for reading content.

Tutorial 1 - Introduction35:03
Topics:
• The print statement
• Operators
Tutorial 2 - Built-In Functions41:43
Topics:
    • Strings (introduction)
    • Use a range of built-in functions - print(), type(), int(), str()
    • Get Keyboard input - input()
    • Translate your Pseudocode into Python programs
Tutorial 3 - if conditions50:25
Topics:
    • if, if-else, elif;
    • generating random numbers;
    • boolean operators: and, or, not.
Tutorial 4 - while loops32:59
Topics:
• While loops
Tutorial 5 - for loops and exceptions33:54
Topics:
• for loops
• handling exceptions
Tutorial 6 - for loop challenge questions21:12
Topics:
• for loops challenge questions
Tutorial 7 - Guess the Word Game30:18
The program created for this tutorial will use strings, for loops, while loops and if-else conditions.

Create a python program to play ‘Guess the Word’.
• The program will store a secret word (variable secret)
• The player will guess a letter in the word (variable guess).
• The program will store each guess entered by the player (variable guesses).
• The player will be allowed a number of guesses (variable turns).
• The program will print a dash (_) for each letter in the secret word not yet found.
• If the players guess is found in the word, the program will display the letter in replace of theappropriate dash.
• The program will display appropriate introduction and end of game messages.
Tutorial 8 - Functions (Dragon Kingdom Game)20:15
The aim of this tutorial is to create a simple game that demonstrates the use of user-defined functions (with arguments, parameters, return values and local variables). We will also review the Boolean and operator and while loops.

Understanding Data with Statistics: Reading data from file8:10
Understanding Data with Statistics: Checking dimensions of Data7:57
Assess dataset dimensions by rows and columns. Apply statistics including mean, standard deviation, min, max, and percent values, and check class distribution balance for classification readiness.
Understanding Data with Statistics: Statistical Summary of Data11:03
Understanding Data with Statistics: Correlation between attributes7:13
Data Pre-processing - Scaling with a demonstration in python11:41
Data Pre-processing - Normalization , Binarization , Standardization in Python8:42
Feature Selection Techniques : Univariate Selection18:38
Learn how univariate feature selection in Python identifies the most relevant data features to boost predictive accuracy, using statistical tests to find the strongest relationships with the target.

Introduction to Artificial Neural Networks15:14
Explore how artificial neural networks mimic the brain to solve real-world problems, and learn the basics of neurons, weights, bias, summation, net input, and activation functions.
Creating the First ANN from Scratch with Python13:39
Multiple Input Neuron8:25
Creating a simple layer of neurons, with 4 inputs. # Python # From scratch26:57
ANN - Illustrative Example8:46
KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step29:35
Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project]13:03

Linear regression2:29
Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression13:51
Univariate Linear Regression - Demonstration - Part 1
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that output variable (y) can be calculated from a linear combination of the input variables (x).
Univariate Linear Regression is a linear regression that has only one input parameter and one output label.
Sourcecode - In this demonstration we will build a model that will predict Happiness.Score for the countries based on Economy.GDP.per.Capita parameter.
Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression27:11
Univariate Linear Regression - Demonstration - Part 2
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that output variable (y) can be calculated from a linear combination of the input variables (x).
Univariate Linear Regression is a linear regression that has only one input parameter and one output label.
Sourcecode - In this demonstration we will build a model that will predict Happiness.Score for the countries based on Economy.GDP.per.Capita parameter.
Multivariate Linear Regression Demo [Hands-on] Linear Regression20:52
Explore multivariate linear regression by predicting happiness from GDP per capita and freedom, using gradient descent, polynomial features, and regularization.

Requirements

Computer & Internet Connection

Description

Academy of Computing & Artificial Intelligence proudly present you the course "Data Engineering with Python". It all started when the expert team of Academy of Computing & Artificial Intelligence (PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2020.

To make the course more interactive, we have also provided a code demonstration where we explain to you how we could apply each concept/principle [Step by step guidance].

Requirements

Here’s the checklist:
A computer - Setup and installation instructions are included.
Your enthusiasm to learn
Everything else needed is already included in the course.

At the end of the Course you will understand the basics of Python Programming and the basics of Data Science & Machine learning.

The course will have step by step guidance for machine learning & Data Science with Python.

You can enhance your core programming skills to reach the advanced level.

Setting up the Environment for Python Machine Learning

Understanding Data With Statistics & Data Pre-processing (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)

Data Pre-processing - Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate Selection

Data Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..

Artificial Neural Networks with Python, KERAS

KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step

Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]

Naive Bayes Classifier with Python [Lecture & Demo]

Linear regression

Logistic regression

Introduction to clustering [K - Means Clustering ]

K - Means Clustering

Python Programming
Setting up the environment
Python For Absolute Beginners : Setting up the Environment : Anaconda
Python For Absolute Beginners : Variables , Lists, Tuples , Dictionary
Boolean operations
Conditions , Loops
(Sequence , Selection, Repetition/Iteration)
Functions
File Handling in Python
Flow Charts
Algorithms
Modular Design
Introduction to Software Design - Problem Solving
Software Design - Flowcharts - Sequence
Software Design - Modular Design
Software Design - Repetition
Flowcharts Questions and Answers # Problem Solving

Does the course get updated?

We continually update the course as well.

What if you have questions?

we offer full support, answering any questions you have.

There’s no risk !

This course comes with a full 30 day money-back guarantee.

Who this course is for:

Beginners with no previous python programming experience looking to obtain the skills to get their first programming job.
Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.
Who want to improve their career options by learning the Python Data Engineering skills.

Who this course is for:

Anyone who wish to start the career in Data Science

Data Engineering with Python

What you'll learn

Explore related topics

Course content

Setting up Python2 lectures • 10min

Python Theory19 lectures • 2hr 47min

Software Design4 lectures • 32min

Python Tutorials8 lectures • 4hr 26min

Setting up the Environment for Machine Learning1 lecture • 4min

Understanding Data With Statistics & Data Pre-processing7 lectures • 1hr 13min

Data Visualization with Python2 lectures • 20min

Artificial Neural Networks [Comprehensive Sessions]7 lectures • 1hr 56min

Naive Bayes Classifier with Python [Lecture & Demo]1 lecture • 17min

Linear regression4 lectures • 1hr 4min

Requirements

Description

Who this course is for: