The Complete Computer Science Training Bundle

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9 Courses & 212 Hours

$39.99$1,800.00

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What's Included

$200.00 Value

Python Data Science

Juan Galvan

115 Lessons (19h)

Lifetime

Certification included
Experience level required: Intermediate
Access 115 lectures & 19 hours of content 24/7
Length of time users can access this course: Lifetime

Course Curriculum

115 Lessons (19h)

Your First Program
Preview

1. Introduction
Who is this Course for2:43
DS + ML Marketplace6:55
Data Science Job Opportunities4:24
Data Science Job Roles10:23
What is a Data Scientist17:00
How To Get a Data Science Job18:39
Data Science Projects Overview11:52
1. Data Science & Machine Learning Concepts
Why We Use Python3:14
What is Data Science13:24
What is Machine Learning14:22
ML Concepts & Algorithms14:42
Machine Learning vs Deep Learning11:09
What is Deep Learning9:44
1. Python for Data Science
What is Programming6:03
Why Python for Data Science?3:14
What is Jupyter3:54
What is Google Colab3:27
Python Variables, Booleans and None11:47
Getting Started with Colab9:07
Python Operators25:26
Python Numbers and Booleans7:47
Python Strings13:12
Python Conditional Statements13:53
Python For Loops and While Loops8:07
Python Lists5:10
More About Python Lists15:08
Python Tuples11:25
Python Dictionaries20:19
Python Sets9:41
Compound Data Types & When to use each one?22:39
Python Functions14:23
Object Oriented Programming in Python18:47
1. Statistics for Data Science
Intro to Statistics7:10
Descriptive Statistics6:35
Measure of Variability12:19
Measure of Variability Continued9:35
Measures of Variable Relationship7:37
Inferential Statistics15:18
Measures of Asymmetry1:57
Sampling Distribution7:34
1. Probability & Hypothesis Testing
5.1 What Exactly Probability3:44
5.2 Expected Values2:38
5.3 Relative Frequency5:15
5.4 Hypothesis Testing Overview9:09
1. NumPy Data Analysis
NumPy Arrays8:21
NumPy Array Basics11:36
NumPy Array Indexing9:10
NumPy Array Data Types12:58
NumPy Array Computations5:53
Broadcasting4:32
1. Pandas Data Analysis
7.1 Intro to Pandas15:52
7.2 Intro to Panda Continued18:05
1. Python Data Visualization
8.1 Data Visualization Overview24:49
8.2 Different Data Visualization Libraries in Python12:48
8.3 Python Data Visualization Implementation8:27
1. Machine Learning
Intro to Machine Learning26:03
1. Decision Trees
15.1 Decision Trees Section Overview4:11
15.2 EDA on Adult Dataset16:53
15.3 What is Entropy and Information Gain21:50
15.4 The Decision Tree ID3 algorithm from scratch Part 111:32
15.5 The Decision Tree ID3 algorithm from scratch Part 27:35
15.6 The Decision Tree ID3 algorithm from scratch Part 34:07
15.7 ID3 - Putting Everything Together21:23
15.8 Evaluating our ID3 implementation16:53
15.9 Compare with Sklearn implementation8:51
15.10 Visualizing the Tree10:15
15.11 Plot the features importance5:51
15.12 Decision Trees Hyper-parameters11:39
15.13 Pruning17:11
15.14 [Optional] Gain Ration2:49
15.15 Decision Trees Pros and Cons7:31
15.16 [Project] Predict whether income exceeds $50Kyr - Overview2:33
1. Ensemble Learning and Random Forests
Ensemble Learning Section Overview3:46
What is Ensemble Learning?13:06
What is Bootstrap Sampling?8:25
What is Bagging?5:20
Out-of-Bag Error (OOB Error)7:47
Implementing Random Forests from scratch Part 122:34
Implementing Random Forests from scratch Part 26:10
Compare with sklearn implementation3:41
Random Forests Hyper-Parameters4:23
Random Forests Pros and Cons5:25
What is Boosting?4:41
AdaBoost Part 14:10
AdaBoost Part 214:33
1. Support Vector Machines
SVM - Outline5:15
SVM - SVM intuition11:38
SVM - Hard vs Soft Margin13:25
SVM - C Hyper-Parameter4:17
SVM - Kernel Trick12:18
SVM - Kernel Types18:13
SVM - with Linear Dataset13:35
SVM - Non-Linear Dataset12:50
SVM- Multi _ Regression5:51
SVM - Project Overview (Voice Gender Recognition)4:26
1. PCA
PCA - Section Overview5:12
What is PCA9:36
PCA - Drawbacks3:31
PCA - Algorithm Steps13:12
PCA - Covariance Matrix vs SVD4:58
PCA - Main Applications2:50
PCA - Image Compression27:00
PCA - Data Preprocessing14:31
PCA - BiPlot and The Screen Plot17:27
PCA - Feature Scaling and Screeplot9:29
PCA - Supervised vs unsupervised4:55
PCA - Visualization7:31
1. Data Science Career
Creating a Data Science Resume6:45
Data Science Cover Letter3:33
How to Contact Recruiters4:20
Getting Started with Freelancing4:13
Top Freelance Websites5:35
Personal Branding4:02
Networking Do's and Don'ts3:45
Importance of a Website2:56

Python Data Science

Juan Galvan

Juan Galvan | Digital Entrepreneur, Marketer, and Visionary

4.4/5 Instructor Rating: ★ ★ ★ ★ ★ ★

Juan Galvan has been an entrepreneur since grade school. He's started several companies, created many products, and sold on various online marketplaces with great success. He is the founder of The Dominant SEO, an agency based out of Seattle, Washington, and is excited to share his business expertise with his online students.

Description

In this practical, hands-on course, you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to use that data practically. The main objective is to educate you to understand the Python programming language's ins and outs for Data Science and Machine Learning and learn exactly how to become a professional Data Scientist with Python and land your first job.

4.4/5 average rating: ★ ★ ★ ★ ★ ★

Access 115 lectures & 19 hours of content 24/7
Learn data cleaning, processing, wrangling, & manipulation
Create a resumé & land your first job as a data scientist
Use Python for Data Science
Write complex Python programs for practical industry scenarios
Learn Plotting in Python (graphs, charts, plots, histograms, & more)

"I think the course is very well explained, the presenter does a good emphasis on important points. And having as an introduction to the course how someone needs to approach a job interview is a fantastic idea, as it makes your brain more focused and aimed-oriented. Good job." – Alvaro Paz Navas

Specs

Important Details

Length of time users can access this course: lifetime
Access options: desktop & mobile
Certificate of completion included
Redemption deadline: redeem your code within 30 days of purchase
Updates included
Experience level required: intermediate

Requirements

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Terms

Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

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