Data : Data Science online training

Frontier Education

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• DS & ML A-Z Course Intro 32:01

  • 1: 1.1 Data Science ML Course Intro1 02:31
  • 2: 1.2 What is data science 09:48
  • 3: 1.3 Machine Learning Overview1 05:26
  • 4: 1.4 Whos this course is for1 02:57
  • 5: 1.5 DL and ML Marketplace1 04:38
  • 6: 1.6 Data Science and ML Job opps 02:37
  • 7: 1.7 Data Science Job Roles1 04:04

• Getting started with R 44:52

  • 8: 2.1 Getting Started 10:58
  • 9: 2.2 Basics 06:25
  • 10: 2.3 Files 11:08
  • 11: 2.4 RStudio 06:59
  • 12: 2.5 Tidyverse 05:19
  • 13: 2.6 Resources 04:03

• Data Type and Structure in R 5:01:57

  • 14: 1.1 Section Introduction 30:03
  • 15: 1.2 Basic Types 08:47
  • 16: 1.3 Vectors Part One 19:41
  • 17: 1.4 Vectors Part Two 24:52
  • 18: 1.5 Vectors - Missing Values 15:36
  • 19: 1.6 Vectors - Coercion 14:07
  • 20: 1.7 Vectors - Naming 10:16
  • 21: 1.8 Vectors - Misc 06:00
  • 22: 1.9 Creating Matrices 31:28
  • 23: 1.10 Lists 31:42
  • 24: 1.11 Introduction to Data Frames 19:20
  • 25: 1.12 Creating Data Frames 19:50
  • 26: 1.13 Data Frames_Helper Functions 31:12
  • 27: 1.14 Data Frames - Tibbles 39:03

• Intermediate R 4:14:11

  • 28: 1.1 Section Introduction Intermediate R 46:31
  • 29: 1.2 Relational Operations 11:07
  • 30: 1.3 Logical Operators 07:05
  • 31: 1.4 Conditoinal Statements 11:20
  • 32: 1.5 Loops 07:57
  • 33: 1.6 Functions 14:20
  • 34: 1.7 Packages 11:29
  • 35: 1.8 Factors 28:14
  • 36: 1.9 Dates and Times 30:11
  • 37: 1.10 Functional Programming 36:41
  • 38: 1.11 Data Import or Export 22:07
  • 39: 1.12 Database 27:09

• Data Manipulation in R 5:39:59

  • 40: 1.1 Data Manipulation in R Section Introduction 36:29
  • 41: 1.2 Tidy Data 10:54
  • 42: 1.3 The Pipe Operator 14:50
  • 43: 1.4 The Filter Verb 21:35
  • 44: 1.5 The Select Verb 46:04
  • 45: 1.6 The Mutate Verb 31:57
  • 46: 1.7 The Arrange Verb 10:04
  • 47: 1.8 The Summarize Verb 23:06
  • 48: 1.9 Data Pivoting 42:42
  • 49: JSON Parsing 10:46
  • 50: String Manipulation 32:39
  • 51: Web Scraping 58:53

• Data Visualization in R 2:24:30

  • 52: 1.1 Data Visualization in R Section Introduction 17:13
  • 53: 1.2 Getting Started 15:38
  • 54: 1.3 Aesthetics Mappings 24:45
  • 55: 1.4 Single Variables Plot 36:50
  • 56: 1.5 Two Varible Plots 20:34
  • 57: 1.6 Facets Layering and Coordinate System 17:56
  • 58: 1.7 Styling and Saving 11:34

• Creating-Reports-with-R-Markdown 28:54

  • 59: Creating-Reports-with-R-Markdown 28:54

• Building Web Apps with R Shiny 1:31:28

  • 60: 1.1 Section-Introduction-With-R-Shiny 26:05
  • 61: 1.2 A Basic App 31:18
  • 62: 1.3 Other Examples 34:05

• Introduction to Machine Learning 1:08:35

  • 63: Intro to Machine Learning - Part 1 21:49
  • 64: Intro to Machine Learning - Part 2 46:46

• Data Preprocessing 1:04:51

  • 65: Data Preprocessing 37:47
  • 66: Introduction to Data Preprocessing 27:04

• Linear Regression_A Simple Model 1:18:14

  • 67: Linear Regression A Simple Model 53:05
  • 68: LR Section Introduction 25:09

• Exploratory Data Analysis 1:28:00

  • 69: Hands-on Exploratory Data Analysis 1:02:57
  • 70: Section Introduction EDA 25:03

• Linear Regression_A Real Model 1:24:52

  • 71: Linear Regression - Real Model Section Intro 32:04
  • 72: Linear Regression in R - real model 52:48

• Logistic Regression 1:17:26

  • 73: Introduction to Logistic Regression 37:48
  • 74: Logistic Regression in R 39:38

• Starting a career in data science 29:42

  • 75: 1.1 Starting a Career in Data Science1 02:54
  • 76: 1.2 Data Science Resume1 03:43
  • 77: 1.3 Getting Started with Freelancing1 04:44
  • 78: 1.4 Top Freelancing Websites1 05:19
  • 79: 1.5 Personal Branding1 05:28
  • 80: 1.6 Importance of Website and Blog1 03:43
  • 81: 1.7 Networking dos and donts1 03:51

• Python for Data Science 23:13:20

  • 82: 1.1 Who is this Course for 02:44
  • 83: 1.2 DS + ML Marketplace 06:56
  • 84: 1.3 Data Science Job Opportunities 04:25
  • 85: 1.4 Data Science Job Roles 10:23
  • 86: 1.5 What is a Data Scientist 17:00
  • 87: 1.6 How To Get a Data Science Job 18:39
  • 88: 1.7 Data Science Projects Overview 11:52
  • 89: 2.1 Why We Use Python 03:15
  • 90: 2.2 What is Data Science 13:24
  • 91: 2.3 What is Machine Learning 14:22
  • 92: 2.4 ML Concepts _ Algorithms 14:43
  • 93: 2.6 Machine Learning vs Deep Learning 11:10
  • 94: 2.7 What is Deep Learning 09:44
  • 95: 3.1 What is Python Programming 06:04
  • 96: 3.2 Why Python for Data Science 04:36
  • 97: 3.3 What is Jupyter 03:54
  • 98: 3.4 What is Colab 03:28
  • 99: 3.5 Jupyter Notebook 18:01
  • 100: 3.6 Getting Started with Colab 09:08
  • 101: 3.7 Python Variables, Booleans and None 11:48
  • 102: 3.8 Python Operators 25:27
  • 103: 3.9 Python Numbers and Booleans 07:48
  • 104: 3.10 Python Strings 13:12
  • 105: 3.11 Python Conditional Statements 13:53
  • 106: 3.12 Python For Loops and While Loops 08:08
  • 107: 3.13 Python Lists 05:10
  • 108: 3.14 More About Python Lists 15:09
  • 109: 3.15 Python Tuples 11:25
  • 110: 3.16 Python Dictionaries 20:19
  • 111: 3.17 Python Sets 09:41
  • 112: 3.18 Compound Data Types and When to use each Data Type 12:58
  • 113: 3.19 Functions 14:24
  • 114: 3.20 Python Object Oriented Programming 18:48
  • 115: 4.1 Intro to Statistics 07:11
  • 116: 4.2 Descriptive Statistics 06:36
  • 117: 4.3 Measure of Variability 12:19
  • 118: 4.4 Measure of Variability Continued 09:35
  • 119: 4.5 Measures of Variable Relationship 07:37
  • 120: 4.6 Inferential Statistics 15:18
  • 121: 4.7 Measures of Asymmetry 01:58
  • 122: 4.8 Sampling Distribution 07:35
  • 123: 5.1 What Exactly Probability 03:45
  • 124: 5.2 Expected Values 02:38
  • 125: 5.3 Relative Frequency 05:16
  • 126: 5.4 Hypothesis Testing Overview 09:09
  • 127: 6.1 NumPy Array Data Types 12:59
  • 128: 6.2 NumPy Arrays 08:22
  • 129: 6.3 NumPy Array Basics 11:36
  • 130: 6.4 NumPy Array Indexing 09:10
  • 131: 6.5 NumPy Array Computations 05:53
  • 132: 6.6 Broadcasting 04:33
  • 133: 7.1 Intro to Pandas 15:53
  • 134: 7.2 Intro to Panda Continued 18:05
  • 135: 8.1 Data Visualization Overview 24:49
  • 136: 8.2 Different Data Visualization Libraries in Python 12:49
  • 137: 8.3 Python Data Visualization Implementation 08:27
  • 138: 9.1 Intro to ML 26:03
  • 139: 10.1 Exploratory Data Analysis 13:06
  • 140: 11.1 Feature Scaling 07:41
  • 141: 11.2 Data Cleaning 07:43
  • 142: 12.1 Feature Engineering 06:11
  • 143: 13.1 Linear Regression Intro 08:17
  • 144: 13.2 Gradient Descent 05:59
  • 145: 13.3 Linear Regression + Correlation Methods 26:33
  • 146: 13.4 Linear Regression Implemenation 05:07
  • 147: 13.5 Logistic Regression 03:23
  • 148: 14.1 KNN Overview 03:01
  • 149: 14.2 Parametic vs Non-Parametic Models 03:29
  • 150: 14.3 EDA on Iris Dataset 22:08
  • 151: 14.4 KNN - Intuition 02:17
  • 152: 14.5 Implement the KNN algorithm from scratch 11:45
  • 153: 14.6 Compare the Reuslt with Sklearn Library 03:47
  • 154: 14.7 KNN Hyperparameter tuning using the cross-validation 10:47
  • 155: 14.8 The decision boundary visualization 04:56
  • 156: 14.9 KNN - Manhattan vs Euclidean Distance 11:21
  • 157: 14.10 KNN Scaling in KNN 06:01
  • 158: 14.11 Curse of dimensionality 08:10
  • 159: 14.12 KNN use cases 03:33
  • 160: 14.13 KNN pros and cons 05:33
  • 161: 15.1 Decision Trees Section Overview 04:12
  • 162: 15.2 EDA on Adult Dataset 16:54
  • 163: 15.3 What is Entropy and Information Gain 21:51
  • 164: 15.4 The Decision Tree ID3 algorithm from scratch Part 1 11:33
  • 165: 15.5 The Decision Tree ID3 algorithm from scratch Part 2 07:35
  • 166: 15.6 The Decision Tree ID3 algorithm from scratch Part 3 04:07
  • 167: 15.7 ID3 - Putting Everything Together 21:23
  • 168: 15.8 Evaluating our ID3 implementation 16:51
  • 169: 15.9 Compare with Sklearn implementation 08:52
  • 170: 15.10 Visualizing the Tree 10:15
  • 171: 15.11 Plot the features importance 05:52
  • 172: 15.12 Decision Trees Hyper-parameters 11:40
  • 173: 15.13 Pruning 17:11
  • 174: 15.14 [Optional] Gain Ration 02:49
  • 175: 15.15 Decision Trees Pros and Cons 07:32
  • 176: 15.16 [Project] Predict whether income exceeds $50Kyr - Overview 02:33
  • 177: 16.1 Ensemble Learning Section Overview 03:47
  • 178: 16.2 What is Ensemble Learning 13:06
  • 179: 16.3 What is Bootstrap Sampling 08:26
  • 180: 16.4 What is Bagging 05:20
  • 181: 16.5 Out-of-Bag Error 07:47
  • 182: 16.6 Implementing Random Forests from scratch Part 1 22:34
  • 183: 16.7 Implementing Random Forests from scratch Part 2 06:11
  • 184: 16.8 Compare with sklearn implementation 03:41
  • 185: 16.9 Random Forests Hyper-Parameters 04:23
  • 186: 16.10 Random Forests Pros and Cons 05:25
  • 187: 16.11 What is Boosting 04:42
  • 188: 16.12 AdaBoost Part 1 04:10
  • 189: 16.13 AdaBoost Part 2 14:34
  • 190: 17.1 SVM - Outline 05:16
  • 191: 17.2 SVM - SVM intuition 11:39
  • 192: 17.3 SVM - Hard vs Soft Margin 13:26
  • 193: 17.4 SVM - C HP 04:18
  • 194: 17.5 SVM - Kernel Trick 12:19
  • 195: 17.6 SVM - Kernel Types 18:14
  • 196: 17.7 SVM - Linear Dataset 13:35
  • 197: 17.8 SVM - Non-Linear Dataset 12:51
  • 198: 17.9 SVM with Regression 05:52
  • 199: 17.10 SVM - Project Overview 04:26
  • 200: 18.1 Unsupervised Machine Learning Intro 20:22
  • 201: 18.2 Representation of Clusters 20:49
  • 202: 18.3 Data Standardization 19:05
  • 203: 19.1 PCA - Section Overview 05:13
  • 204: 19.2 What is PCA 09:37
  • 205: 19.3 PCA - Drawbacks 03:32
  • 206: 19.4 PCA - Algorithm Steps 13:12
  • 207: 19.5 PCA - Cov vs SVD 04:58
  • 208: 19.6 PCA - Main Applications 02:50
  • 209: 19.7 PCA - Image Compression Scratch 27:01
  • 210: 19.8 PCA - Data Preprocessing Scratch 14:32
  • 211: 19.9 PCA - BiPlot 17:28
  • 212: 19.10 PCA - Feature Scaling and Screeplot 09:29
  • 213: 19.11 PCA - Supervised vs unsupervised 04:56
  • 214: 19.12 PCA - Visualization 07:32
  • 215: 20.1 Creating a Data Science Resume 06:45
  • 216: 20.2 Data Science Cover Letter 03:33
  • 217: 20.3 How To Contact Recruiters 04:20
  • 218: 20.4 Getting Started with Freelancing 04:13
  • 219: 20.5 Top Freelance Websites 05:35
  • 220: 20.6 Personal Branding 04:03
  • 221: 20.7 Networking Do_s and Don_ts 03:45
  • 222: 20.8 Importance of a Website 02:56

With expert guidance and a combination of videos, PDFs, and worksheets, this course will enable you to develop your data cleaning, become a Data Analyst and unlock your full potential.

Implementation in R is done in such a way so that not only you learn how to implement a specific Model in Python but you learn how to build real times templates and find the accuracy rate of Models so that you can easily test different models on a specific problem, find the accuracy rates and then choose the one which give you the highest accuracy rate. This course is for you, if you are:

• Curious about learning Data Science and Machine Learning
• Curious to learn the Maths behind Machine Learning Models
• Interested in becoming a real time professional Data Scientist by knowing all the Maths and Intuition behind every Machine Learning Model

Curriculum of the courses:

Course 1: Learn Data Science and Machine Learning with R from A-Z

Section 1: Introduction to Data Science +ML with R from A-Z

Section 2: Getting Started with R

Section 3: Data Types and Structures in R

Section 4: Intermediate R

Section 5: Data Manipulation in R

Section 6: Data Visualisation in R

Section 7: Creating Reports with R Markdown

Section 8: Building Webapps with R Shiny

Section 9: Introduction to Machine Learning

Section 10: Data Preprocessing

Section 11: Linear Regression: A Simple Model

Section 12: Exploratory Data Analysis

Section 13: Linear Regression: A Real Model

Section 14: Logistic Regression

Section 15: Starting a Career in Data Science

Course 2: Learn Python for Data Science & Machine Learning from A-Z

Section 1: Introduction to Python for Data Science & Machine Learning from A-Z

Section 2: Data Science & Machine Learning Concepts

Section 3: Python For Data Science

Section 4: Statistics for Data Science

Section 5: Probability and Hypothesis Testing

Section 6: NumPy Data Analysis

Section 7: Pandas Data Analysis

Section 8: Python Data Visualisation

Section 9: Introduction to Machine Learning

Section 10: Data Loading & Exploration

Section 11: Data Cleaning

Section 12: Feature Selecting and Engineering

This Data Science course covers:

1. Data Types and Structures in R : what you need to know
2. Intermediate R: tailoring your approach to maximise impact
3. Mastering data processing
4. data manipulation

You’ll also be able to access a number of exclusive bonus resources to help you along your Data Science journey, including:

• Python For Data Science
• Statistics for Data Science
• NumPy Data Analysis

This Data Science is ideal for people looking to progress their career into a Data Scientist, for those who want to become a Data Analyst, as well as looking to further develop their skills and knowledge.

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