Data : Data Science online training

This Data Science course includes a whole host of practical tips and advice, helping you to develop your data science skills to become the data scientist, data analyst or exiting careers in data science.

In this course you will learn Data Science and Machine Learning Models with R/ Python Programming Language. Machine learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalisation, robot control, time series forecasting, and much more.

This is bundle of two courses:

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

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

Reed courses certificate of completion

Digital certificate - Included

• 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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