The Complete Data Science 2019 Video Course on 4 DVDs

469  Lessons

Duration  27:45 Hours

Course content

Part 1: Introduction

The Field of Data Science - The Various Data Science Disciplines

The Field of Data Science - Connecting the Data Science Disciplines

The Field of Data Science - The Benefits of Each Discipline

The Field of Data Science - Popular Data Science Techniques

The Field of Data Science - Popular Data Science Tools

The Field of Data Science - Careers in Data Science

The Field of Data Science - Debunking Common Misconceptions

Part 2: Probability

Probability - Combinatorics

Probability - Bayesian Inference

Probability - Distributions

Probability - Probability in Other Fields

Part 3: Statistics

Statistics - Descriptive Statistics

Statistics - Practical Example: Descriptive Statistics

Statistics - Inferential Statistics Fundamentals

Statistics - Inferential Statistics: Confidence Intervals

Statistics - Practical Example: Inferential Statistics

Statistics - Hypothesis Testing

Statistics - Practical Example: Hypothesis Testing

Part 4: Introduction to Python

Python - Variables and Data Types

Python - Basic Python Syntax

Python - Other Python Operators

Python - Conditional Statements

Python - Python Functions

Python - Sequences

Python - Iterations

Python - Advanced Python Tools

Part 5: Advanced Statistical Methods in Python

Advanced Statistical Methods - Linear regression with StatsModels

Advanced Statistical Methods - Multiple Linear Regression with StatsModels

Advanced Statistical Methods - Linear Regression with sklearn

Advanced Statistical Methods - Practical Example: Linear Regression

Advanced Statistical Methods - Logistic Regression

Advanced Statistical Methods - Cluster Analysis

Advanced Statistical Methods - K-Means Clustering

Advanced Statistical Methods - Other Types of Clustering

Part 6: Mathematics

Part 7: Deep Learning

Deep Learning - Introduction to Neural Networks

Deep Learning - How to Build a Neural Network from Scratch with NumPy

Deep Learning - TensorFlow 2.0: Introduction

Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks

Deep Learning - Overfitting

Deep Learning - Initialization

Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

Deep Learning - Preprocessing

Deep Learning - Classifying on the MNIST Dataset

Deep Learning - Business Case Example

Deep Learning - Conclusion

Appendix: Deep Learning - TensorFlow 1: Introduction

Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset

Appendix: Deep Learning - TensorFlow 1: Business Case

Software Integration

Case Study - What's Next in the Course?

Case Study - Preprocessing the 'Absenteeism_data'

Case Study - Applying Machine Learning to Create the 'absenteeism_module'

Case Study - Loading the 'absenteeism_module'

Case Study - Analyzing the Predicted Outputs in Tableau

Learn MongoDB Beginner to Advanced 7 Video course Training DVD

Course 01 
Introduction to MongoDB
Duration 2h 27m 

Course 02
Introduction to Mongoose for Node.js and MongoDB
Duration 1h 41m

Course 03
MongoDB Administration
Duration 7h 20m

Course 04
Big Data & Reporting with MongoDB
Duration 2h 26m

Course 05
Using MongoDB with ASP.NET MVC
Duration 3h 12m

Bonus Course  06
Learning MongoDB
Duration: 7 hours 22 minutes

Bonus Course  07
Learning MongoDB
Duration: 3 hours and 26 minutes

Buy This Course DVD Now @

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Data Pipelines with Python And Scaling Python for Big Data Video Training DVD Rs 425/-

Total Training Duration : 10.30 hours

Data Pipelines with Python
Duration: 3h 40m

Chapter: Introduction
 Welcome To The Course 02m 52s
About The Author 01m 55s
How To Access Your Working Files 01m 15s
Chapter: Automation 101
 Introduction To Automation 02m 47s
Adventures With Servers 06m 37s
Being A Good Systems Caretaker 06m 3s
What Is A Queue? 02m 31s
What Is A Consumer? What Is A Producer? 01m 59s
Chapter: Easy Task Processing With Celery
 Why Celery? 01m 48s
Celery Architecture & Set Up 05m 25s
Writing Your First Tasks 07m 48s
Deploying Your Tasks 06m 8s
Scaling Your Workers 08m 52s
Monitoring With Flower 05m 5s
Advanced Celery Features 06m 0s
Chapter: Scaling Data Analysis With Dask
 Why Dask? 03m 1s
First Steps With Dask 10m 8s
Dask Bags 10m 18s
Dask Distributed 09m 58s
Chapter: Data Pipelines With Luigi & Airflow
 What Are Data Pipelines? What Is Dag? 02m 36s
Luigi And Airflow: A Comparison 05m 49s
First Steps With Luigi 07m 12s
More Complex Luigi Tasks 09m 16s
Introduction To Hadoop 08m 21s
First Steps With Airflow 08m 7s
Custom Tasks With Airflow 09m 15s
Advanced Airflow: Subdags And Branches 11m 17s
Using Luigi With Hadoop 10m 15s
Chapter: Other Workflow Frameworks
 Apache Spark 08m 28s
Apache Spark Streaming 06m 31s
Django Channels 09m 38s
And Many More 05m 59s
Chapter: Testing With Pipelines
 Introduction To Testing With Python 07m 24s
Property-Based Testing With Hypothesis 06m 8s

Scaling Python for Big Data
Duration: 7h 3m

Welcome To The Course 00:02:53
About The Author 00:01:55
How To Access Your Working Files 00:01:15
Introduction To Automation 00:02:48
Adventures With Servers 00:06:37
Being A Good Systems Caretaker 00:06:03
What Is A Queue? 00:02:32
What Is A Consumer? What Is A Producer? 00:02:00
Why Celery? 00:01:49
Celery Architecture & Set Up 00:05:25
Writing Your First Tasks 00:07:49
Deploying Your Tasks 00:06:08
Scaling Your Workers 00:08:52
Monitoring With Flower 00:05:05
Advanced Celery Features 00:06:00
Why Dask? 00:03:01
First Steps With Dask 00:10:08
Dask Bags 00:10:18
Dask Distributed 00:09:58
What Are Data Pipelines? What Is Dag? 00:02:37
Luigi And Airflow: A Comparison 00:05:50
First Steps With Luigi 00:07:12
More Complex Luigi Tasks 00:09:17
Introduction To Hadoop 00:08:21
First Steps With Airflow 00:08:07
Custom Tasks With Airflow 00:09:16
Advanced Airflow: Subdags And Branches 00:11:17
Using Luigi With Hadoop 00:10:15
Apache Spark 00:08:28
Apache Spark Streaming 00:06:32
Django Channels 00:09:39
And Many More 00:05:59
Introduction To Testing With Python 00:07:24
Property-Based Testing With Hypothesis 00:06:09
What's Next? 00:03:57
Introduction to PySpark
Introduction And Course Overview 00:02:01
About The Author 00:01:02
Installing Python 00:04:38
Installing iPython And Using Notebooks 00:06:28
How To Access Your Working Files 00:01:15
Download And Setup 00:03:24
Running The Spark Shell 00:05:35
Running The Spark Shell With iPython 00:06:38
What Is A Resilient Distributed Dataset - RDD? 00:04:54
Reading A Text File 00:03:34
Actions 00:02:13
Transformations 00:02:30
Persisting Data 00:04:11
Map 00:03:04
Filter 00:03:56
Flatmap 00:03:16
MapPartitions 00:04:07
MapPartitionsWithIndex 00:01:51
Sample 00:02:36
Union 00:01:11
Intersection 00:01:28
Distinct 00:02:02
Cartesian 00:03:17
Pipe 00:03:40
Coalesce 00:02:12
Repartition 00:02:29
RepartitionAndSortWithinPartitions 00:03:58
Reduce 00:04:19
Collect 00:01:56
Count 00:03:05
First 00:01:20
Take 00:01:05
TakeSample 00:03:03
TakeOrdered 00:02:10
SaveAsTextFile 00:04:09
CountByKey 00:02:40
ForEach 00:03:11
GroupByKey 00:02:31
ReduceByKey 00:03:30
AggregateByKey 00:03:44
SortByKey 00:02:47
Join 00:04:16
CoGroup 00:02:09
WholeTextFile 00:03:15
Pickle Files 00:03:59
HadoopInputFormat 00:05:35
HadoopOutputFormat 00:05:31
Broadcast Variables 00:04:17
Accumulators 00:05:08
Using A Custom Accumulator 00:04:52
Partitioning 00:07:56
Spark Standalone Cluster 00:04:26
Mesos 00:03:38
Yarn 00:02:28
Client Versus Cluster Mode 00:02:41
Spark Streaming 00:04:21
Dataframes And SQL 00:03:28
MLlib 00:04:29
Resources And Where To Go From Here 00:01:02
Wrap Up 00:01:28

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This DVDs are only suitable for a PC/laptop/Mac; it WILL NOT play on a TV 

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