Data Science - Master Analytics and become Data Scientist

Learn Python, RLang, Neural networks, ANN, Deep learning - Tools, softwares like knime,spark,scipy, Tableau and others

What you'll learn:

• Data science and usage of tools and softwares

Data Science and Data Analytics course covers wide range of topics from language to tools and softwares.

49 videos of around 8 hours duration.

Section Topic Duration (hh:mm:ss)

1. Data Science

1.1 Data Science introduction 00:09:50

1.2 What is the most powerful language 00:09:36

1.3 Data Science Tools 00:15:46

1.4 Deep Learning 00:14:53

2. Python Language

1.1 Python - introduction 00:09:55

1.2 Install python on windows 00:04:48

1.4 Understanding Python language 00:10:19

1.5 Python coding style PEP8 00:08:31

2.1 Data types - Strings and numbers 00:10:21

2.2 Comments and docstrings 00:03:43

2.3 Control flow statements 00:08:50

2.4 Data structures - Lists and Tuples 00:11:00

3.1 functions 00:11:27

3.5 Modules and Packages - I 00:10:08

3.6 Modules and Packages - II 00:08:05

4.1 Python Classes 00:08:54

4.2 Classes - inheritance - multiple inheritance 00:09:47

4.3 Classes - Method Resolution Order (MRO) - multiple inheritance 00:07:33

5.1 File read write IO operations 00:12:03

7.1 Standard libraries 00:05:14

3. R Language

1.1 R Lang introduction 00:09:57

1.2 Installation of R and R Studio 00:14:46

2.1 R Language – Intro, Vectors and Objects 00:13:33

2.2 R Language –Objects factors 00:04:41

2.3 R Language – Arrays Matrices 00:12:57

2.4 R Language – Lists - Data frames 00:10:35

2.5 R Language – File IO - reading from and writing to files 00:15:20

2.6 R Language – Control flow statements

2.7 R Language – Functions

2.8 R Language – Statistics, Probability distributions 00:11:33

2.9 R Language – Packages - Create, build, install and package 00:13:47

2.10 R Language – Plots

2.11 RLang and DataScience - Tidyverse 00:06:54

2.12 Tidyverse - ggplot2 00:10:45

3.1 R Language secrets

4. KNIME

1.1 KNIME Introduction 00:04:43

1.2 KNIME installation and setup 00:07:12

1.3 KNIME Analytics Platform Practice session 00:15:43

5. SciPY

1.1 Scipy introduction 00:10:24

2.1 Numpy introduction 00:06:15

2.2 Numpy - practice session 00:12:36

3.1 Pandas-Python Data Analysis Library 00:06:31

3.2 Pandas- practice session 00:14:29

4.1 Matplotlib - introduction 00:04:38

4.2 Matplotlib - practice session 00:10:15

5.1 Interactive Python - IPython introduction 00:05:06

6.1 SymPy 00:08:24

6. Tableau

1.1 Tableau - introduction 00:11:37

1.2 Tableau Desktop public - Practice session 1 00:17:46

1.3 Tableau Desktop public - Practice session WDC 00:06:21

Data Science is evolving science and have appetite for analytics and this course will walk you through the required skills.