Data Science – Master Analytics and become Data Scientist
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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

Requirements

Basic computer knowledge is enough.

Description

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.

Who this course is for:

Who wants to become data scientist and data analyst

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