Data Science - Master Analytics and become Data Scientist

Data Science - Master Analytics and become Data Scientist
Data Science - Master Analytics and become Data Scientist

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Data Science - Master Analytics and become Data Scientist. Learn Python, RLang, Neural networks, ANN, Deep learning - Tools, softwares like ...
What you'll learn

  • Data science and usage of tools and softwares

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.

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