Data Science Master Analytics and become Data Scientist

Data Science – Master Analytics and become Data Scientist, available at $44.99, has an average rating of 3.45, with 49 lectures, based on 50 reviews, and has 7025 subscribers.

You will learn about Data science and usage of tools and softwares This course is ideal for individuals who are Who wants to become data scientist and data analyst It is particularly useful for Who wants to become data scientist and data analyst.

Enroll now: Data Science – Master Analytics and become Data Scientist

Summary

Title: Data Science – Master Analytics and become Data Scientist

Price: $44.99

Average Rating: 3.45

Number of Lectures: 49

Number of Published Lectures: 49

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Data science and usage of tools and softwares

Who Should Attend

Who wants to become data scientist and data analyst

Target Audiences

Who wants to become data scientist and data analyst

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.

Course Curriculum

Chapter 1: Data Science

Lecture 1: 1.1 Data Science introduction

Lecture 2: 1.2 What is the most powerful language

Lecture 3: 1.3 Data Science Tools

Lecture 4: 1.4 Deep Learning, NN and ANN

Chapter 2: Python Language

Lecture 1: 1.1 Python – introduction

Lecture 2: 1.2 Install python on windows

Lecture 3: 1.4 Understanding Python language

Lecture 4: 1.5 Python coding style PEP8

Lecture 5: 2.1 Data types – Strings and numbers

Lecture 6: 2.2 Comments and docstrings

Lecture 7: 2.3 Control flow statements

Lecture 8: 2.4 Data structures – Lists and Tuples

Lecture 9: 3.1 functions

Lecture 10: 3.2 Modules and Packages – I

Lecture 11: 3.3 Modules and Packages – II

Lecture 12: 4.1 Python Classes

Lecture 13: 4.2 Classes – inheritance – multiple inheritance

Lecture 14: 4.3 Classes – Method Resolution Order (MRO) – multiple inheritance

Lecture 15: 5.1 File read write IO operations

Lecture 16: 6.1 Standard libraries

Chapter 3: R Language

Lecture 1: 1.1 R Language introduction

Lecture 2: 1.2 Installation of R and R Studio

Lecture 3: 2.1 R Language – Intro, Vectors and Objects

Lecture 4: 2.2 R Language –Objects factors

Lecture 5: 2.3 R Language – Arrays Matrices

Lecture 6: 2.4 R Language – Lists – Data frames

Lecture 7: 2.5 R Language – File IO – reading from and writing to files

Lecture 8: 2.8 R Language – Statistics, Probability distributions

Lecture 9: 2.9 R Language – Packages – Create, build, install and package

Lecture 10: 2.11 R Language and DataScience – Tidyverse

Lecture 11: 2.12 Tidyverse – ggplot2

Chapter 4: KNIME

Lecture 1: 1.1 KNIME Introduction

Lecture 2: 1.2 KNIME installation and setup

Lecture 3: 1.3 KNIME Analytics Platform Practice session, demo

Chapter 5: Scipy

Lecture 1: 1.1 Scipy introduction

Lecture 2: 2.1 Numpy introduction

Lecture 3: 2.2 Numpy – practice session

Lecture 4: 3.1 Pandas-Python Data Analysis Library

Lecture 5: 3.2 Pandas- practice session

Lecture 6: 4.1 Matplotlib – introduction

Lecture 7: 4.2 Matplotlib – practice session

Lecture 8: 5.1 Interactive Python – IPython introduction

Lecture 9: 6.1 SymPy

Chapter 6: Tableau

Lecture 1: 1.1 Tableau introduction

Lecture 2: 1.2 Tableau Desktop public – Practice session

Lecture 3: 1.3 Tableau Desktop public – Practice session – wdc

Chapter 7: Anaconda Distribution

Lecture 1: 1.1 Anaconda introduction

Lecture 2: 1.2 Anaconda – installation

Lecture 3: 1.3 Anaconda Navigator

Instructors

Kaushik Vadali
Cloud Infra and Info Security professional at CBTU

Rating Distribution

1 stars: 6 votes

2 stars: 5 votes

3 stars: 10 votes

4 stars: 15 votes

5 stars: 14 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!