Data Science and Analytics - Upskill with Webdroid

About Course

A Data Science and Analytics program equips learners with the ability to manipulate structured and unstructured data through various tools, algorithms, and software, emphasizing the development of critical Data Science skills. It is essential for participants to understand the data science course outline, which includes the acquisition of these skills, before choosing an educational establishment.

The foundational subjects of any data science curriculum or degree encompass Statistics, Programming, Machine Learning, Artificial Intelligence, Mathematics, and Data Mining, regardless of the course’s delivery mode.

While the data science syllabus remains consistent across various degrees, the projects and elective components may vary. For instance, the B.Tech., Data Science syllabus, compared to the B.Sc., in Data Science, also includes practical labs, projects, and thesis work. Similarly, the M.Sc., in Data Science emphasizes research-oriented studies, including specialized training and research initiatives.

Learn Python - the most popular programming language and for Data Science and Software Development.
Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.
Access and web scrape data using APIs and Python libraries like Beautiful Soup.

Course Content

Section 1 : Data Science and Analytics course Intro

Data Science ML Course Intro

00:00
What is data science?

00:00
Machine Learning Overview

00:00
Who’s this course is for?

00:00
DL and ML Marketplace

00:00
Data Science and ML Job opportunity

00:00
Data Science Job Roles

00:00

Section 2 : Getting started with R

Section 3 : Data type and structures in R

Section Introduction

00:00
Basic Types

00:00
Vectors Part One

00:00
Vectors Part Two

00:00
Vectors – Missing Values

00:00
Vectors – Coercion

00:00
Vectors – Naming

00:00
Vectors – Misc

00:00
Creating Matrices

00:00
10 Lists

00:00
Introduction to Data Frames

00:00
Creating Data Frames

00:00
Data Frames_Helper Functions

00:00
Data Frames – Tibbles

00:00

Section 4 : Intermediate R

Section Introduction Intermediate R

00:00
Relational Operations

00:00
Logical Operators

00:00
Conditional Statements

00:00
Loops

00:00
Functions

00:00
Packages

00:00
Factors

00:00
Dates and Times

00:00
Functional Programming

00:00
Data Import or Export

00:00
Database

00:00

Section 5 : Data Manipulation in R

Data Manipulation in R Section Introduction

00:00
Tidy Data

00:00
The Pipe Operator

00:00
The Filter Verb

00:00
The Select Verb

00:00
The Mutate Verb

00:00
The Arrange Verb

00:00
The Summarize Verb

00:00
Data Pivoting

00:00
JSON Parsing

00:00
String Manipulation

00:00
Web Scraping

00:00

About Course

What Will You Learn?

Course Content

Section 1 : Data Science and Analytics course Intro

Data Science ML Course Intro

What is data science?

Machine Learning Overview

Who’s this course is for?

DL and ML Marketplace

Data Science and ML Job opportunity

Data Science Job Roles

Section 2 : Getting started with R

Getting Started

Basics

Files

R Studio

Tidyverse

Resources

Section 3 : Data type and structures in R

Section Introduction

Basic Types

Vectors Part One

Vectors Part Two

Vectors – Missing Values

Vectors – Coercion

Vectors – Naming

Vectors – Misc

Creating Matrices

10 Lists

Introduction to Data Frames

Creating Data Frames

Data Frames_Helper Functions

Data Frames – Tibbles

Section 4 : Intermediate R

Section Introduction Intermediate R

Relational Operations

Logical Operators

Conditional Statements

Loops

Functions

Packages

Factors

Dates and Times

Functional Programming

Data Import or Export

Database

Section 5 : Data Manipulation in R

Data Manipulation in R Section Introduction

Tidy Data

The Pipe Operator

The Filter Verb

The Select Verb

The Mutate Verb

The Arrange Verb

The Summarize Verb

Data Pivoting

JSON Parsing

String Manipulation

Web Scraping

Section 6 : Data Visualization in R

Data Visualization in R Section Introduction

Getting Started

Aesthetics Mappings

Single Variable Plot

Two Variable Plots

Facets Layering and Coordinate System

Styling and Saving

Section 7 : Creating Reports with R Markdown

Creating Reports with R Markdown

Section 8 : Building Web apps with R Shiny

Section Introduction With R Shiny

Basic App

Other Examples

Section 9 : Introduction to Machine Learning

Intro to Machine Learning – Part 1

Intro to Machine Learning – Part 2

Section 10 : Data Preprocessing

Data Preprocessing Intro

Data Preprocessing

Section 11 : Linear Regression: A Simple Model