Data Science and Analytics
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.
Course Content
Section 1 : Data Science and Analytics course Intro
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Data Science ML Course Intro
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What is data science?
00:00 -
Machine Learning Overview
00:00 -
Who’s this course is for?
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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 4 : Intermediate R
Section 5 : Data Manipulation in R
Section 6 : Data Visualization in R
Section 7 : Creating Reports with R Markdown
Section 8 : Building Web apps with R Shiny
Section 9 : Introduction to Machine Learning
Section 10 : Data Preprocessing
Section 11 : Linear Regression: A Simple Model
Section 12 : Exploratory Data Analysis
Section 13 : Linear Regression – Real Model
Section 14 : Logistic Regression
Section 15 : Starting A Career in Data Science
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