Digital Marketing
This course is designed to provide an in-depth understanding of digital marketing and its applications in the modern business landscape. The course will cover all aspects of digital marketing, including search engine optimization (SEO), social media marketing, email marketing, content marketing, and mobile marketing.
Enrollment
Pre-register yourself for the next cohort
Program Duration
5-7 months
Learning Format
Online
What You Will Learn
- Fundamentals of Digital Marketing
- Search Engine Optimization (SEO)
- Social Media Marketing
- Email Marketing and Content Marketing
- Mobile Marketing
Course Syllabus
Module 1
Module 2
Module 3
Module 4
Module 5
Module 1
Introduction to Digital Marketing
- Learn about the basic principles of digital marketing, including online advertising, website optimization, and analytics.
Module 2
Search Engine Optimization (SEO)
- This module will cover the strategies and techniques for improving website visibility in search engines such as Google.
Module 3
Social Media Marketing
- This module will teach students about the effective use of social media channels such as Facebook, Twitter, LinkedIn, and Instagram for business growth.
Module 4
Email Marketing
- Learn about creating effective email marketing campaigns and creating content that resonates with target audiences.
Module 5
Content Marketing
- This module will focus on the mobile landscape and how to create mobile-friendly websites, SMS marketing campaigns, and mobile apps
Program Offerings
Practical Skills
You will learn practical skills in emerging careers that make you more valuable and appealing to employers
Projects
You will acquire the knowledge to create portfolio-worthy projects showcasing your skills
Mentorship
Experts working in the industry will mentor you in becoming successful professionals with leadership skills
Module 7
- Week 9
Clean Data, Assignment Prep
Module 8
- Week 10
Cleaning Data with SQL, Verify and Report Cleaning Results
Module 9
- Week 11
Adding Data to Resume + Course Challenge
Module 10
- Week 12
Organizing Data to Begin Analysis, Formatting and Adjusting Data
Module 11
- Week 13
Aggregating Data for Analysis, Performing Data Calculations
Module 12
- Week 14
Visualizing Data, Creating Data Visualizations with Tableau
Module 13
- Week 15
Crafting Data Stories, Developing Presentations and Slideshows
Module 14
- Week 16
Programming and Data Analytics, Programming using R Studio
Module 15
- Week 17
Working with Data in R, More About Visualizations, Aesthetics and Annotations
Module 16
- Week 18
Documentation and Reports, Learn About Capstone Basics
Module 17
- Week 19
Building Your Portfolio + Using Your Portfolioo
Module 18
- Weeks 20 & 21
Putting Your Certificate to Work
Module 19
- Weeks 22 & 23
Introduction to Probability and Data with R
Module 20
- Weeks 24 & 25
Inferential Statistics
Module 21
- Weeks 26 & 27
Linear Regression and Modelling
Module 22
- Weeks 28 & 29
Bayesian Statistics
Module 23
- Weeks 30
Statistics with R Capstone (Final Project)
Module 24
- Weeks 31
Job - Interview Mocking and Training
Module 7
- Week 9
Clean Data, Assignment Prep
Module 8
- Week 10
Cleaning Data with SQL, Verify and Report Cleaning Results
Module 9
- Week 11
Adding Data to Resume + Course Challenge
Module 10
- Week 12
Organizing Data to Begin Analysis, Formatting and Adjusting Data
Module 11
- Week 13
Aggregating Data for Analysis, Performing Data Calculations
Module 12
- Week 14
Visualizing Data, Creating Data Visualizations with Tableau
Module 13
- Week 15
Crafting Data Stories, Developing Presentations and Slideshows
Module 14
- Week 16
Programming and Data Analytics, Programming using R Studio
Module 15
- Week 17
Working with Data in R, More About Visualizations, Aesthetics and Annotations
Module 16
- Week 18
Documentation and Reports, Learn About Capstone Basics
Module 17
- Week 19
Building Your Portfolio + Using Your Portfolioo
Module 18
- Weeks 20 & 21
Putting Your Certificate to Work
Module 19
- Weeks 22 & 23
Introduction to Probability and Data with R
Module 20
- Weeks 24 & 25
Inferential Statistics
Module 21
- Weeks 26 & 27
Linear Regression and Modelling
Module 22
- Weeks 28 & 29
Bayesian Statistics
Module 23
- Weeks 30
Statistics with R Capstone (Final Project)
Module 24
- Weeks 31
Job - Interview Mocking and Training
Module 7
- Week 9
Clean Data, Assignment Prep
Module 8
- Week 10
Cleaning Data with SQL, Verify and Report Cleaning Results
Module 9
- Week 11
Adding Data to Resume + Course Challenge
Module 10
- Week 12
Organizing Data to Begin Analysis, Formatting and Adjusting Data
Module 11
- Week 13
Aggregating Data for Analysis, Performing Data Calculations
Module 12
- Week 14
Visualizing Data, Creating Data Visualizations with Tableau
Module 13
- Week 15
Crafting Data Stories, Developing Presentations and Slideshows
Module 14
- Week 16
Programming and Data Analytics, Programming using R Studio
Module 15
- Week 17
Working with Data in R, More About Visualizations, Aesthetics and Annotations
Module 16
- Week 18
Documentation and Reports, Learn About Capstone Basics
Module 17
- Week 19
Building Your Portfolio + Using Your Portfolioo
Module 18
- Weeks 20 & 21
Putting Your Certificate to Work
Module 19
- Weeks 22 & 23
Introduction to Probability and Data with R
Module 20
- Weeks 24 & 25
Inferential Statistics
Module 21
- Weeks 26 & 27
Linear Regression and Modelling
Module 22
- Weeks 28 & 29
Bayesian Statistics
Module 23
- Weeks 30
Statistics with R Capstone (Final Project)
Module 24
- Weeks 31
Job - Interview Mocking and Training
Frequently Asked Questions
The monthly fee for the courses is Rs 2000 and the registration fee is Rs 500 (it is non-refundable)
Yes, you will receive an international certificate after completing the course.
The eligibility criteria for the courses are age 22-35, unemployed, graduate, available for a full-time job after completion, basic English comprehension, preferably tech background, and computer skills.
