Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom, Campus Based/Physical Classroom | Video and Text Based |
Course Overview
This certification course is designed to help working professionals acquire the skills and knowledge to become SMI experts. The Post Graduate Certification program in Social Media Analytics and Intelligence by Talentsprint covers topics such as social network analysis, security and privacy on social media, ethics and more.
The programme also provides hands-on experience with real data, hackathons, capstone projects and industry mentorship. The programme is taught by top faculty from IIIT Hyderabad, who are renowned for their research and innovation in the field of social computing. The programme also gives the participants an opportunity to visit the prestigious IIIT Hyderabad campus and become alumni of the institute.
The Highlights
- Certificate of completion IIIT - Hyderabad
- Interactive Live Sessions
- Expert Faculty
- 6 Months Course
Programme Offerings
- video lecture
- Certification
- Get Alumni Status
- Hands-On Lab
- Capstone Project
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIIT Hyderabad |
The Post Graduate Certification program in Social Media Analytics and Intelligence fees amount to Rs. 2,00,000 for Indian candidates. The application fee is Rs. 1,000. EMI payment option is also available.
Post Graduate Certification program in Social Media Analytics and Intelligence fee structure
Details | Amount |
Application Fee | Rs. 1,000 |
Course Fee | Rs. 2,00,000 |
Eligibility Criteria
Academic Qualifications
Bachelor’s degree with a minimum of 50% marks.
Work Experience
A minimum of 1 year of professional experience is required for admission.
What you will learn
Upon completion of the Post Graduate Certification program in Social Media Analytics and Intelligence classes, participants will:
- Learn how to collect, handle, and analyse social media data from platforms such as Twitter, Facebook, Instagram, Reddit, LinkedIn, YouTube, and Moj.
- Derive actionable insights from social media data to use AI and cutting-edge tools to analyse conversations, respond to social signals, synthesise social data, and identify influencer
- Understand the challenges related to trust, credibility, fake news, spam, phishing, fraud, hate crimes, and privacy issues on social media platforms.
- Learn how to conduct social science research using social media data, and apply sociological and psychological theories.
Who it is for
The Post Graduate Certification program in Social Media Analytics and Intelligence syllabus is designed for professionals who are interested in building expertise in Social Media Intelligence. It is particularly beneficial for individuals working in roles such as:
Admission Details
Carefully follow the instructions to get enrolled in the Post Graduate Certification program in Social Media Analytics and Intelligence online course. Browse the URL: https://iiit-h.talentsprint.com/social-media-analytics/
Step 1- Apply for the programme
Step 2- Submit Documents
Step 3- Selection by IIIT-H Committee
Step 4- Join the Programme
The programme selection process will be conducted by IIIT-H, focusing exclusively on the participants' education, work experience, and motivation.
Please submit scanned copies of the following documents within 7 days of application:
- Education Certificate
- Experience Letter/Latest Pay Slip
Application Details
The website asks for personal details and then begins the enrolment process by sending the participant an email.
The Syllabus
- Course outcomes
- Course logistics
- Brief theory and examples about Social media analysis and CSS
Introduction to data collection from social media
- APIs - Twitter, Reddit (Pushshift.io)
- Web Crawling
- BeautifulSoup
- Selenium
- Legality and Ethics of crawling
- Data Handling practices - DBs
- Continuous, and Realtime collection
- Twitter, Facebook, Instagram, Reddit, LinkedIn, YouTube, Moj
Data attributes (Twitter and Reddit)
Quantitative distributions
- Finding influencers using follower/following counts
- Time series distributions
- Detecting anomalies (Virality detection)
Language Analysis
- Pre-processing: Tokenization, Stop words (Custom stop words using tf-idf), Stemming and Lemmatization
- Word Clouds and Chatter Plots
- Topic modeling
- Introduction to Code switch and Code mix data
Network Analysis
- Building Network (Nodes, edges, edge properties)
- Visualizing Networks
- Network topology and what they can mean in social networks
- Centrality metrics and inference with respect to social networks: Page Rank - Popularity, Betweenness - Community Gatekeepers, Degree - Reach
- Community detection
Social Media Marketing 101
- Tools for social media analysis: TweetDeck, Bufferly and other listening tools
Trust, credibility, & fake news on social media
- Evolution of the term fake news (Eg. Credibility-Hoax-Rumor-Fake News-Misinformation-Disinformation)
- Defining different terminology mentioned above
- Types of fake news
- Different ways by which problem is tacked
- Detection (Content and context based)
- Propagation
- Intervention
Spam, phishing, frauds, hate crime, abuse, extremism via online social media
- Information privacy disclosure, revelation, and its effects in online social media
- Privacy on social media: challenges, implication, opportunities
- Collateral damage due to information leakage
- Consumer behavior, analytics & applications
- Effective and usable privacy setting and policies on online social media
Tracking social footprint/identities across different social network
- Attribute based - Matching is done based on metadata like username, photo, DOB, check-ins, etc.
- Language based - Language models are used to identify users based on writing styles
- Network based - Network features of two networks are compared to identify node representing the same user.
- Broader definition of Ethics & Bias in technology space
- Ethics & Bias related to online social media
- Studies (online social and otherwise) that had questionable ethical methodologies
Computer Science and Social media as a tool to conduct social science research
- New data collection frontiers: Online user studies, historic social media data
- Mass collaborations
- Large scale (potentially global) experiments
Examples of using Sociology and Psychological theories to decide research directions
Multimodal Classification of social media data (taught via use cases of CSS)
- NLP + large language models
- Computer vision and CNNs
- Graph Neural Network
Causal Experiment design
- Causation vs Correlation
- Basics of causal inference
- Treatment and control
- Effect
- Confounders and Covariates
Randomized control trials
Methods
- Propensity score
- Regression discontinuity
- Difference in Difference
- Survival analysis and CoX regression
- Double ML
Challenges while using social media data
- Text as a confounder
- Platform dynamics as a confounder
- Accounting of unavailable data