Social media usage has skyrocketed in recent years, but never more so than it has since the COVID-19 outbreak, as lockdowns restrictions shifted user behavior and habits towards more digital dependency. While data science has certainly played a significant role in social media, and vice versa, the pandemic has forever altered the impact and importance of analytics in this space. Social media platforms have become a playground for real-time trend analysis and the focal point for modern business marketing strategies.
The trajectory of social media’s role in business development is going to increase in the coming years, and corresponding career roles will emerge as a result. We’re already seeing the popularity of job skills in this field, including web analysts, AI Engineer, machine learning engineers, digital marketing specialists, social media analysts, and advanced mobile marketers, and now is the best possible time for both practicing and aspiring industry professionals to be exploring the collision between data science and social media.
Acceleration of Data Sources
The remarkable rise in social media usage has ushered in a growing accumulation of both structured and unstructured data from various formats, such as pictures, videos, sounds, text, and geolocations.
Social media has progressed to become a critical driver for collecting and spreading information across sectors like entertainment and media, journalism, business, politics, and science. This growth opens up new potential for analysis and pattern discovery that provide insights into key challenges, trends, influences, and market and social changes.
The complexity of performing data discovery, collection, and preparation for analytics and predictive modeling often depends on the particular application domain, data source and format, methods, and goals. Data science techniques such as big data analytics, data mining, machine learning, and AI are widely used to this end. In today's day and age, learning Data Science has become extremely important, regardless of your working domain.
For example, a marketing firm looking to identify patterns in customer behavior to forecast future purchasing habits would need to utilize predictive analytics and modeling, including cluster models, regression analysis, and cluster filtering, all of which are rooted in statistical and ML algorithms.
Similarly, social media platforms like Facebook, Instagram, and LinkedIn use ML, AI, and Natural Language Processing (NLP) to personalize user experiences, parse massive volumes of data, recognize people in pictures, and guide content development.
Broadening Use Cases
The correlation between data science and social media has been repeatedly demonstrated across numerous use cases, shaping business strategies, scientific and medical research, product development, and marketing optimization.
Enhancing Decision Making
Organizations analyze big data to uncover trends in real-time and use these competitive insights to inform their decision making, minimize business risks, and connect that social data to their bottom line.
Medical and Scientific Research
Social media is a vital healthcare resource to mitigate misinformation, offer real-time updates to the public, and raise awareness about health issues. Public healthcare organizations, academic researchers, and scientists use data science to take advantage of the availability and accessibility of data for educational, collaboration, and research recruitment purposes.
Companies use ML driven social listening techniques for smart analysis of natural language content, along with videos and photos. This helps them get a hold of early trends or rapidly changing preferences so they can develop products that best fit the market.
Marketing Strategy Improvement
Social media analytics and big data analytics are used to support marketing strategies, including measuring marketing performance KPIs, monitoring the effectiveness of campaigns, and creating contextualized, personalized ads and content based on customer sentiment.
Monitoring COVID-19’s Impact
Embark on a Data Science Career Journey
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Program Name Data Scientist Master's Program Post Graduate Program In Data Science Post Graduate Program In Data Science Geo All Geos All Geos Not Applicable in US University Simplilearn Purdue Caltech Course Duration 11 Months 11 Months 11 Months Coding Experience Required Basic Basic No Skills You Will Learn 10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more 8+ skills including
Exploratory Data Analysis, Descriptive Statistics, Inferential Statistics, and more
8+ skills including
Supervised & Unsupervised Learning
Data Visualization, and more
Additional Benefits Applied Learning via Capstone and 25+ Data Science Projects Purdue Alumni Association Membership
Free IIMJobs Pro-Membership of 6 months
Resume Building Assistance
Upto 14 CEU Credits Caltech CTME Circle Membership Cost $$ $$$$ $$$$ Explore Program Explore Program Explore Program
Disruption Shapes Career Opportunities
Traditional forms of communication have been fundamentally disrupted by social media, presenting emerging opportunities for data science practitioners to guide and support everything from academic research and health awareness to digital marketing and service development.
For those who want to broaden their skill horizons in data science and social media, Simplilearn offers specific educational courses in Data Science and Business Analytics to guide both existing and potential career paths.