Social Media Data Analytics

Go to Course: https://www.coursera.org/learn/social-media-data-analytics

Introduction

### Course Review: Social Media Data Analytics on Coursera In the digital age, social media has become a treasure trove of data, offering insights that can help companies, researchers, and individuals make informed decisions. For anyone looking to delve into the world of social media analytics, the **Social Media Data Analytics** course offered on Coursera is an excellent choice. This course provides a comprehensive foundation in utilizing various APIs, data processing techniques, and sentiment analysis, equipping learners with the practical skills needed to analyze social media data. #### Course Overview This course stands out with its well-structured curriculum, aiming to provide learners with valuable outcomes upon completion. By the end of the course, participants will be able to: - Utilize various Application Programming Interface (API) services to collect data from key social media platforms like YouTube, Twitter, and Flickr. - Process and analyze structured data using statistical methods like correlation, regression, and classification. - Conduct sentiment analysis on unstructured textual data to decipher user sentiments and trends. The course syllabus is divided into four main units, each building upon the knowledge acquired in the previous sections. #### Syllabus Breakdown 1. **Introduction to Data Analytics** In this foundational unit, learners are introduced to the key concepts of social media data analytics. The distinction between structured and unstructured data is explained, with a strong emphasis on the methods used to analyze structured data. Participants will also explore various visualization techniques to effectively present their findings. This unit is critical, as it sets the groundwork for understanding how to collect and interpret social media data. 2. **Collecting and Extracting Social Media Data** This unit is where the hands-on aspect of the course begins. Learners will be guided to extract data from Twitter and YouTube through Python scripting. Emphasis is placed on setting up developer accounts and navigating data collection APIs. Having programming experience in Python and R is crucial at this stage, as the unit focuses on practical applications of the programming languages for data extraction. 3. **Data Analysis, Visualization, and Exploration** Moving deeper into data examination, this unit focuses on analyzing and visualizing the previously collected social media data. Participants will be engaging with statistical analyses using Python and R, including correlation and regression techniques. Additionally, learners will work with a larger dataset from Yelp for a more comprehensive analysis, making this unit particularly beneficial for gaining practical experience with real-world data. 4. **Case Studies** The final unit solidifies the learners' skills through practical case studies. Students will conduct sentiment analysis using Python and carry out basic text mining with R. This real-world application not only reinforces the concepts learned but also provides insight into the practical challenges one might face in the field of social media data analytics. The course concludes with resources for further learning, enabling learners to continue expanding their knowledge. #### Recommendations The **Social Media Data Analytics** course is highly recommended for those looking to enhance their data analytics skills in the context of social media. It is suitable for beginners who have a basic understanding of Python and R, as well as experienced data analysts wanting to add a new dimension to their skill set. The course’s hands-on approach, combined with the theoretical insights provided, makes for an enriching learning experience. If you are an aspiring data analyst, marketer, or simply someone intrigued by the data-driven insights of social media, this course will empower you to effectively navigate and analyze social media landscapes. The skills you gain will be applicable across various industries, especially in marketing, research, and product development. ### Conclusion In conclusion, Coursera's **Social Media Data Analytics** course is a comprehensive program that combines theoretical knowledge with practical applications. Its structured approach, coupled with case studies and hands-on exercises, equips learners to tackle real-world challenges associated with social media data. With the increasing importance of data analytics in today’s data-centric world, this course is a worthwhile investment in your professional development. Sign up today, and unlock the power of social media data!

Syllabus

Introduction to Data Analytics

In this first unit of the course, several concepts related to social media data and data analytics are introduced. We start by first discussing two kinds of data - structured and unstructured. Then look at how structured data, the primary focus of this course, is analyzed and what one could gain by doing such analysis. Finally, we briefly cover some of the visualizations for exploring and presenting data.Make sure to go through the material for this unit in the sequence it's provided. First, watch the four short videos, then take the practice test, followed by the two quizzes. Finally, read the documents about installation and configuration of Python and R. This is very important - before proceeding to the next units, make sure you have installed necessary tools, and also learned how to install new packages/libraries for them. The course expects students to have programming experience in Python and R.

In this unit we will see how to collect data from Twitter and YouTube. The unit will start with an introduction to Python programming. Then we will use a Python script, with a little editing, to extract data from Twitter. A similar exercise will then be done with YouTube. In both the cases, we will also see how to create developer accounts and what information to obtain to use the data collection APIs. Once again, make sure to go item-by-item in the order provided. Before beginning this unit, ensure that you have all the right tools (Python, R, Anaconda) ready and configured. The lessons depend on them and also your ability to install required packages.

In this unit, we will focus on analyzing and visualizing the data from various social media services. We will first use the data collected before from YouTube to do various statistics analyses such as correlation and regression. We will then introduce R - a platform for doing statistical analysis. Using R, then we will analyze a much larger dataset obtained from Yelp. Make sure you have covered the material in the previous units before proceeding with this. That means, having all the tools (Anaconda, Python, and R) as well as various packages installed. We will also need new packages this time, so make sure you know how to install them to your Python or R. If needed, please review some basic concepts in statistics - specifically, correlation and regression - before or during working on this unit.

In the final unit of this course, we will work on two case studies - both using Twitter and focusing on unstructured data (in this case, text). The first case study will involve doing sentiment analysis with Python. The second case study will take us through basic text mining application using R. We wrap up the unit with a conclusion of what we did in this course and where to go next for further learning and exploration.