Social Media Analytics with Dash

Harnessing Digital Conversations for Real-time Social Trend Analysis

  • Schedule

    25 – 27 June 2024

    09.00 – 15.00 (WIB)

  • Investment

    Rp. 5.550.000


Hours Course




In this dynamic course, participants will embark on a comprehensive exploration of Social Media Analytics using the powerful combination of Python and Dash. Beginning with Python for Data Analysis, participants delve into dataframes, Python data types, and statistical summaries. The course then progresses to Data Wrangling and Visualization, introducing techniques for reshaping data and utilizing the Plotly Python graphics library.

The highlight is the construction of a Monthly Social Media Analytics Dashboard with Dash, covering basic Python programming and essential Dash layout components.  By the end of the course, participants will not only be adept at wielding Python for robust data analysis but will also possess the capability to create interactive and visually compelling social media analytics dashboards using Dash, empowering them to glean insights from the ever-evolving landscape of digital discourse.

Course Syllabus

  • Reading data: Understand how to import data into Python using pandas from a comma-separated values (CSV) file.
  • Python data types: Explore the various data types in Python and their relevance in data analysis.
  • Data frame structure: Learn the fundamental structure of a pandas DataFrame and how it organizes tabular data.
  • Using pandas built-in statistics summary: Gain proficiency in generating summary statistics for a DataFrame using pandas.
  • Slicing dataframes: Master the technique of extracting specific portions of data from a DataFrame.
  • Indexing using .loc and .iloc: Explore loc and iloc methods for selecting data based on labels and integer-location.
  • Conditional subsetting: Learn how to filter data based on specific conditions.
  • Reference and Copying: Understand the implications of referencing and copying DataFrames in Python.
  • Importing requirements: Explore the process of importing necessary libraries and packages for creating a reproducible analysis environment.
  • Exporting requirements: Understand how to save and share the list of requirements to recreate the analysis environment.
  • Working with multi-index dataframe: Learn techniques for handling data with multiple hierarchical indices.
  • Data Reshaping: Explore methods such as stack, unstack, melt, and pivot for restructuring data.
  • Plotly python graphics library: Introduce participants to Plotly as a powerful library for interactive data visualizations.
    • Introduction to plotly express: Understand the basics of Plotly Express for creating quick and easy visualizations.
    • Basic visual enhancements: Learn techniques to enhance the visual appeal of plots.
  • Many ways to visualize a context: Explore various methods of visualizing data in different contexts.
    • Categorical ranking: Visualize data based on categorical rankings.
    • Data distribution: Represent the distribution of data.
    • Correlation between data: Visualize relationships and correlations between variables.
    • Time-based evolution: Understand how to visualize data changes over time.
  • Python object and data structures: Gain a foundational understanding of Python’s objects and data structures.
  • Python statements: Learn essential Python statements, including if-else statements, for loops, and functions.
  • Dash basic layout: Understand the basics of creating layouts in Dash for building interactive web applications.
  • Dash components: Explore HTML components, Bootstrap components, and core components used in Dash applications.

Course Receivables:

  • Lecturer’s Notes

    Including Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout each course.

  • Certification of Completion

    Show current employer hat you’ve completed the course with a signed certificate of completion.

  • Quality Learning Environment

    We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, and small group size.

  • Refreshments & Coffee Break

    In our commitment to delivering a premium experience, we collaborate with leading catering services in Jakarta. Our aim is to ensure that all participants are served delectable lunches and revitalizing coffee breaks.


Courses in this series cater to a diverse audience: from casual learners and working professionals to those venturing into data science and machine learning without a programming background.

We recognize that many students may not have prior expertise in statistics, mathematics, or algebra. Therefore, our courses are designed with a gentle learning curve, placing a strong emphasis on hands-on experience and individualized instruction. Our dedicated team of instructors and teaching assistants ensure personalized guidance every step of the way.

Teaching Methodology:

Students work through tons of real-life examples using sample datasets donated by our mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.