Exploratory Data Analysis

Learn various EDA techniques and its application to modern business analytics.

Exploratory Data Analysis

Course details

Get more in-depth on exploratory data analysis practice you can perform using pandas in this 12-hour course. Pick up the essential exploratory tools in this library to cover more statistical capabilities of pandas. We will also guide you through one of the most demanding, yet important process in data analytics: data cleansing. Upon completion of this course, you will uncover more possibilities in working with data using pandas.

Please bring along:

  • 1x Laptop
  • Purchased ticket


  • Why and What: Exploratory Data Analysis

    Day 1

  • Cross Tabulation and Pivot Table

    Day 1

  • Date Time Objects

    Day 2

  • Categorical Data Types

    Day 3

  • Treating Duplicates and Missing Values

    Day 4

Course Producer

Samuel Chan

Machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide), a certified professional (certificates from Microsoft, MongoDB, Stanford University, John Hopkins University), and an experienced consultant that has worked with several public-trading companies from his time staying in China, Japan and Singapore.

Between 2017 and 2018, Samuel has trained and consulted with more than 20 companies around Indonesia and a regular guest speaker/trainer in a number of universities in Singapore and Indonesia. He is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies.

4-Day Workshop Modules

Syllabus: Exploratory Data Analysis

Module 1: Exploratory Data Analysis Tools II

  • Frequency Table in pandas
  • Higher Dimensional Table
  • Data Aggregation
  • Using Pivot Table

Module 2: Working with Data Types

  • Working with Date Time Data
  • Working with Categorical Data

Module 3: Dealing with Untidy Data

  • Not a Number (NaN)
  • Checking NaN Values
  • Missing Values Treatment
  • Removing Duplicate Values

Module 4: Graded Assignment

Use item dataset listed in a popular e-commerce website to perform the exploratory data analysis process you have learned. We will explore different product categories in terms of various price and scale range. This module was gathered as part of a larger research work where the analyst wanted to study the price convergence of Indonesia essential household items.

Program Receivables:

  • Cutting Edge Curriculum

    A hands-on coding bootcamp with the opportunity to work on real datasets donated by businesses and the public sector. Coursebooks (PDF/HTML files), data set for practice, reference notes, and working files (R Notebook or Jupyter Notebook) are accessible through our Learning Management System account.

  • Project-Oriented Learning

    Work with real-life cases and learn under the assistance of our qualified instructors throughout the 1-month course.

  • Certification of Completion

    Show current and prospective employers that 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, small group size. Dinners are included for evening workshops.

  • Engaging Community

    Be a part of our data-passionate community with 5000+ members and 300+ alumni.


The Data Analytics Specialization is a 4-week bundle that is curated to accelerate a student’s mastery in building data products, develop a web application, and data visualization.

Students are not assumed to have a working knowledge of Python or prior proficiency in statistics/mathematics/algebra. As such the workshop follows a gentle learning curve and emphasizes hands-on, one-to-one tutoring from our team of instructors and teaching assistants.

Learn data analysis by building:

Students work through tons of real-life examples using sample datasets donated by our team of 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.

Part of Data Analytics Specialization

This course is part of the Algoritma Data Analytics Specialization. Participants are rewarded with a certificate of completion upon passing criteria.