Web Scraping Techniques in Python

Gathering Data from Websites to Fuel Your Analytics and Insights

  • Schedule

    5 – 7 March 2024
    09.00 – 15.00 WIB

  • Investment

    Rp. 5.550.000


Hours Course




Our ‘Web Scraping with Python’ course is designed to be beginner-friendly, focusing on teaching participants how to gather data from the web easily and responsibly. We start with the basics of Python programming and data handling, making it simple for those new to coding. Then, we introduce web scraping, showing how to use tools like BeautifulSoup and Selenium to collect data from websites. The course is hands-on, guiding you through organizing the data you collect and using it effectively. By the end of the course, you’ll not only be comfortable with Python and web scraping but also know how to do it ethically, respecting website rules and data usage.

Course Syllabus

  • Working with Jupyter Notebook: Gain hands-on experience using the Jupyter Notebook environment, a popular tool for interactive coding and data analysis.
  • Python Syntaxes and Jargons: Learn the fundamental syntax and terminology of Python programming to build a solid foundation for data analysis.
  • Introduction to Dataframe: Understand the concept of a DataFrame, a key data structure in Python for handling and analyzing tabular data.
  • Reading & Extracting Dataframes: Explore methods to read and extract data from various sources, preparing it for analysis.
  • Python Data Types: Familiarize yourself with different data types in Python, essential for effective data manipulation.
  • Exploratory Data Analysis: Learn techniques for exploring and summarizing data to gain insights and identify patterns.
  • Categorical and Numerical Variables: Understand the distinction between categorical and numerical variables and their significance in data analysis.
  • Using Panda’s Built-in Statistics summary: Explore Pandas, a powerful library for data manipulation, and learn to generate statistical summaries for better understanding.
  • Indexing and Subsetting: Master the techniques of indexing and subsetting data to extract relevant information for analysis.
  • Understanding the Concept of Web Scraping: Grasp the fundamental concept of web scraping and its application in extracting data from websites.
  • Types of Web Scraping: Explore different approaches to web scraping, including static and dynamic scraping, and choose the appropriate method for specific tasks.
  • Ethical Considerations: Learn the importance of ethical web scraping, respecting website rules and legal considerations.
  • Navigating and Understanding Website Structure: Develop skills in navigating websites and understanding their structure to efficiently locate and extract data.
  • HTML Basics for Web Scraping: Gain a basic understanding of HTML, the markup language of websites, to facilitate effective web scraping.
  • Introduction to BeautifulSoup for Parsing HTML: Learn to use BeautifulSoup, a Python library, for parsing HTML and extracting data from web pages.
  • Scraping Data from Web Pages: Explore practical techniques for scraping data from web pages using BeautifulSoup.
  • Introduction to Selenium for Dynamic Web Scraping: Understand the role of Selenium in dynamic web scraping and automating interactions with web elements.
  • Automating Web Interactions with Selenium: Dive into the automation capabilities of Selenium for interacting with websites, streamlining the scraping process.
  • Handling Forms and User Inputs: Learn how to navigate and interact with web forms, enabling the scraping of data from pages with interactive elements.
  • Scraping JavaScript-rendered Pages: Understand the challenges and solutions associated with scraping data from pages rendered using JavaScript.
  • Saving Scraped Data into Various Formats (CSV, JSON, etc.): Master the techniques for saving scraped data into different file formats for further analysis and sharing.
  • Structuring Data for Analysis: Learn effective strategies for organizing and structuring scraped data to facilitate analysis and decision-making.

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.