Training BeautifulSoup: Getting Started and Web Data Extraction in Python

$1,155

  • 1 day
  • Onsite
  • 8 people max.
Ref: MOE132
8 people max.
$1,155
In-company rate: from $2,195 for the whole team
On-site and certification exam on quote
1 day
Onsite

Don't let this gap widen

Why this program matters

  • Without mastery of web data extraction with BeautifulSoup in Python, data scientists and developers waste 80% of their time on inefficient manual scraping, stalling critical projects.

  • An average company thus loses 45 000 € per year in productivity and data errors, with 25% of business insights skewed by faulty extractions.

  • These gaps lead to flawed strategic decisions, threatening competitiveness and career stability in a data-driven market.

  • Every month without this expertise compounds delays, allowing competitors to dominate with fresh and reliable data.

Célian Lebacle, Learni trainer · Web & mobile expert
Célian Lebacle

Learni trainer · Web & mobile expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

Trusted by — schools, companies and industrial groups

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  • Aptar
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  • Equans
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  • Ubisoft
  • Microsoft
  • Atos

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft, Microsoft and Atos.

BeautifulSoup training objectives

  • Understand web scraping
  • Discover the BeautifulSoup environment
  • Extract data from HTML pages
  • Navigate, search, and manipulate the HTML tree with BeautifulSoup
  • Clean and organize extracted data for exploitation
  • Handle common anomalies and obstacles during scraping

Program

BeautifulSoup curriculum: 2 modules over 1 day

Module 1 : Fundamentals of Web Scraping and BeautifulSoup

Presentation of web scraping and its uses in business. Legal issues and best practices. Installation and getting started with BeautifulSoup. HTML structure review. Understanding BeautifulSoup syntax. First scripts to retrieve and parse HTML content.

Module 2 : Advanced Data Extraction and Anomaly Management

Advanced navigation techniques in the DOM tree (CSS selectors, find, find_all, select). Handling dynamic pages, common errors, and data cleaning. Retrieving tables, links, and images. Introduction to automation and script robustness. Practical case study on a real site.

How BeautifulSoup skills are assessed

  • Practical exercises after each module
  • Final project scraping data from a public site chosen by the learner
  • Summary self-assessment quiz

How the BeautifulSoup course is taught

  • Live interactive demonstrations
  • Provision of notebooks and example scripts
  • Case studies on various sites (e-commerce, news, etc.)
  • Delivery of comprehensive training materials

BeautifulSoup training: access and practical arrangements

The Training BeautifulSoup: Getting Started and Web Data Extraction in Python program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training BeautifulSoup: Getting Started and Web Data Extraction in Python program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training BeautifulSoup: Getting Started and Web Data Extraction in Python program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.

The Learni story

Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.

Our method

How Learni guarantees the quality of your BeautifulSoup training

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

A child walking to school with a backpack
Our social commitment

A school kit donated to a child for every training booked

To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.

  • Backpack, notebooks and essential supplies
  • Distributed through our partner charities
  • Included, at no extra cost to you
Enroll and offer a kit
FAQ

BeautifulSoup: frequently asked questions

What can participants do after BeautifulSoup: Getting Started and Web Data Extraction in Python training?+
They can understand web scraping, discover the BeautifulSoup environment and extract data from HTML pages. Those are the stated objectives of the BeautifulSoup: Getting Started and Web Data Extraction in Python program at Beginner level, checked by the end-of-session assessment.
What does the BeautifulSoup: Getting Started and Web Data Extraction in Python program cover, day by day?+
Day 1: Fundamentals of Web Scraping and BeautifulSoup. Day 2: Advanced Data Extraction and Anomaly Management. The BeautifulSoup: Getting Started and Web Data Extraction in Python program runs over 1 day, delivered on site.
How is BeautifulSoup: Getting Started and Web Data Extraction in Python training assessed?+
Assessment relies on practical exercises after each module, final project scraping data from a public site chosen by the learner and summary self-assessment quiz. Learni issues a completion certificate together with the individual assessment report at the end of the BeautifulSoup: Getting Started and Web Data Extraction in Python program, which an L&D team can file as evidence.
How much does BeautifulSoup: Getting Started and Web Data Extraction in Python training cost per participant?+
BeautifulSoup: Getting Started and Web Data Extraction in Python training costs $1,155 per participant. For an in-company team, the flat rate starts at $2,195 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does BeautifulSoup: Getting Started and Web Data Extraction in Python training take?+
1 day, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for BeautifulSoup: Getting Started and Web Data Extraction in Python training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before BeautifulSoup: Getting Started and Web Data Extraction in Python training?+
Basic knowledge of Python and HTML.
Is a certificate issued after BeautifulSoup: Getting Started and Web Data Extraction in Python training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for BeautifulSoup: Getting Started and Web Data Extraction in Python training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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