Become a Data Science Pro: 4-Day Python Weekend Bootcamp in Olympia, WA

Looking to build a career in data science or upgrade your analytics skills? Join our 4-Day Data Science with Python Certification Bootcamp, a hands-on training program designed to help you master the essential tools and techniques used by modern data professionals.

This intensive data science training focuses on practical learning, enabling you to work with real datasets, perform analysis, and build predictive models using Python. From understanding raw data to generating meaningful insights, this program walks you through the complete data science lifecycle.

Whether you are a beginner or a working professional, this data science certification course will help you develop the confidence to solve real-world business problems using data.

This program is designed to give you job-ready skills in data science with Python through real-time learning and applied concepts.

• Strong foundation in Python for data science applications
• Hands-on experience with data cleaning and preprocessing
• Ability to analyze and interpret structured and unstructured data
• Skills to create impactful data visualizations
• Understanding of statistics used in data science
• Exposure to machine learning algorithms and predictive modeling
• Practical project experience to build your portfolio
• Recognition through a data science certification

Python for Data Science: Learn Python basics and how it is applied in analytics and automation.

Data Wrangling & Preparation: Handle missing data, transform datasets, and prepare data for analysis.

Data Analysis & Visualization: Generate insights using visualization tools and storytelling techniques.

Statistics for Data Science: Understand key statistical concepts used in real-world analytics.

Machine Learning Basics: Explore regression and classification models used in data science projects.

Capstone Project: Work on a real-time project to apply your learning across the entire workflow.

• 4-day instructor-led data science classroom training
• Hands-on labs with real business datasets
• Practical assignments and mini projects
• Interactive sessions with expert trainers
• Study materials and reusable code notebooks
• Career guidance for data science roles
• Certificate of completion for data science certification

This data science with Python training is perfect for:

• Aspiring data science professionals
• IT professionals transitioning into analytics
• Business analysts and consultants
• Students and recent graduates
• Anyone interested in earning a data science certification

Python has become the backbone of data science, analytics, and machine learning. Its simplicity, flexibility, and powerful libraries make it the most preferred language for data professionals worldwide.

Completing a data science certification with Python can open doors to roles such as:

• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Intelligence Analyst

• 10% OFF for 3–5 participants
• 15% OFF for 6–10 participants
• 20% OFF for 15+ participants

iCert Global is a global provider of industry-aligned professional training in data science, analytics, IT, and emerging technologies. Their programs combine practical application with expert instruction, preparing professionals for high-demand roles in today’s data ecosystem.

PMP, Agile & Scrum, ITIL, Lean Six Sigma, CCNA, Cloud Computing, Cybersecurity, AI & Machine Learning and more.

For any queries related to the exam, location, course material or more, please connect on:

Phone: +1 713-287-1187
Email: Website: www.icertglobal.com

Info: Python setup & environment (Anaconda, Jupyter Notebook)

Python basics: variables, data types, control flow, functions

Working with lists, tuples, dictionaries, and sets

Data input/output and file handling

Introduction to NumPy for numerical computing

Hands-on: Data exploration with Python

Info: Data cleaning, filtering, and transformation with Pandas

Handling missing and duplicate data

Merging and concatenating datasets

Data aggregation and group operations

Visualization with Matplotlib and Seaborn

Practical exercises: Visual storytelling with charts and plots

Hands-on: Real dataset analysis

Info: Descriptive statistics & inferential statistics

Probability concepts for analysis

Regression fundamentals (linear & logistic)

Evaluating model performance (accuracy, RMSE, ROC, confusion matrix)

Introduction to classification and clustering

Hands-on: Build & evaluate ML models using Scikit-learn

End-to-end mini ML project

Info: Introduction to advanced concepts (feature engineering, model tuning)

Unsupervised learning fundamentals (K-Means, PCA)

Capstone mini project: from data collection to model building

Interpreting and presenting analytical results

Career roadmap & interview preparation tips

Q&A and revision session

Know what’s Happening Next — before everyone else does.

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