Learn Data Science with Python – 4-Day Weekend Bootcamp in Buffalo, NY

Begin your journey into Data Science with our engaging 4-Day Weekend Data Science with Python Classroom Bootcamp. This hands-on training program helps participants build practical knowledge in Python programming, data analysis, visualization, and introductory machine learning using real-world datasets.

During this instructor-led classroom training, learners will explore how to gather, organize, and analyze data while developing the ability to solve real business challenges through data-driven insights.

The program emphasizes experiential learning through coding exercises, guided labs, and project-based activities. By the end of the bootcamp, participants will understand how to convert raw data into valuable insights and build simple predictive models using Python — one of the most widely used tools in modern analytics.

This training blends theoretical concepts with practical industry examples to help professionals develop a solid understanding of modern data science workflows.

Rather than focusing only on theory, this bootcamp prioritizes practical learning and hands-on implementation across the entire data science lifecycle.

• Python fundamentals for data science applications
• Data preparation and transformation using Pandas and NumPy
• Exploratory data analysis and visualization techniques
• Statistical methods to interpret data effectively
• Introduction to machine learning concepts using Scikit-learn
• Capstone project applying the full data science workflow
• Career guidance and resume preparation support

• 4 days of instructor-led weekend classroom sessions
• Hands-on labs using real-world datasets
• Mini capstone project for applied learning
• Python notebooks and study materials included
• Career mentoring and interview preparation
• Digital learning resources provided

• Practical Python skills for data analytics
• Data cleaning, transformation, and visualization methods
• Introduction to machine learning fundamentals
• Project-based hands-on learning approach
• Industry-experienced trainers
• Interactive learning sessions with Q&A
• Certificate of completion

This Data Science bootcamp is suitable for:

• Aspiring Data Scientists and Data Analysts
• IT professionals and software developers
• Business analysts and consultants
• Students and graduates interested in analytics
• Professionals planning a career shift into data-driven roles

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

Day 1: Python Fundamentals & Data Handling
Day 2: Data Cleaning, Wrangling & Visualization
Day 3: Statistics & Machine Learning Basics
Day 4: Predictive Modeling, Projects & Career Guidance

(A detailed agenda will be shared with registered participants.)

Python has become the most widely used programming language for data science, analytics, and machine learning. Organizations across industries rely on Python-based data solutions to drive decision-making, optimize operations, and build intelligent systems.

Learning data science with Python opens doors to high-demand careers in analytics, artificial intelligence, and business intelligence.

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

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