About the Course
This advanced course builds upon foundational Python knowledge, focusing on the practical application of key libraries for solving complex Data Science problems. You will dive deep into statistical computing and visualization with SciPy, Matplotlib, and Seaborn, and learn to build, evaluate, and tune Machine Learning (ML) models using the industry-standard Scikit-learn framework. This is crucial training for aspiring Data Scientists who need to transition from theoretical understanding to building complete, deployable analytical pipelines.
Audience Profile
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Data Analysts and Data Engineers who have completed a Python fundamentals course and are ready for applied ML.
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Professionals seeking hands-on experience in building and evaluating predictive models using Python.
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Individuals preparing for roles requiring proficiency in Scikit-learn, SciPy, and advanced data visualization.
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Anyone looking to integrate statistical analysis and machine learning into existing business applications.
Learning Objectives
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APPLY SCI-PY: Leverage the SciPy library for advanced statistical computing, optimization, and scientific routines.
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VISUALIZE DATA: Master Matplotlib and Seaborn to create insightful, publication-quality visualizations for exploratory data analysis (EDA).
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BUILD ML MODELS: Implement and evaluate core Machine Learning algorithms, including classification, regression, and clustering using Scikit-learn.
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PERFORM MODEL SELECTION: Understand and apply techniques for model evaluation, cross-validation, hyperparameter tuning, and preventing overfitting.
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CREATE ANALYTICAL PIPELINES: Design and implement a complete data science workflow, from data preparation to model deployment simulation.
Prerequisites
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Successful completion of a Python fundamentals course (e.g., TTPS4874) or equivalent working knowledge of Python syntax, functions, and data structures.
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Working knowledge of NumPy and Pandas for data manipulation.
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Basic understanding of statistical concepts (mean, variance, correlation).
What’s included?
- Authorized Courseware
- Intensive Hands on Skills Development with an Experienced Subject Matter Expert
- Hands-on practice on real Servers and extended lab support 1.800.482.3172
- Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
- Academy Code of Honor: Test Pass Guarantee
- Optional: Package for Hotel Accommodations, Lunch and Transportation
With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.
Methods
- Instructor Led (the best training format we offer)
- Live Online Classroom – Online Instructor Led
- Self-Paced Video
Speak to an Admissions Representative for complete details
| Start | Finish | Public Price | Public Enroll | Private Price | Private Enroll |
|---|---|---|---|---|---|
| 12/8/2025 | 12/10/2025 | ||||
| 12/29/2025 | 12/31/2025 | ||||
| 1/19/2026 | 1/21/2026 | ||||
| 2/9/2026 | 2/11/2026 | ||||
| 3/2/2026 | 3/4/2026 | ||||
| 3/23/2026 | 3/25/2026 | ||||
| 4/13/2026 | 4/15/2026 | ||||
| 5/4/2026 | 5/6/2026 | ||||
| 5/25/2026 | 5/27/2026 | ||||
| 6/15/2026 | 6/17/2026 | ||||
| 7/6/2026 | 7/8/2026 | ||||
| 7/27/2026 | 7/29/2026 | ||||
| 8/17/2026 | 8/19/2026 | ||||
| 9/7/2026 | 9/9/2026 | ||||
| 9/28/2026 | 9/30/2026 | ||||
| 10/19/2026 | 10/21/2026 | ||||
| 11/9/2026 | 11/11/2026 | ||||
| 11/30/2026 | 12/2/2026 | ||||
| 12/21/2026 | 12/23/2026 | ||||
| 1/11/2027 | 1/13/2027 |
Curriculum Applied Python for Data Science
Module 1.0 – Advanced Data Manipulation and Preparation Advanced Pandas techniques: Multi-indexing, time series handling, and complex joins. Data normalization, scaling, and feature engineering strategies. Handling categorical data and text features for ML models.
Module 2.0 – Statistical Computing with SciPy Introduction to the SciPy ecosystem and sub-packages. Performing statistical testing, probability distributions, and inferential analysis. Optimization and numerical integration routines using SciPy.
Module 3.0 – Data Visualization Mastery Customizing plots and charts with Matplotlib for detailed analysis. Using Seaborn for high-level statistical visualization (e.g., heatmaps, pair plots). Creating interactive and dynamic visualizations for reporting.
Module 4.0 – Introduction to Machine Learning with Scikit-learn The Scikit-learn framework: model lifecycle, data splitting, and basic API. Implementing Linear Regression and Logistic Regression for predictive modeling. Understanding the Bias-Variance trade-off.
Module 5.0 – Core Machine Learning Algorithms Implementing Decision Trees and Random Forests for classification and regression. Introduction to Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). Performing unsupervised Clustering (K-Means) for segmentation.
Module 6.0 – Model Evaluation and Tuning Evaluating model performance: metrics for classification (accuracy, precision, recall, F1-score) and regression (MSE, R-squared). Applying Cross-Validation techniques for robust model assessment. Hyperparameter tuning using Grid Search and Random Search.
