US organizations that still rely on manual spreadsheet analysis face higher error risk and slower turnaround as datasets become larger and more frequent. Python-based workflows reduce repetitive copying, improve traceability, and make analysis easier to repeat across teams.
Virtual Training
Data Science, AI, and Advanced Analytics
Python Programming for Data Science Online Course
Join our virtual, live instructor-led session and master Python Programming for Data Science Training from anywhere in the world.
10 Days
Duration
Live Online
Delivery
7 Dates
Available
Certificate
Included
Master Python Data Science to automate complex analysis, build predictive models, and generate actionable insights through industry-standard libraries and scalable coding practices.
Upcoming Virtual Training Schedules
Join from anywhere in the world with our live instructor-led sessions
| Code | Start Date | End Date | Duration | Fee | |
|---|---|---|---|---|---|
| PDS-01 | Mon - Fri (10 Days) | USD 1,700 | Reserve my seat → Register my team → | ||
| PDS-01 | Mon - Fri (10 Days) | USD 1,700 | Reserve my seat → Register my team → | ||
| PDS-01 | Weekend (8 Weeks) | USD 1,700 | Reserve my seat → Register my team → | ||
| PDS-01 | Mon - Fri (10 Days) | USD 1,700 | Reserve my seat → Register my team → | ||
| PDS-01 | Mon - Fri (10 Days) | USD 1,700 | Reserve my seat → Register my team → | ||
| PDS-01 | Weekend (8 Weeks) | USD 1,700 | Reserve my seat → Register my team → |
Here's What You'll Learn
Each module tackles real challenges you face in your role
1
Python Environment and Foundation Setup
2
Advanced Data Structures and Control Flow
3
Functional Programming for Data Pipelines
4
Numerical Computing with NumPy
5
Data Manipulation with Pandas Series
6
Structured Analysis with Pandas DataFrames
7
Advanced Data Wrangling and Integration
8
Data Visualization with Matplotlib and Seaborn
9
Statistical Analysis and Hypothesis Testing
10
Machine Learning Fundamentals with Scikit-learn
11
Model Evaluation and Hyperparameter Tuning
12
Working with External Data Sources
13
Automation, Scripting, and Logging
14
Integration and Strategic Reporting
Market-specific guidance for United States
A country-aware view of the pressures, proof points, and practical tools that shape how this course applies locally.
Tools and platforms relevant to this field
6Field-relevant examples that may be featured in training where they support the confirmed scope. Exact coverage depends on participant needs and delivery format.
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Pandas The Pandas development communityUsed for data cleaning, transformation, aggregation, and tabular analysis in Python-based analytics workflows.
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NumPy NumPy communityUsed for numerical computing, array operations, and efficient processing of structured data.
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Matplotlib Matplotlib projectUsed to create charts and visualizations for exploratory analysis and reporting.
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scikit-learn scikit-learn developersUsed to build introductory predictive models and evaluate machine learning workflows.
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Jupyter Notebook Project JupyterUsed for interactive analysis, documentation, and sharing reproducible data science work.
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Visual Studio Code MicrosoftUsed as a common development environment for writing, testing, and maintaining Python data scripts.
Where this course runs
Python Programming for Data Science Training is delivered in the cities below — pick the one that fits your schedule.
