Python Essentials for Data Science: 1 Day Session in Boston, MA

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Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session

The Python for Data Science course provides the essential skills to analyze, visualize, and interpret data using Python. You’ll learn to use popular libraries like Pandas, NumPy, Matplotlib, and Seaborn to explore datasets and uncover insights. Designed for aspiring data scientists and analysts, this course bridges core programming with data-driven decision-making — empowering you to start your journey in the world of data science.

By the end of this course, you will:

• Use Python libraries to load, explore, and manipulate data
• Apply data cleaning techniques to prepare data for analysis
• Create visualizations to convey patterns and insights
• Understand the basics of machine learning workflows
• Perform data-driven decision-making with confidence
• Start your journey toward becoming a skilled data analyst or scientist

Aspiring data scientists, analysts, developers, and students.

This course is designed for anyone ready to explore the world of data science using Python, regardless of prior experience. With expert guidance and real-world datasets, you’ll learn to make sense of data and present your findings visually. Our hands-on approach ensures you don’t just learn theory, but also apply it through practical exercises. Plus, Python is the most popular language in data science — learning it unlocks opportunities in AI, analytics, automation, and more.

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Want to build a data-driven culture within your team?

We offer customizable in-house Python for Data Science training tailored to your organization's goals and data challenges. Looking to upskill your workforce in data handling and analysis? Let us design a program that fits your needs.

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Info:
• Why Python is essential for data science
• Setting up Jupyter Notebook or VS Code
• Overview of data science workflow
• Activity

Info:
• Introduction to DataFrames and Series
• Reading and writing CSV, Excel files
• Data exploration and basic stats
• Activity

Info:
• Understanding arrays and vectorized operations
• Array creation, indexing, reshaping
• Using NumPy for basic calculations
• Case Study

Info:
• Handling missing data and duplicates
• Data type conversions and string operations
• Filtering, sorting, and transforming data
• Activity

Info:
• Plotting line, bar, scatter, histogram charts
• Customizing styles and labels
• Creating multiple plots in a figure
• Activity

Info:
• Heatmaps, pair plots, categorical plots
• Using themes and palettes
• Visualizing data distributions
• Activity

Info:
• What is machine learning?
• Basics of supervised vs unsupervised learning
• Using scikit-learn for simple models
• Case Study

Info:
• Choose a dataset and perform analysis
• Clean, analyze, visualize, and present findings
• Share insights and interpretations
• Activity

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