Python Data Science Training Course || Chrono Learn

Course Details

Data Science
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Python Data Science Training Course

By Chronolearn
11/May/2025
0 Students

Course Description

The Python Data Science Training Course is a comprehensive, hands-on program designed to equip participants with the foundational and advanced skills required to perform data analysis, visualization, and predictive modeling using Python. This course covers essential tools and libraries in the Python ecosystem—including NumPy, Pandas, Matplotlib, Seaborn, and Jupyter Notebooks—enabling learners to manipulate data efficiently and extract meaningful insights. Whether you are a beginner or transitioning into data science, this course emphasizes real-world use cases and industry-relevant projects to help you build a strong portfolio and solve practical data challenges.

What You’ll Learn

By the end of this course, you will be able to:

Understand Python’s role and applications in modern data science.
Use Jupyter Notebooks and IPython for interactive programming and analysis.
Perform high-performance numerical operations using NumPy arrays and broadcasting.
Manipulate and analyze large datasets efficiently with Pandas.
Handle missing data, merge datasets, and apply group-based aggregation methods.
Visualize data trends and patterns using Matplotlib and Seaborn.
Apply debugging and code profiling techniques to optimize performance.
Work with real-time data and time series for in-depth trend analysis.
Build a strong foundation for further learning in machine learning, AI, or big data analytics.

Course Curriculum

Introduction to Python for Data Science
- Overview of Python and its Ecosystem
- Real-World Applications of Python in Data Science
- Key Features and Advantages of Using Python
- Setting Up Python Environments (Anaconda, Jupyter, VS Code)
- Python vs. Other Data Science Languages (R, Julia)

Navigating IPython & Jupyter Notebooks
- Launching IPython and Jupyter Notebook Interfaces
- Essential Keyboard Shortcuts for Productivity
- Magic Commands in IPython
- Running Scripts and External Files using %run, %load
- Timing and Profiling Code with %time, %timeit
- Managing Errors and Debugging Efficiently
- Accessing System Shell via IPython

Data Manipulation with NumPy
- Introduction to NumPy and Its Role in Data Science
- Creating and Working with NumPy Arrays
- Broadcasting and Vectorized Computations
- Mathematical and Statistical Operations
- Boolean Masking and Conditional Logic
- Indexing, Slicing, and Fancy Indexing Techniques
- Working with Structured Arrays
- Real-Time Applications: Numerical Simulations, Image Processing

Data Analysis with Pandas
- Introduction to Pandas Series and DataFrames
- Data Selection, Filtering, and Transformation
- Handling Missing Data with Imputation Techniques
- Combining Datasets: Concatenation, Merging, and Joining
- Grouping and Aggregation for Data Summarization
- Working with Dates, Times, and Time Series
- String Operations in Pandas
- Using eval() and query() for Efficient Queries
- Real-Time Use Case: Exploratory Data Analysis (EDA)

Data Visualization using Matplotlib & Seaborn
- Understanding the Basics of Data Visualization
- Functional vs. Object-Oriented API in Matplotlib
- Line Charts, Bar Plots, and Scatter Plots
- Customizing Charts: Colors, Labels, Legends, Grids
- Multi-Plot Layouts and Subplots
- Creating Heatmaps, Boxplots, and Distribution Plots
- Adding Text Annotations and Styling
- 3D Visualizations with mpl_toolkits.mplot3d
- Enhancing Plots Using Seaborn’s High-Level API

Chronolearn

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