Live remote learning · Data science

Python Data Science Training

NumPy, Pandas, Seaborn, and Matplotlib for practical data analysis and visualization. A hands-on learning path for anyone who wants to analyze data, clean datasets, and create clear visual reports with confidence.

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NumPy Arrays and numerical computing

Pandas DataFrames and data cleaning

Seaborn Statistical visualization

Matplotlib Customizable plots and figures

Why this course

Move from basic Python to practical data analysis

Many learners know Python basics but feel stuck when they need to work with real datasets. This course helps bridge that gap by focusing on the tools used every day in data analysis: NumPy for numerical work, Pandas for tabular data, and visualization libraries for clear communication.

The goal is not only to memorize commands. The course builds understanding through practice, so learners can clean data, explore patterns, fix common mistakes, and explain results using well-structured notebooks.

Learning outcomes

At the end of this training, learners will be able to

✓

Use NumPy ndarray objects for creation, indexing, slicing, reshaping, and numerical operations.

✓

Apply vectorization and broadcasting to write cleaner calculations without unnecessary Python loops.

✓

Work with Pandas DataFrames using filtering, sorting, aggregation, groupby, merge, joins, and pivot tables.

✓

Clean datasets by handling missing values, duplicate rows, wrong data types, and basic date/time columns.

✓

Create clear statistical plots with Seaborn for comparisons, distributions, relationships, and categories.

✓

Customize charts with Matplotlib, including labels, titles, legends, figure size, axes, and exported images.

Detailed program

A practical four-module learning path

The course combines short explanations, live demonstrations, and guided notebook exercises.

Working environment and notebook workflow

Set up a comfortable data science workflow using Jupyter notebooks or Google Colab. Learn how to organize notebooks, run cells, install packages, import libraries, read files, and write small reusable analysis steps.

NumPy: numerical computing in Python

Learn the ndarray type, dimensions, dtype, array creation, indexing, slicing, reshaping, aggregation, sorting, vectorized operations, and broadcasting. The focus is on writing calculations clearly and efficiently.

Pandas: tabular data analysis

Work with Series, Index, and DataFrame objects. Practice selection, filtering, cleaning, missing values, duplicate handling, type conversion, concatenation, groupby, merge, joins, pivot tables, dates, and simple rolling operations.

Matplotlib: customizable data visualization

Build and customize charts using Matplotlib. Practice line plots, bar charts, scatter plots, histograms, labels, legends, titles, axes, figure size, subplots, and exporting figures. Seaborn is also introduced for faster statistical visualization.

Learning level

Is this course right for you?

This course is designed for learners who already know basic Python and want to move into data analysis. You do not need advanced programming experience, but you should be familiar with variables, lists, loops, functions, and basic scripts.

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Who can join Students, researchers, professionals, and independent learners.

Recommended background Basic Python knowledge: variables, loops, functions, and simple programs.

Learning support Live explanation, guided exercises, examples, and corrected notebooks.

Teaching method

Short theory, direct practice, and guided exercises

Each topic is introduced with a simple explanation and then practiced immediately in notebooks. Learners work through realistic examples instead of only reading syntax, so every idea connects to a practical data analysis task.

The course pace can be adjusted according to the learner’s background. Exercises, example datasets, and corrected notebooks can be shared after the sessions for continued practice.

FAQ

Common questions

Do I need advanced Python before joining?

No. Basic Python is enough. You should know variables, lists, loops, functions, and how to run simple code.

Why learn NumPy and Pandas together?

NumPy helps with numerical operations, while Pandas makes it easier to work with tables and real datasets. Together, they form the foundation of practical data analysis in Python.

Why is Matplotlib included?

Matplotlib gives you control over charts, labels, axes, legends, figure size, and exports. It is useful when you need to customize plots for assignments, reports, dashboards, or publications.

Will Seaborn also be covered?

Yes. Seaborn is introduced for fast statistical visualization, while Matplotlib is used to understand and customize the final figures.

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Plan your data science learning path

Share your current Python level, preferred schedule, and learning goals. We will suggest a suitable path for coding, data analysis, visualization, or AI.