Practical Data Science with Jupyter is a course in hands-on data work with Python in JupyterLab, from a Python refresher and NumPy calculations to loading, cleaning and summarising data with pandas, building charts with Matplotlib and seaborn, and carrying out exploratory data analysis. It suits data analysts and aspiring data scientists who have already completed Python Basic.
Most data science work does not start with building models. It starts with loading scattered data, cleaning it, reshaping it, summarising it and charting it so the picture becomes clear. Python with Jupyter notebooks is the standard toolset for this work, because code, results and explanations of what you found live in one file that is easy to rerun and share with the team.
This course takes learners who have completed basic Python through structured data work in JupyterLab. They review the Python used with data, calculate with NumPy, manage data with pandas 3, read CSV, Excel and JSON files, clean data including missing values, duplicates, text and dates, combine and summarise data, and build charts with Matplotlib and seaborn. The course closes with exploratory data analysis of a real data set, ending in a report inside a notebook. (3 days, 6 hours per day, 18 hours in total, Beginner level.)
Course Details
This course is the 2026 edition of Practical Data Science with Jupyter. It runs for 3 days, 6 hours per day (18 hours in total, 09:00-16:00), as lectures with labs. Beginner level. It keeps the original core of Jupyter, a Python refresher covering lambdas and CSV handling, NumPy, pandas, data cleaning and Matplotlib charts, but installs JupyterLab with uv instead of Anaconda, moves to pandas 3 and NumPy 2, drops XML parsing with lxml.objectify and adds merge, groupby, seaborn and an EDA workshop. The course focuses on preparing and exploring data; learners who want to build predictive models can continue with Data Science with Python. All tools are open source and free. Learners take home a lab guide, notebooks and sample data sets for further practice.
Section 1: Lab: Setting Up JupyterLab
Section 2: Lab: Python for Data Work
Section 3: Lab: Data Structures and Functions
Section 4: Lab: The NumPy ndarray
Section 5: Calculations and Statistics with NumPy
Section 6: Lab: Getting Started with pandas 3
Section 7: Lab: Selecting, Filtering and Calculating
Section 8: Lab: Reading and Writing Data Files
Section 9: Lab: Data Cleaning, Part 1
Section 10: Lab: Data Cleaning, Part 2
Section 11: Lab: Combining and Summarising Data
Section 12: Lab: Charts with Matplotlib
Section 13: Lab: Charts with seaborn and pandas
Section 14: Exploratory Data Analysis (EDA)
Section 15: Workshop: A Real Data Project
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
A Jupyter notebook is a file that combines code, results, charts and explanations in cells, so you can experiment and present your analysis in one place. JupyterLab is the current application for opening and editing notebooks. In the course we install it with uv by creating a project, adding jupyterlab, pandas and matplotlib, then running uv run jupyter lab. If you already use Anaconda you can keep using it, or open notebooks in VS Code.
First check which columns have missing values and how many with isna().sum(). Then choose a method that fits the data: if few rows are affected and they do not matter, remove them with dropna; if you need to keep them, fill the gaps with fillna using the mean, median, most common value or, for time series, the previous value. Record the reason for each decision in the notebook. In pandas 3, text columns use NaN for missing values like other column types.
A Python list can hold any mix of types, and calculations need a loop over each item. A NumPy array stores values of one type contiguously in memory, so a whole set can be calculated in one vectorised statement, which is much faster on large data. Arrays also support many dimensions and come with ready-made mathematical and statistical functions. pandas itself is built on the same idea.
It suits data analysts who want to move from Excel to Python, people starting a data science career, students and researchers who handle large data sets and employees who produce regular reports. You should have completed Python Basic or be able to write basic Python, use Excel at a basic level and understand basic statistics such as the mean and standard deviation.
You will be able to work with data confidently in JupyterLab, calculate with NumPy, manage tables with pandas 3, read CSV, Excel and JSON files, clean data including missing values, duplicates, text and dates, combine and summarise data, build charts with Matplotlib and seaborn, and carry out EDA to reach conclusions from real data. Participants who complete the training receive a Certificate of Completion with its own number, which can be verified online.
Bring a laptop with permission to install software. Install uv and let it install Python 3.13 or later, JupyterLab, NumPy, pandas, Matplotlib and seaborn, or open notebooks in VS Code if you prefer. All tools are open source and free. The instructor provides sample data sets and notebooks for every lab.
Articles are published in Thai.
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