Introduction

Jupyter Notebooks for Data Science Training Course is designed to transform beginners and intermediate learners into proficient data scientists capable of analyzing, visualizing, and modeling data using the most popular open-source tools. Participants will gain hands-on experience in Python programming, data wrangling, machine learning, and interactive data visualization, all within the versatile Jupyter Notebook environment. With real-world case studies and practical exercises, learners will master the skills needed to tackle complex business intelligence, predictive analytics, and AI-driven projects efficiently.

Our training program emphasizes a practical, project-oriented methodology, ensuring that every concept is reinforced through hands-on exercises and industry-relevant scenarios. By leveraging Jupyter Notebooks, participants can seamlessly combine code, visualizations, and narratives to produce reproducible and shareable analyses. This course is perfect for aspiring data scientists, analysts, developers, and business professionals who want to accelerate their career in data science, enhance problem-solving skills, and drive insights from complex datasets.

Programme Curriculum

Jupyter Notebooks for Data Science Training Course

Introduction

Course Duration

5 days

Course Objectives

Master Python programming for data science applications.
Gain expertise in Jupyter Notebook workflow for interactive coding.
Perform data wrangling and cleaning using Pandas and NumPy.
Develop skills in data visualization using Matplotlib, Seaborn, and Plotly.
Implement exploratory data analysis (EDA) on real-world datasets.
Apply statistical analysis to derive meaningful insights.
Build machine learning models using scikit-learn.
Understand supervised and unsupervised learning techniques.
Learn feature engineering and model optimization strategies.
Work with time series and predictive analytics in Jupyter.
Create interactive dashboards and reports within notebooks.
Solve real-life business problems through data-driven projects.
Enhance reproducibility and collaboration in data science projects.

Target Audience

Aspiring data scientists and analysts
Python developers looking to transition into data science
Business analysts seeking data visualization skills
Students in computer science or statistics
Machine learning enthusiasts
Professionals in finance, marketing, and operations
Researchers handling large datasets
IT professionals aiming to implement data-driven solutions

Course Modules

Module 1: Introduction to Jupyter Notebook & Python for Data Science

Understanding the Jupyter Notebook interface
Writing and executing Python scripts interactively
Overview of Python libraries for data science
Installing and configuring Anaconda environment
Case Study: Setting up a Jupyter Notebook project for exploratory analysis

Module 2: Data Wrangling with Pandas & NumPy

Handling structured and unstructured data
Cleaning and transforming datasets using Pandas
Efficient array operations with NumPy
Combining, merging, and reshaping datasets
Case Study: Data cleaning and preparation for sales analysis

Module 3: Data Visualization Techniques

Creating interactive plots with Matplotlib and Seaborn
Customizing graphs and dashboards
Using Plotly for interactive visualizations
Visual storytelling for business insights
Case Study: Visualizing customer behavior trends

Module 4: Exploratory Data Analysis (EDA)

Identifying patterns, correlations, and outliers
Generating summary statistics and insights
Feature selection and dimensionality reduction
Automating EDA using Python libraries
Case Study: EDA on marketing campaign datasets

Module 5: Statistical Analysis for Data Science

Descriptive and inferential statistics
Hypothesis testing and confidence intervals
Regression and correlation analysis
Probability distributions for predictive modeling
Case Study: Analyzing survey data for actionable insights

Module 6: Machine Learning Fundamentals

Introduction to supervised and unsupervised learning
Building models using scikit-learn
Model evaluation and performance metrics
Implementing classification, regression, and clustering
Case Study: Predicting customer churn with logistic regression

Module 7: Advanced Machine Learning & Model Optimization

Hyperparameter tuning and cross-validation
Feature engineering and selection techniques
Handling imbalanced datasets
Deploying models in Jupyter Notebooks
Case Study: Predictive analytics for sales forecasting

Module 8: Data Science Projects & Interactive Reporting

Creating interactive dashboards in notebooks
Automating data pipelines and workflows
Documenting analyses for reproducibility
Presenting results to stakeholders with storytelling techniques
Case Study: End-to-end project: Customer segmentation and insights

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Interactive analysis Data visualization Reproducible research Python workflows

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000