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Data Science Bootcamp - Analysis, Visualisation & Machine Learning

Become a certified Data Analyst

4.5
Certified Course
27 September 2025
N.Rampersand
Medium
On-campus or Online
Starts on
27 September 2025
Schedule
Saturdays @12.00 (GMT+4)
Course duration
16 weeks (3-hour sessions)
Requirements
None
Language
English
Certificate
Globally recognised certifications
Instructor
N.Rampersand
Contact Us
WhatsApp | Messenger
Call us
+230 59810950
Registration Deadline
20 September 2025
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Course
Rs 25000 | USD 550
Rs 50000
Register for the Course
After registration, contact us to complete the payment and receive your enrolment email.

Description

The Data Science Bootcamp at Coder Faculty is a dynamic 16-week programme designed for both students and professionals aspiring to pivot into the field of data science. This comprehensive course provides an in-depth exploration of data science principles, focusing on data analytics with Tableau, Python programming, Artificial Intelligence and machine learning using practical applications in data analysis.

The program starts with an extensive Python programming module, establishing a solid foundation in data science applications. It explores Python syntax, control structures and essential libraries like NumPy and pandas for data manipulation and analysis.

As the course advances, the curriculum includes a new focus on Tableau where students engage in advanced data analysis and visualisation, learning to manage large datasets and derive significant insights. This enriches the course with practical skills in data visualisation and business intelligence. Tableau sessions cover data preparation, transformation, modeling, analysis and visualisations to create interactive dashboards and reports. These sessions provide hands-on experience in using Tableau for real-world data analysis and business decision-making.

A distinctive feature of this bootcamp is its focus on real-world applicability. Throughout the 16 weeks, students work on data analytics practicals, simulating real-world challenges and solutions. These mini-projects are integral to developing a professional portfolio that demonstrates their skills and readiness for a data science career.

The program ends with a capstone project, certification exams and a career guidance session. These elements ensure that graduates not only gain comprehensive theoretical knowledge but also practical skills and industry insights, preparing them for a successful transition into data science roles.

Register for the course

What you'll learn

Become a Certified Data Scientist

Master the data engineering workflow

Explore, prepare, visualise and interpret large datasets

Perform statistical analysis for data-driven decision making

Gain real-world experience with data analytics projects

Master Tableau for Data Visualisation

Create interactive dashboards and reports with Tableau

Learn scientific computing with Python

Get a solid grasp of machine learning and its applications

Learn how to train predictive machine learning models

Why should you take this course?

This bootcamp offers an intensive, hands-on learning experience, ideal for students or professionals seeking a career into data science. With its blend of rigorous academic content, practical projects and career-oriented training, the Data Science Bootcamp at Coder Faculty stands as a gateway to the exciting and rapidly evolving world of data science. At the end of the bootcamp, students will possess a highly sought skillset that will open up new career opportunities.

Certificates

  • A certification of course completion will be awarded to all students who successfully complete the course requirements.
  • Students who opt for the international track will also receive 3 globally recognised certifications at no additional cost.
  • An additional certificate from Harvard University is available to students who complete additional coursework and pass the required exams.

Requirements

  • There are no requirements for this course.
  • Anyone can take this course as it is designed for both beginners and professionals.
  • Students possessing a basic understanding of any programming language will find it easier to follow the course. However, it is not a requirement.


Register for this course and become a certified Python Developer today!

Course Content

16 weeks
Induction and Development Environment Setup
Session 1 • 3 hours
Course introduction and overview
Installing Visual Studio Code and recommended extensions (e.g., Jupyter)
Installing Python and verifying environment setup
Installing and configuring Git
Installing Tableau Public
Overview of key tools: VSCode, Git, Tableau, CLI
Command Line Basics: file system navigation and essential commands
Working with GitHub: creating repositories, cloning, pushing, pulling
Version control using Git: init, add, commit, status, branch, merge
Best practices for project structure and code management
Getting started: Data Science Fundamentals
Session 2 • 3 hours
Data science concepts
Types of data
Datasets and data sources
Data science workflow
Analysis methods
Challenges in data analysis
Role of data analysis in decision making
Data engineering processes
Exploring Datasets
Data types and structures
Dimensions and measures
Discrete vs continuous data
Artificial intelligence
Machine learning process
Virtual Envionments
Tableau: Data Preparation, Analysis and Visualisation
Session 3 • 3 hours
Introduction to Tableau
Navigating the Tableau Interface
Connecting data sources
Data preparation with Tableau
Data inspection and cleaning
Handling missing values
Data transformation
Splitting and merging columns
Data aggregation and filtering
Creating hierarchies and groups
Creating basic visualisations
Exploratory data analysis
Exploring advanced visualisation techniques
Advanced analytical tools in Tableau
Demonstrating data relationships
Case study: Analysing sales data
Tableau: Interactive Dashboards and Advanced Techniques
Session 4 • 3 hours
Creating interactive dashboards
Sizing and layout
Views
Exporting your dashboard
Advanced interactivity
Custom extensions
Performance optimisation
Accessibility and inclusivity
Sharing and collaboration
Developing stories
Case study: Building and publishing a dashboard
Conclusion
Mini-Project
Introduction to Python Programming
Week 5 • 3 hours
Overview of Python
Setting up and running Python scripts
Comments for documentation
Input and Output operations
Understanding variables and data types
Arithmetic and logical operations
Decision making using conditional statements (if, elif, else)
Iterative programming using loops (for, while)
String manipulation and formatting
Basic debugging techniques
Writing clean and readable Python code
Solving a mini project using Python
Python Data Structures and Advanced Operations
Week 6 • 3 hours
An overview of Python Data Structures
Working with Lists: indexing, slicing, methods
Understanding Sets and their applications in data science
Manipulating Tuples and when to use them
Working with Dictionaries for key-value data handling
Nested structures and list comprehensions
Python Advanced Concepts
Session 7 • 3 hours
Exception Handling
Files
String Manipulation
Functions
Testing
Libraries
Object Oriented Programming
Session 8 • 3 hours
Object Oriented Programming Concepts
Classes
Inheritance
Polymorphism
Advanced Data Analytics - Data manipulation and preprocessing
Session 9 • 3 hours
Introduction to Data Science with Python
Working with Numpy Arrays
Data Manipulation with Pandas: Series and DataFrames
Data Cleaning and Transformation
Aggregation and Grouping
Preprocessing data
Handling Missing Data and Data Types
Encoding data and labels
Scaling and Binning data
Data Visualisation and Time Series Analysis
Session 10 • 3 hours
Importance of Data Visualisation in Analytics
Creating plots with Matplotlib (line, bar, scatter, histogram, etc)
Enhancing visualisations with Seaborn
Extracting insights from data
Plot styling and aesthetics
Visualising distributions and relationships
Working with Time Series Data
Parsing dates and setting datetime indices
Plotting and interpreting time series trends
Handling missing values in time series
Artificial Intelligence and Machine Learning
Session 11 • 3 hours
Overview of AI and ML
Supervised, Unsupervised and Reinforcement Learning
Classification and Regression
Linear Regression
Logistics Regression
Naive Bayes
K-Nearest Neighbours
Decision Trees and Random Forests
Support Vector Machines
K Means Clustering
Case Studies and Applications
ML: Training, evaluating and tuning a predictive model
Session 12 • 3 hours
Data preprocessing and feature engineering
Splitting data: Train, Test, and Validation Sets
Model training techniques
Training your model with Scikit-learn
Model Evaluation and performance metrics
Cross Validation and Hyperparameter Tuning
Tuning and Optimising Machine Learning Models: Grid Search
Advanced Hyperparameter Optimisation Techniques
Regularization and Overfitting
Model Selection and Best Practices
Learning curves and loss
Feature Importance and Selection
Model Interpretability and Explainability
Analytics Tools and Libraries
Session 13 • 3 hours
Overview of Python Ecosystem for Analytics
Scikit-learn for Machine Learning
Model Selection and Best Practices
TensorFlow and Keras for Deep Learning
Other Essential Libraries and Tools
Natural language processing libraries
Case Studies and Applications
Interactive EDA with Python – Guest Lecture and Real-World Case Study
Session 14 • 3 hours
Understanding the role of Exploratory Data Analysis (EDA) in real-world projects
Live case study demonstration by a professional Data Scientist
Interactive EDA with defined objectives
Visual storytelling with plots and graphs
Key techniques: correlation analysis, outlier detection, feature exploration
Q&A session with the guest lecturer
Natural Language Processing and Large Language Models
Session 15 • 3 hours
Overview of Natural Language Processing (NLP) and its applications
Text preprocessing techniques: tokenisation, stopwords, stemming, lemmatisation
Classical NLP tasks: Text classification, sentiment analysis, named entity recognition
Topic modelling and word embeddings
Introduction to Large Language Models (LLMs)
Understanding transformers and the architecture behind LLMs
Retrieval-Augmented Generation (RAG) – concept and use cases
Fine-tuning vs prompt engineering in LLMs
Using off-the-shelf LLM APIs
Case studies and applications of NLP and LLMs in real-world projects
Project Overview and Career Advice
Session 16 • 3 hours
Project Introduction and Guidelines
Data Collection and Preparation
Model Development and Evaluation
Results Presentation and Reporting
Project Submission and Review
Career Advice as a Data Scientist

Instructor

N.Rampersand
N.Rampersand

Instructor

Nirmal Rampersand is an accomplished Lead Software Engineer with extensive experience in training and leading software development teams with agile frameworks. With a diverse skill set spanning multiple programming languages and domains, he is proficient in Python, Node.js, Java, PHP, SQL, NoSQL, C++, Machine Learning (ML), and Natural Language Processing (NLP). He is currently conducting doctoral research in the field of Computational Intelligence and Optimisation.

With several years of dedicated teaching experience, Nirmal has consistently demonstrated his passion for nurturing the next generation of software developers and data scientists. He has successfully trained both students and professionals in a wide array of domains, including Python programming, Full Stack Web Development, and Data Science & Machine Learning. As a Udemy instructor, he has harnessed his expertise to develop comprehensive curriculums and on-demand courses. This has allowed him to reach a global audience of learners, empowering them with the knowledge and skills necessary to succeed in today's fast-paced tech industry.

He also excelled as a Freelance Software Engineer, demonstrating mastery in developing robust REST APIs using Node.js and Python. His innovative flair was evident in creating intelligent systems for NLP solutions and complex chatbots. Furthermore, he is an expert in data mining and analytics. His proficiency extends to application development, covering the entire lifecycle, and he's crafted user-friendly, cross-platform mobile applications. These hands-on experiences uniquely qualify him to impart real-world knowledge to students and professionals alike.

These rich experiences in software engineering, development, and AI have uniquely positioned him as an educator who not only imparts knowledge but also shares real-world, hands-on expertise with his students.

Student Reviews

  • Aswad Banee - Software Engineer at Agileum

    Rating: 5/5

    It was a fantastic training, and you got fantastic teaching skills. This made it possible for me to quickly pick up python. Every single one of your lectures was excellent. I sincerely appreciate it.

  • Aditya Potharala - Software Engineering Student

    Rating: 5/5

    The course covers a lot of material and also the assignments were manageable. It was interactive, informative and well planned. It has been a truly rewarding learning experience and the tutor was very understanding and supportive throughout the course. Highly recommended !

  • Altaaf Waresh Allee - Junior Developer

    Rating: 5/5

    Courses are well structured, lectures are well explained and practical sessions are made easy for anyone. Mr Nirmal a really great lecturer, one who shows passion for his job and cares for his students. Really enjoyed the efforts made for students.



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