Description

The Data Science Bootcamp at Coder Faculty is a dynamic 12-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, machine learning, and 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. Teableau 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 12 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 exam, 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.

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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 certificate of course completion will be awarded to students who successfully complete the course.
  • An International certification (Certified Python Programmer) will be awarded to students.
  • An International certitification (Certified Data Analyst) will be awarded to students.
  • A certificate issued from Harvard University is also available for students who opt to take the course with higher course completion criteria.

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

12 weeks
Getting started: Data Science Fundamentals
Session 1 • 3 hours
Course Overview
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
Installation of tools
Virtual Envionments
Jupyter Notebooks
Tableau: Data Preparation, Analysis and Visualisation
Session 2 • 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 5 • 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
An introduction to Python
Session4 • 3 hours
Input/Ouput
Variables
Operations
Decisions
Loops
Lists
Sets
Tuples
Dictionaries
Python Advanced Concepts
Session 5 • 3 hours
Exception Handling
Files
String Manipulation
Functions
Testing
Libraries
Object Oriented Programming
Session 6 • 3 hours
Object Oriented Programming Concepts
Classes
Inheritance
Polymorphism
Data analysis with Python: Numpy, Pandas and preprocessing
Session 7 • 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
Splitting data into training and testing sets
Artificial Intelligence and Machine Learning
Session 8 • 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 and evaluating a predictive model
Session 9 • 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
Session 10 • 3 hours
Grid Search and Random Search
Advanced Hyperparameter Optimisation Techniques
Regularization and Overfitting
Model Selection and Best Practices
Analytics Tools and Libraries
Session 11 • 3 hours
Grid Search and Random Search
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
Session 12 • 3 hours
Importance of Exploratory Data Analysis (EDA)
Interactive EDA Techniques
Using Jupyter Notebooks for EDA
Interactive Visualization with Plotly
Advanced case Study in EDA
Natural Language Processing
Session 13 • 3 hours
Introduction to NLP
Text Preprocessing
Text Classification
Sentiment Analysis
Named Entity Recognition
Topic Modelling
Word Embeddings
Sequence to Sequence Models
Case Studies and Applications
Project Overview and Career Advice
Session 14 • 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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