Master Advanced Data Science - Data Scientist I AIML Experts TM

4.3 ★★★★ ★

Master Advanced Data Science - Data Scientist I AIML Experts TM

Name: Master Advanced Data Science - Data Scientist - Online Course
Rating: 4.3 (375 reviews)
Author: Dr. Noble Arya

Real-World Case Studies and Practical Applications in Data Science

updated on icon Updated on Jun, 2025

language icon Language - English

person icon Dr. Noble Arya

category icon IT and Software ,Other IT and Software,Python

Lectures -67

Duration -31 hours

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4.3 ★★★★ ★

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Course Description

This full-fledged Data Science Mastery Program equips the learners with the necessary knowledge and the skills needed throughout the entire data science lifecycle. The course covers key concepts, tools, and techniques in data science-from very basic data collection to processing to advanced machine learning models. What learners will find in this program:

Core Data Science Fundamentals:

Data Science Sessions Part 1 & 2- Basis of data science methodologies and approaches.

Data Science vs Traditional Analysis – comparing current data science techniques with traditional statistical methods.

Data Scientist Journey Parts 1 & 2 – Everything you ever wanted to know about what it takes to be a data scientist.

Data Science Process Overview Parts 1 & 2 – Step-by-Step Process in data science projects.

Programming Essentials:

Introduction to Python for Data Science – Python programming basics tailored for data science tasks.

Python Libraries for Data Science – In-depth of major python libraries like Numpy, Pandas, Matplotlib, and Seaborn.

Introduction to R for Data Science – Basic understanding of the R programming language used for data analysis.

Data Structures and Functions in Python & R – Useful ways of handling and manipulating data in both Python and R.

Data Collection & Preprocessing:

Introduction to Data Collection Methods – Familiarization with different data collection methods, including experimental studies.

Data Preprocessing – Cleaning, transforming and preparing of data for analysis (Parts 1 & 2).

Exploratory Data Analysis (EDA) – Identification of outliers, anomalies, and understanding the underlying structure of data.

Data Wrangling – Merging, transforming and cleaning datasets for analysis

Handling Missing Data and Outliers – Techniques to manage incomplete or incorrect data.

Visualization & Analysis:

Data Visualization Techniques – Best practices in selecting the right visualization method in representing data.

Tableau and Data Visualization Advanced data visualization software

Inferential Statistics for Hypothesis Testing & Confidence Intervals Statistical method for hypothesis testing

Machine Learning Mastery

Introduction to Machine Learning Core concepts, types of learning, their applications

Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction) Findings with unlabeled data in unsupervised learning

Supervised Learning (Regression, Classification, Decision Trees) Prediction with labeled data.

Regression & Classification Evaluation Metrics – Techniques to measure the performance of a model, for example, precision, recall, accuracy.

Model Evaluation and Validation Techniques – Techniques that improve model robustness as well as those which are associated with bias-variance tradeoffs.

Advanced Topics in Data Science:

Dimensionality Reduction (t-SNE) – Reducing complexity in high-dimensional datasets

Feature Engineering and Selection – Choosing the best feature set for machine learning models.

SQL for Data Science – Writing SQL queries for data extraction and some advanced querying techniques.

Ethical Challenges in Data Science – Evaluating ethical issues in data collection, curation, and model deployment.

Hands-on Applications & Case Studies:

Data Science in Practice Case Study (Parts 1 & 2) – Filling the gap between theory and practice with real-world projects applying data science.

End-to-End Python & R for Data Science – Do every exercise to keep your Python and R coding skills razor-sharp when solving real problems in data analysis.

Working with Data Science Applications – Applying data science techniques in real-world situations.

By the end of the program, learners will be able to oversee end-to-end data science projects from data collection and cleaning right through to visualization, statistical analysis, and the development of robust machine learning models. The course is structured using multiple hands-on projects, case studies, and a capstone providing learners with a comprehensive foundation in data science and machine learning, launching them into careers as a data scientist or AI/ML professional.

Goals

Data Science Sessions Parts 1 & 2: Learn the approaches and methodologies behind data science
Data Science vs Traditional Analysis: dive into the nuances of modern data science approaches & old statistics methods
Data Scientist Journey Parts 1 & 2: Discover skills, roles, and responsibilities of a data scientist
Data Science Process Overview Parts 1 & 2: Learn the data science process end-to-end.
Learn Python Programming. This is the language with a vast number of applications in data science task implementation and analysis.
Data Science Libraries in Python. Master the key libraries in Python such as Numpy, Pandas, and Matplotlib
Introduction to R for Data Science: Familiarize with R programming related to the implementation of statistical analysis.
Data Structures and Functions in Python & R: Handle data manipulation and handling in Python and R, as easy as slicing cake.
Introduction to Methods of Data Collection: Let's learn the various methods of data collection that includes Experimental methods .
Data Preprocessing (Parts 1 & 2): Refine your raw data. It is in a clean transformation into the usable form for analysis.
Exploratory Data Analysis: Identify outliers and anomalies in order to gain deeper insights into the data.
Data Visualization Techniques: Identify the most effective type of visual technique to be used to deliver insights from the data
Tableau and Data Visualization: Intermediate data visualization using Tableau
Inferential Statistics for Hypothesis Testing: Inferential statistics application in testing hypotheses, determination of the confidence interval
Introduction to Machine Learning: Understanding Machine Learning, its Fundamentals, and Applications.
Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction): Find patterns and clusters on unlabeled data.
Supervised Learning: Develop predictive models by using labelled data
Evaluation Metrics for Regression & Classification: Use many metrics to get an idea about how the machine learning model is doing
Model Evaluation and Validation Techniques : Use techniques of validation and bias variance trade off techniques to make the model robust.
Ethical issues in data science regarding ethical dilemmas arise primarily at the stages of data gathering and then subsequent model deployment.

Prerequisites

Anyone can learn this class it is very simple.

Anyone who wants to learn future skills and become Data Scientist, Sr. Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.

Master Advanced Data Science - Data Scientist I AIML Experts TM

Curriculum

Check out the detailed breakdown of what’s inside the course

Data Science Session

1 Lectures

Data Science Session 36:46 36:46

Data Science Part2

1 Lectures

Data Science Vs Traditional Analysis Session

1 Lectures

Data Scientist

1 Lectures

Data Scientist Part2

1 Lectures

Data Science Process Overview

1 Lectures

Data Science Process Overview

1 Lectures

Introduction to Python for Data Science

1 Lectures

Python Libraries for Data Science

1 Lectures

Introduction to R for Data Science

1 Lectures

R Programmig

1 Lectures

Introduction to Python Programming

1 Lectures

Introduction to Python Programming Part2

1 Lectures

Data Structures and Functions in Python

1 Lectures

Python for AIML- Data Structures and Functions

1 Lectures

Working with Libraries and Handling Files

1 Lectures

Python Introduction to Numpy

1 Lectures

Introduction to R Programming

1 Lectures

Introduction to R Programming Part2

1 Lectures

Data Structures in R

1 Lectures

Data Structures in R Part2

1 Lectures

R Programming

1 Lectures

R Programming Part2

1 Lectures

Introduction to Data Collection Methods

1 Lectures

Introduction to Data Collection Methods Experimental Studies

1 Lectures

Data Preprocessing

1 Lectures

Data Preprocessing

1 Lectures

Introduction to Exploratory Data Analysis EDA

1 Lectures

EDA- Detecting Outliers and Anomalies in Data

1 Lectures

Data Visualization in Data Science

1 Lectures

Choosing the Right Visualization for Data

1 Lectures

Introduction to Statistical Analysis for Data Science

1 Lectures

Inferential Statistics for Hypothesis Testing & Confidence Intervals

1 Lectures

Introduction to Data Science Tools and Software

1 Lectures

Tableau and Data Visualization

1 Lectures

Data Wrangling in Data Science

1 Lectures

Data Wrangling & EDA in Data Science Part2

1 Lectures

Data Integration & Transformation for Data Science

1 Lectures

Handling Missing Data and Outliers

1 Lectures

Introduction to Machine Learning

1 Lectures

ML Unsupervised Learning

1 Lectures

Supervised Learning- Regression

1 Lectures

Evaluation Metrics for Regression Models

1 Lectures

Supervised Learning- Classification in Machine Learning

1 Lectures

Supervised Learning- Decision Trees

1 Lectures

Unsupervised Learning- Clustering

1 Lectures

Unsupervised Learning DBSCAN Clustering

1 Lectures

Unsupervised Learning- Dimensionality Reduction

1 Lectures

Unsupervised Learning- Dimensionality Reduction with t-SNE

1 Lectures

Model Evaluation and Validation Techniques

1 Lectures

Model Evaluation- Bias-Variance Tradeoffs

1 Lectures

Introduction to Python Libraries for Data Science

1 Lectures

Introduction to Python Libraries for Data Science

1 Lectures

Introduction to R Libraries for Data Science

1 Lectures

Introduction to R Libraries for Data Science Statistical Modeling

1 Lectures

Introduction to SQL for Data Science

1 Lectures

SQL Queries for Data Science

1 Lectures

SQL and Advanced Queries

1 Lectures

SQL and Advanced Queries Part 2

1 Lectures

Data Science in Practice- Case Study

1 Lectures

Data Science in Practice- Case Study Data Quality & Model Interpretability

1 Lectures

Introduction to Data Science Ethics

1 Lectures

Ethical Challenges in Data Collection and Curation

1 Lectures

Data Science Project Lifecycle

1 Lectures

Feature Engineering and Selection

1 Lectures

Application- Working with Data Science

1 Lectures

Application Working with Data Science - Data Manipulation Part 2

1 Lectures

Instructor Details

Dr. Noble Arya

Dear Esteemed Lifelong Learner,
Warm greetings.
I am Dr. Noble Arya, a Full-Stack Data Scientist, AI/ML Researcher, and Product Innovator with extensive experience across leading global organizations, including General Electric (GE) and Wipro Technologies.
As the founder of NobleX Infinity Labs®️, a globally recognized platform with prestigious TM® certification, I have had the privilege of mentoring over 100,000 students and 500+ educators across 170 countries. Our educational programs consistently maintain an average course rating of 4.7 out of 5 stars, as rated by more than 100,000 learners.
Academically, I hold a Honorary Doctorate in Artificial Intelligence and Machine Learning, and I am a certified graduate of the Post Graduate Program in Artificial Intelligence and Machine Learning from The University of Texas at Austin, in collaboration with Great Learning. My journey also includes 15+ years of dedicated research and application in AI/ML, Data Science, Deep Learning, and Value Innovation, with a strong grounding in the principles of Pure Consciousness.
I have been honored with over 300 national and international awards in recognition of my contributions to Future Skills, Creativity, Technological Innovation, and Ethical AI. My areas of expertise include Computer Science, Artificial Intelligence, Design Thinking, Super Pure Consciousness, and Value Innovation—acquired through both formal training and self-directed study.
Over the years, I have collaborated with prestigious institutions and organizations such as the Himalayan Institute of Alternatives (Ladakh), Teach for India, Harvard Medical School, University of Texas at Austin, and Toastmasters International. As an entrepreneur, I have successfully scaled two startups from grassroots family initiatives to nationally and internationally recognized enterprises.
My skillset encompasses a broad spectrum, including:-
Real-World Digital and Future Skills
Pure Consciousness and Value Technology
Project Management and Entrepreneurship
Artificial Intelligence and Computer Science
Data Science, Machine Learning, Deep Learning, Design Thinking and Product Development
Having completed over 100 projects in the past decade and a half, I am now committed to democratizing access to transformative education. Through my Udemy courses and YouTube channel, I aim to positively impact millions of learners. My vision for the coming decade is to empower 7 billion people worldwide, both online and offline, with real-life, problem-solving capabilities and ethical applications of AI—grounded in consciousness and compassion.
I invite you to join me on this transformative journey. Follow my work on Udemy and YouTube, and let us build a future where knowledge, technology, and human values uplift all 7+ billion people across the globe.
With deep gratitude to the Creator and to every atom and molecule across all universes, from the beginning to infinity.
With sincere regards,
Dr. Noble Arya
Full-Stack Data Scientist | AI/ML Researcher | Product Innovator

With gratitude till infinity,

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