Advanced Data Science Professional (ADSP) using Python x ChatGPT

Data Scientist has become one of the most in-demand job position in every industry. The demand for professionals with Data Science expertise are booming and is expected to grow exponentially year-on-year.

Python has become programming language of choice in the areas of Data Science. Recent studies have shown that majority data scientists are utilizing Python daily operations, making it the number one tool for data professionals. As Python becomes the mainstream language of choice, it is essential that Data Scientist acquire the necessary knowledge and skills to stay competitive.

Applied AI-driven Data Science Professional (ADSP) using Python x Gen AI Tools

$680.00 - $780.00

Mode of Delivery

Date

Time / Duration

Course Material

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SKU: N/A Category: DATA Tags: ADSP, artificial intelligence, chatgpt, data mining, data pre-processing, data science, data strategies, machine learning, python programming

Overview
Course Outline

Gain Hands-on Expertise in Python, Generative AI tools, and AutoML for Real-World Applications
Global Recognition
Networking Opportunities
Career Advancement
Job Opportunities in the Digital Economy
Increase Earning Potential
Learn to Streamline Workflows and Automate Complex Data Science Tasks

Pre-requisite

No pre-requisite. Suitable for everyone with and without prior technology experience.

Who Should Attend

Business Leaders, CIOs, CISOs, Data Scientists, AI Enthusiasts, System Analyst, Technologist, System Engineer, IT Professionals, Consultants, Project Managers, Developers, Business Analysts, Data Analysts, Business Process Outsourcing (BPO) Professionals, Anyone seeking to acquire advanced knowledge on AI-driven Data Science

Course Objective

Acquire advanced knowledge and skills on integrating AI techniques into data science workflows using Python-based tools and libraries for automation, machine learning, and data visualization.

Learn how to utilize Generative AI for data augmentation, insights generation, and advanced NLP applications to thrive in the evolving AI and data science landscape.

Course Overview

AI-driven data science professionals are at the forefront of innovation, driving insights from complex data. As AI evolves, demand for experts skilled in automation, predictive modeling, and ethical AI will surge, shaping industries and creating transformative solutions for a data-powered future.

Applied AI-driven Data Science Professional (ADSP) blends Python and Generative AI tools, providing participants with hands-on experience in automating workflows, advanced NLP, AI-powered visualization, and modeling techniques.

Duration: 16 Hours

Certification: Participants will receive a Certificate of Competency upon completing the course and passing the examination

Module 1 - Introduction to AI-Driven Data Science

What is AI-driven Data Science
Evolution of Data Science with AI
Artificial Intelligence vs. Machine Learning vs. Deep Learning
Benefits of AI-Driven Data Science
Automated Data Handling using AI Tools
Overview of Supervised, Unsupervised, and Reinforcement Learning
Introduction to Models e.g. GPT and their applications in Data Science

Module 2 - Python for AI-Driven Data Science

Overview of Python for Data Science and AI
Key Python Libraries for AI-driven Workflows
Working with Structured and Unstructured Data e.g. pandas, NumPy
Using Exploratory Data Analysis (EDA) to Uncover Patterns and Trends
Exploratory Data Analysis (EDA) using matplotlib, seaborn
Overview of supervised, unsupervised, and reinforcement learning
Introduction to models like GPT and their applications in data science

Module 3 - Feature Engineering with Python

Introduction to Feature Engineering
Handling Missing Data
Scaling, Normalization, and Encoding of Categorical Variables (one-hot, label encoding)
Creating New Features From Existing Data
Model-based Feature Selection (e.g., Decision Tree Importance, RFE)
Automated Feature Engineering using FeatureTools
Extracting Time-based Features and Rolling Statistics
Techniques and Application in Reducing Redundant Features
Text Feature Engineering
Evaluating Features and Integrating Feature Engineering into Workflow

Module 4 - Intro to Generative AI for Data Science

What is Generative AI?
Generative AI vs. Applied AI vs. Discriminative Models
Types of generative models e.g. Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformer-based models (e.g., GPT, DALL·E)
Applications of Generative AI in Data Science
Overview of Frameworks and Libraries
Data Augmentation with Generative AI
Generative AI for Insights Generation

Module 5 - Advanced NLP with Generative AI

Evolution of NLP with Generative AI models
Tokenization, Embeddings, and Contextual Representations
Transformer-based Architectures e.g. GPT (Generative Pre-trained Transformer), BERT vs. GPT: Key Differences and Applications
Deep Dive into Text Generation and Summarization
Techniques for Abstractive and Extractive Summarization
Advanced Techniques for Understanding Sentiment and Emotions in Text
Fine-tuning Pre-Trained Models for Specific Tasks

Module 6 - Deep Dive into AI-Powered Visualization

Introduction to AI-Powered Visualization
Visual Perception vs. Effective Storytelling
AI-Driven Tools for Visualization
Generating Visualizations Directly from Data insights using Autoviz and DataPrep
Customizing Visualizations with AI using matplotlib and seaborn
Creating Live Visualizations for Streaming Data using Python
Visualizing Predictions, Forecasts, and Trends using AI

This course involves extensive practical/hands-on exercises, rigorous usage of real-time case studies, role-playing and group discussion

Examination

Upon completion of the course, participants are required to attempt an examination. This exam tests a candidate’s knowledge and skills related to Digital Transformation, based on the syllabus covered.

Participants are expected to score a minimum of 70% to pass the examination

Course Outline

Module 1 Introduction to Data Science

Data Science is a discipline on data, its application, and how it can be utilized to help companies and professionals to make better decisions. In this module, participants will be introduced to the core components of data science. Participants will also learn about how to craft a data science strategy that is both efficient and reliable.

Topics Covered

What is Data Science
Data Science Vs. Analytics
What is Data warehouse
Online Analytical Processing (OLAP)
MIS Reporting
Data Science and its Industry Relevance
Problems and Objectives in Different Industries
How to Harness the power of Data Science?
ELT vs ETL
ChatGPT and Its Role in Data Science

Module 2 Deep Dive into Python Programming for Data Science

Python Programming language is one of the most accessible and flexible programming languages available. The syntax is simplified and can be easily written. With its extensive libraries, it is no wonder that data professionals are using Python as their preferred language. In this module, participants will learn the fundamentals of Python Programming and some of its popular libraries for Data Science.

Topics Covered

Python Editors & IDE
Custom Environment Settings
Basic Rules in Python
Most Common Packages / Libraries in Python (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations - Mathematical - string - date
Reading and writing data
Simple plotting/Control flow/Debugging/Code profiling

Module 3 Importing / Exporting Data with Python

Reading / writing data one of the first steps to kickstarting any Data Science project. Participants will have the chance to experience first-hand how to read / write data from various sources.

Topics Covered

Importing Data into from Various sources
Database Input (Connecting to database)
Viewing Data objects - sub setting, methods
Exporting Data to various formats

Module 4 Data Cleansing with Python

The accuracy and quality of any analysis depend on how well the data is being cleaned. Data cleaning can be used to detect and correct errors or anomalies in the data. In this module, participants will learn the art of data cleaning to enhance the quality of data.

Topics Covered

Cleaning of Data with Python
Steps to Data Manipulation
Python Tools for Data manipulation
User Defined Functions in Python
Stripping out extraneous information
Normalization of Data and Data Formatting
Important Python Packages e.g.Pandas, Numpy

Module 5 Data Visualization with Python

Data visualization helps people see, interact, and understand data better. The right visualization can help align the understanding of various stakeholders. Participants will have the opportunity to learn how to use Python Programming to perform Data Visualization.

Topics Covered

Exploratory Data Analysis
Descriptive Statistics, Frequency Tables and Summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)

Module 6 Statistics Fundamentals

Statistics are being used by Data Scientists to gather, review, analyze, and draw conclusions from data. Statistics are the core of machine learning algorithms, capturing, and translating data patterns into actionable evidence.

Topics Covered

Basic Statistics - Measures of Central Tendencies and Variance
Building blocks (Probability Distributions, Normal distribution, Central Limit Theorem)
Inferential Statistics (Sampling, Concept of Hypothesis Testing)
Statistical Methods: Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi-square
Statistical Methods: ANOVA
Statistical Methods: Correlation and Chi-square

Module 7 Introduction to Machine Learning using ChatGPT

Machine learning is a field of study that gives computers the ability to learn without being programmed extensively. It applies algorithms to process the data and get trained for delivering future predictions. In this module, participants will be introduced to the key concepts of Machine Learning and how it can be easily applied using Python Programming.

Topics Covered

Statistical Learning vs Machine Learning
Iteration and Evaluation
Supervised Learning vs Unsupervised Learning
Predictive Modelling - Data Pre-processing, Sampling, Model Building, Validation
Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
Cross ValidationTrain & Test, Bootstrapping, K-Fold validation etc

Module 8 Predictive Analytics with Python x ChatGPT

A statistical technique derived from data mining, machine learning, and predictive modeling by using data from past events to predict the future. Participants will experience first-hand how predictive analytics can be deployed using Python Programming.

Topics Covered

Introduction to Predictive Modelling
Types of Business Problems
Mapping of Techniques
Using ChatGPT to Deploy Machine Learning Model
Linear Regression
Logistic Regression
Segmentation - Cluster Analysis (K-Means / DBSCAN)
Decision Trees (CHAID/CART/CD 5.0)
Time Series Forecasting

Module 9 Understanding A/B Testing Concepts

A/B testing is a framework that lets you set up your experiment to compare the performance of two versions of the same thing. It helps teams validate key questions, establishes causality, and helps data-driven teams looking to optimize their products.

Topics Covered

Introduction to A/B Testing
Measuring Conversion for A/B Testing/li>
T-Test and P-Value
Measuring T-Statistics and P-Values using Python
A/B Test Gotchas
Novelty Effects, Seasonal Effects, and Selection of Bias
Data Pollution

Advanced Data Science Professional (ADSP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions

What You Can Expect

Technology-Based Training

Open-Source Tools

80% Hands-On Exercises / 20% Theory

Designed by Industry Leaders and Subject-Matter Experts

Progressive Learning

Highly interactive, supportive and encouraging

Be recognized as a CASUGOL Certified Professional

What Learners Say

Excellent

CASUGOL4.9

Camille Gaudi

Very relevant and collaborative training. The instructor is very attentive to the participant's needs and answers all their queries.Casugol offers not only workshops,but a chance to get to know other people in the industry and allows you to build a network and belong to a digital community.It is a pleasure to be part of this community. More power to Casugol Team!

Mark Joseph Zabala

The SDTL course was concise, well‑organized, and highly practical. The templates and frameworks were straightforward yet impressively precise, making the concepts easy to understand and apply in real-world situations. It was also very helpful in strengthening a leader’s perspective on Digital Transformation. Overall, the program delivered strong value and offered an excellent balance of clarity, depth, and practicality.

koen de wit

I wanted to expand my horizon and open myself to a new way of thinking about digital transformation. It certainly more than overdelivered

David Mould

Peesa Choomsawasdi

Enjoyed the class, insightful and pragmatic.

Ahmad Tariq Ahmad Ziyad

Thanks Dwayne for teaching us the CASUGOL Digital Transformation Framework. After the class, it makes me can't wait to start to many potential area of improvements in my company internally and externally on our Digital Transformation journey

Kuchai

Very exciting course to be joined by others in AI era.

Joel Sebastian

Marshall Tommie Lajawai

Great training experience!

Auriel Anthony

Thank you CASUGOL for coming all the way to Sabah, East Malaysia at Sabah Credit Corporation. Had amazing digital transformation training with CASUGOL!

cordon scc

great learning experience and learn more about digital transformation. Also great speaker

Jeamron Arat

Attended CASUGOL's CDTP training and learned a lot about the Digital Transformation framework and core principles which can be applied on the job. Plenty of real-world examples and active discussion during the trainings.

Kira Chan

toni gasang

Innocelzever Abdon

I've enjoyed the whole 2-day training as Dwayne kept it light and fun while being an educator at the same time, it made my experience memorable and informative

Japhet Moraleja

Ian Curt Sarmiento

Great way of sharing expertise and knowledge in small steps but big meaning change.

Patricia Caldito-Lim

Casugol provides a better understanding on how to leverage well on Artificial Intelligence. I've a learned a lot on a 1-day session. Thank you!

Kavilashini Alagenthran

Tiwi Harefa

Raihan Muhammad Iqbal

vira agatha

It was really fun to follow the casugol training, the lessons were easy to understand

Hanna Yunita

Nurianda Wulandari

Terimakasih untuk pembelajarannya selama ini, semoga ilmu yang di berikan bermanfaat bagi semua org

Liza Sinaga

ryanlau terate22

Anita Romauli Simamora

During this training, I gained a lot of valuable experience, both in terms of knowledge, skills, and work attitudes. The material presented is very relevant to the needs of the world of work, and is presented in an interactive and easy-to-understand manner.