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
Price range: $990.00 through $1,100.00
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.
Course Benefits
- 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
Course Outline
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
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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.
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Topics Covered
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Module 2 Deep Dive into Python Programming for Data Science
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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.
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Topics Covered
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Module 3 Importing / Exporting Data with Python
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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.
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Topics Covered
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Module 4 Data Cleansing with Python
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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.
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Topics Covered
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Module 5 Data Visualization with Python
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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.
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Topics Covered
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Module 6 Statistics Fundamentals
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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.
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Topics Covered
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Module 7 Introduction to Machine Learning using ChatGPT
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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.
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Topics Covered
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Module 8 Predictive Analytics with Python x ChatGPT
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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.
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Topics Covered
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Module 9 Understanding A/B Testing Concepts
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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.
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Topics Covered
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Advanced Data Science Professional (ADSP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions
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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 |
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