AI-driven Data Analytics Essentials (DAE) Using Python and GEN AI Tools
Every industry and vertical is expected to increase their investment in Data technologies and solutions, fueling the growth of data-driven economy.
With an increased interest and investment in AI and Gen AI, new tools for collecting and analyzing data and new enterprise roles and responsibilities has emerge presenting IT professionals and individuals with tremendous career opportunities.
This can never be a better time to acquire the knowledge and skills required in Data Analytics and Data Science.
- Duration: 16 Hours
- Certification: Participants will receive a Certificate of Competency upon completing the course and passing the examination
Select your preferred batch for more information
For corporate booking or course customization, Email Us or Speak to our Education Officer
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Pre-requisite
No pre-requisite. Suitable for everyone with and without prior technology experience.
Who Should Attend
Data Analysts, Business Analysts, Data Scientists, IT Professionals, Researchers, Consultants, Students and Aspiring Data Professionals, Key Decision Makers, Business Leaders, Business Process Outsourcing (BPO) Professionals, Anyone interested in acquiring knowledge and skills in Data Analytics
Key Features
- 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
Course Objective
- Acquire the essential knowledge and technical skills on how data analytics can be used by organizations to enhance decision making and uncover hidden data insights.
- Learn the core components of Data Analytics, Data Preprocessing and Cleaning, Data Mining, Data Warehousing and Visualization using Python Programming and GEN AI Tools.
Examination
Participants are required to attempt an examination upon completion of course. 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
Why Data Analytics Essential (DAE)?
- Develop Deep Knowledge on Data Analytics Concepts, Techniques, and Tools
- Global Recognition
- Networking Opportunities
- Career Advancement
- Job Opportunities in the Digital Economy
- Increase Earning Potential
- Empower Decision-Making to Drive Business Growth
Course Outline
Data Analytics Essentials (DAE) involves immersive hands-on experiences, thorough utilization of real-world case studies, practical exercises, and collaborative group discussions.
Be Recognized as a CASUGOL Certified Professional
Add Value to your already prolific portfolio a Certificate of Competency from CASUGOL.
Awarded upon completing and meeting all requirements of the course.
All certificates issued by CASUGOL are to individuals who have successfully completed CASUGOL Certification Programs / Executive Workshops and have fulfilled all requirements by demonstrating proficiency in applying the knowledge and skills acquired.
Stories from Our Learners
Find out what it's like to take a CASUGOL Course from current and past learners.
Advance Your Business | Career to the Next Level
Face-to-Face and Online Zoom options available. Click on Clear to select your preferred batch..
For corporate booking or course customization, Email Us or Speak to our Education Officer
Click Here for More Info on Course
Course Features
- Lectures 51
- Quiz 0
- Duration 16 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Self
- 8 Sections
- 51 Lessons
- 16 Hours
- MODULE 1 - Introduction to AI-driven Data AnalyticsAcquire essential knowledge on the key components of Data Analytics and techniques like statistical analysis and visualization. Learn about what it takes to empower professionals across industries to extract valuable information and make informed decisions based on data-driven evidence.6
- 1.0AI-driven Data Analytics Overview
- 1.1Data Analytics Built on AI-driven Methodologies
- 1.2Importance and Advantages of AI-driven Data Analytics
- 1.3Developing AI-driven Data Analytics Strategies, AI Tools, and Methodologies
- 1.4Data Analytics Maturity Model and the Use of AI within Analytics Processes
- 1.5Understanding Descriptive, Predictive and Prescriptive Analytics
- MODULE 2 - Different Types of Analytics and ApplicationExplore their diverse applications, from business intelligence to healthcare, illuminating paths for data-driven success.7
- 2.0Different Application of AI-driven Analytics Method
- 2.1Concepts of Text Analytics (Natural Language Processing) and Web Analytics with Machine Learning Algorithms
- 2.2Data / Information Architecture for AI-driven Systems
- 2.3AI-Enhanced Extract Transform Load (ETL) vs. Extract Load Transform (ELT) Architecture
- 2.4Data Warehouse for AI-driven Data Analytics
- 2.5Enhancing Business intelligence and Data Analytics with AI Tools
- 2.6Pragmatic Applications of AI-driven Application
- MODULE 3 - Deep Dive into Python Programming for AI-driven Data AnalyticsUncover the versatility of Python, from web development to AI, making it an essential language for beginners and seasoned developers alike.7
- 3.0Overview of Python’s Role in AI and Data Analytics
- 3.1Key Python Libraries for AI-driven Analytics: NumPy, Pandas, Scikit-learn, TensorFlow, Keras, and PyTorch
- 3.2Setting up Python Environment for AI and Data Analytics
- 3.3Data Cleaning and Preprocessing with Pandas and NumPy
- 3.4Scaling, Normalizing, and Transforming Data for Machine Learning Models
- 3.5Exploratory Data Analysis (EDA) with AI-powered Insights
- 3.6Data Pipeline Automation with AI and Python
- MODULE 4 - Key Modules / Packages in Python for AI-driven Data AnalyticsUnlock the power of Python in Data Analytics through key modules like Pandas for data manipulation, NumPy for numerical operations, Matplotlib for visualization, and Scikit-Learn for machine learning.6
- 4.0Overview of Essential Python Libraries and Packages for AI and Data Analytics
- 4.1Installing and Managing Python Packages using pip and conda
- 4.2Data Manipulation and Transformation with Pandas
- 4.3Numerical Computations with NumPy and SciPy
- 4.4Core Machine Learning Models and Algorithms Available in Scikit-learn
- 4.5Data Visualization with Matplotlib, Seaborn, and Plotly
- MODULE 5 - Data Mining Processes for Data AnalyticsFrom preprocessing to pattern discovery, these processes employ algorithms and statistical methods, unveiling actionable insights crucial for informed decision-making in diverse industries.7
- 5.0Overview of AI in Data Mining and its Role in Data Analytics
- 5.1Traditional Data Mining Techniques vs. AI-driven Approaches
- 5.2AI-powered Tools for Data Cleaning and Anomaly Detection
- 5.3Handling Missing Data, Outliers, and Noisy Data using AI Algorithms
- 5.4Overview of AI-based Tools for Automating Data Mining
- 5.5Enhancing Knowledge Discovery in Databases (KDD) with AI: Automating Knowledge Extraction, Pattern Recognition, and Trend Analysis
- 5.6Future Trends in AI-Driven Data Mining
- MODULE 6 - AI-Powered Pattern Discovery and Predictive Analytics in Data Mining TechniquesAcquire knowledge on how to employ algorithms such as clustering, association, and classification, these techniques unveil hidden knowledge, facilitating informed decision-making in diverse fields from business to research.7
- 6.0AI-driven Classification Techniques in Data Mining (Decision Trees, Random Forests, SVM, Neural Networks)
- 6.1AI-driven Clustering Methods for Data Mining (K-means, DBSCAN, Hierarchical Clustering)
- 6.2Association Rule Mining and Market Basket Analysis
- 6.3Introduction to Sequential Pattern Mining
- 6.4AI-driven Techniques for Web Content Mining
- 6.5Time Series Data Mining using AI-enhanced Techniques
- 6.7AI-driven Predictive Analytics
- MODULE 7 - Deep Dive into Visualization with PythonFrom Matplotlib to Seaborn and advanced libraries, delve into techniques that transform raw information into visually compelling narratives, enhancing the understanding of trends and patterns in diverse datasets.7
- 7.0Overview of Traditional vs. AI-enhanced Data Visualization Techniques
- 7.1Key Python Libraries for AI-driven Visualization (Matplotlib, Seaborn, Plotly, Bokeh, Altair)
- 7.2Relation Plots (Scatter Plot, Bubble Plot, Correlogram, Heatmap)
- 7.3AI-driven Time Series Visualization
- 7.4Creating Geospatial Visualizations using AI
- 7.5Using AI-driven Forecasting Models
- 7.6Techniques for Visualizing Real-time Streaming Data using AI models
- MODULE 8 - Understanding Machine LearningLearn the algorithms that enable computers to learn from data and make predictions or decisions without explicit programming. Explores patterns, statistical models, and neural networks, enhancing systems to improve performance and adaptability across diverse domains like healthcare, finance, and technology4
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