Data Analytics Essentials (DAE) with Python

The worldwide revenue for Big Data and Data Analytics is expected to grow exponentially. Almost all industries and verticals are expected to increase their own spending and investment in Data technologies and solutions, helping to fuel growth of data technologies.

Together with an increase interest and investment in AI, new tools for collecting and analysing data and new enterprise roles and responsibilities will emerge presenting IT professionals and individuals planning to pursue a career in Big Data and Data Analytics with tremendous career opportunities. This can never be a better time to acquire the necessary skills and gain proficiency in Data Analytics and Data Science.

AI-Driven Data Analytics Essentials (DAE) using Python x Gen AI

Price range: $880.00 through $960.00

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.

Mode of Delivery

Date

Time / Duration

Course Material

Clear

SKU: N/A Categories: Certification Programs, Data Certification Tags: chatgpt, dae, data analytics, data cleaning, data preprocessing, data science, data strategies, data visualization, exploratory analysis, gen ai, predictive analytics, python for analytics

Overview

Every industry and vertical is expected to increase its investment in Data technologies and solutions, fueling the growth of a 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 have emerged, 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

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

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

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.

Module 1 - Introduction to AI-driven Data Analytics

AI-driven Data Analytics Overview
Data Analytics Built on AI-driven Methodologies
Importance and Advantages of AI-driven Data Analytics
Developing AI-driven Data Analytics Strategies, AI Tools,
and Methodologies
Data Analytics Maturity Model and the Use of AI within Analytics Processes
Understanding Descriptive, Predictive, and Prescriptive Analytics

Module 2 - Different Types of Analytics and Application

Different Application of AI-driven Analytics Method
Concepts of Text Analytics (Natural Language Processing) and Web Analytics with Machine Learning Algorithms
Data / Information Architecture for AI-driven Systems
AI-Enhanced Extract Transform Load (ETL) vs. Extract Load Transform (ELT) Architecture
Data Warehouse for AI-driven Data Analytics
Enhancing Business Intelligence and Data Analytics with AI Tools
Pragmatic Applications of AIdriven Application

Module 3 - Deep Dive into Python Programming for AI-driven Data Analytics

Overview of Python’s Role in AI and Data Analytics
Key Python Libraries for AI-driven Analytics: NumPy, Pandas, Scikit-learn, TensorFlow, Keras, and PyTorch
Setting up Python Environment for AI and Data Analytics
Data Cleaning and Preprocessing with Pandas and NumPy
Scaling, Normalizing, and Transforming Data for Machine Learning Models
Exploratory Data Analysis (EDA) with AI-powered Insights
Data Pipeline Automation with AI and Python

Module 4 - Key Modules / Packages in Python for AI-driven Data Analytics

Overview of Essential Python Libraries and Packages for AI and Data Analytics
Installing and Managing Python Packages using pip and conda
Data Manipulation and Transformation with Pandas
Numerical Computations with NumPy and SciPy
Core Machine Learning Models and Algorithms Available in Scikit-learn
Data Visualization with Matplotlib, Seaborn, and Plotly

Module 5 - Data Mining Processes for Data Analytics

Overview of AI in Data Mining and its Role in Data Analytics
Traditional Data Mining Techniques vs. AI-driven Approaches
AI-powered Tools for Data Cleaning and Anomaly Detection
Handling Missing Data, Outliers, and Noisy Data using AI Algorithms
Overview of AI-based Tools for Automating Data Mining
Enhancing Knowledge Discovery in Databases (KDD) with AI
Automating Knowledge Extraction, Pattern Recognition, and Trend Analysis
Future Trends in AI-Driven Data Mining

Module 6 - AI-Powered Pattern Discovery and Predictive Analytics in Data Mining Techniques

AI-driven Classification Techniques in Data Mining (Decision Trees, Random Forests, SVM, Neural Networks)
AI-driven Clustering Methods for Data Mining (K-means, DBSCAN, Hierarchical Clustering)
Association Rule Mining and Market Basket Analysis
Introduction to Sequential Pattern Mining
AI-driven Techniques for Web
Content Mining
Time Series Data Mining using
AI-enhanced Techniques
AI-driven Predictive Analytics

Module 7 - Deep Dive into Visualization with Python

Overview of Traditional vs. AI-enhanced Data Visualization Techniques
Key Python Libraries for AI-driven Visualization (Matplotlib, Seaborn, Plotly, Bokeh, Altair)
Relation Plots (Scatter Plot, Bubble Plot, Correlogram, Heatmap)
AI-driven Time Series Visualization
Creating Geospatial
Visualizations using AI
Using AI-driven Forecasting Models
Techniques for Visualizing Realtime Streaming Data using AI models

Module 8 - Understanding Machine Learning

Statistical Learning vs. Machine Learning
Iteration and Evaluation
Supervised, Unsupervised, and Reinforcement Learning
Different Phases of Predictive Modelling (Data Preprocessing, Sampling, Model Building, Validating)

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 AI-driven Data Analytics based on the syllabus covered.

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

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

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Selva Subramanian Sivanaiah

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Ahmad Tariq Ahmad Ziyad

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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

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

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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.