Data Analytics Essentials Using Python x GEN AI @ Kuala Lumpur, Malaysia

Schedule

Mon Oct 14 2024 at 09:30 am to Tue Oct 15 2024 at 05:30 pm

Location

Bangsar South | Kuala Lumpur, KL

Learn how data analytics enhances decision-making and uncover hidden data insights. Learn the core components of Data Analytics with Python
About this Event

For more information: https://casugol.com/course/dae

International Acclaimed Certification. 5-Star Reviews

Suitable for everyone. Learn in an Interactive, Supportive, and Encouraging Environment.

Duration: 16 Hours
Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination

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.

Examination

Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Artificial Intelligence, Machine Learning, and Python Programming based on the syllabus covered

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

MODULE 1 - INTRODUCTION TO DATA ANALYTICS

Data Analytics Overview
Concepts of Data Analytics
Importance and Advantages of Data Analytics
Developing / Application of Data Analytics Strategies
Data Analytics Maturity Model
Understanding Descriptive, Predictive and Prescriptive Analytics

MODULE 2 - DIFFERENT TYPES OF ANALYTICS AND APPLICATION

Different Application of Analytics Method
Concepts of Text Analytics and Web Analytics
Data / information architecture
ETL Architecture
What is Data Warehouse
Business intelligence vs Data Analytics
Application of Analytics in an Organisation

MODULE 3 - DEEP DIVE INTO PYTHON PROGRAMMING

Introduction to Python Programming
Setting up Python IDE and Programming Environment
Understanding Structure of Python Programming
Python Variables: Integer, Floats, Strings
Using of List vs. Dictionary
Operators and Loops: If-Else, For, While, Break, Continue
Types of Functions in Python
Introduction to Built-In Functions in Python

MODULE 4 - WORKING WITH KEY MODULES / PACKAGES IN PYTHON FOR DATA ANALYTICS

Understanding Modules in Python
Working with NumPy Module
Using Python Pandas Module
Data Pre-processing, Data Cleaning, and Data Engineering
Introduction to MatPlotLib in Python
Data Visualization using Python Programming

MODULE 5 - DATA MINING PROCESSES FOR DATA ANALYTICS

Fundamentals of Data Mining
Objectives of Data Mining
Key aspects of Data Mining
Concepts of Knowledge Discovery in Databases (KDD)
Models in Data Mining
Data Mining Model vs Statistical Model
Data Mining Processes

MODULE 6 - DATA MINING TECHNIQUES

Different Data Mining Techniques
Data Classification
Clustering Analysis
Regression Analysis
Association Rules
Outliers Analysis
Sequential Patterns
Predictive Analytics

MODULE 7 - DEEP DIVE INTO VISUALIZATION WITH PYTHON

Data Visualization and Data Exploration
Comparison Plots (Line Chart, Bar Chart, Radar Chart)
Relation Plots (Scatter Plot, Bubble Plot, Correlogram, Heatmap)
Composition Plots (Pie Chart, Stacked Bar Chart, Stacked Area Chart)
Distribution Plots (Histogram, Density Plot, Box Plot, Violin Plot)
Geo Plots (Dot Map, Choropleth Map, Connection Map)
Enhancement of Visualization

MODULE 8 - MATPLOTLIB AND SEABORN LIBRARIES IN PYTHON FOR VISUALIZATION

Deep Dive into Matplotlib in Python
Essential Matplotlib Components for Plotting
Basic Plots, Layouts, Images, and Mathematical Expressions using Matplotlib
Introduction to Seaborn in Python
Kernel Density Estimation, Bivariate Distribution, Pairwise Relationships
Multi-plots, Regression Plots, Squarify, and Geospatial Plotting using Seaborn

MODULE 9 - UNDERSTANDING MACHINE LEARNING

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

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 with a Certificate of Competency from CASUGOL.

Awarded upon completing and meeting all requirements of the course.