Data Analytics Essentials Using Python x ChatGPT @ Singapore

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

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

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

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 the growth of data technologies. Together with an increased interest and investment in AI, new tools for collecting and analyzing 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.

- Duration: 3 Days / 24 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

-Lecture1.1 - Data Analytics Overview

-Lecture1.2 - Concepts of Data Analytics

-Lecture1.3 - Importance and Advantages of Data Analytics

-Lecture1.4 - Developing / Application of Data Analytics Strategies

-Lecture1.5 - Data Analytics Maturity Model

-Lecture1.6 - Understanding Descriptive, Predictive and Prescriptive Analytics

MODULE 2 - DIFFERENT TYPES OF ANALYTICS AND APPLICATION

-Lecture2.1 - Different Application of Analytics Method

-Lecture2.2 - Concepts of Text Analytics and Web Analytics

-Lecture2.3 - Data / information architecture

-Lecture2.4 - ETL Architecture

-Lecture2.5 - What is Data Warehouse

-Lecture2.6 - Business intelligence vs Data Analytics

-Lecture2.7 - Application of Analytics in an Organisation

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MODULE 3 - DEEP DIVE INTO PYTHON PROGRAMMING Lecture3.1 - Introduction to Python Programming

-Lecture3.2 - Setting up Python IDE and Programming Environment

-Lecture3.3 - Understanding Structure of Python Programming

-Lecture3.4 - Python Variables: Integer, Floats, Strings

-Lecture3.5 - Using of List vs. Dictionary

-Lecture3.6 - Operators and Loops: If-Else, For, While, Break, Continue

-Lecture3.7 - Types of Functions in Python

-Lecture3.8 - Introduction to Built-In Functions in Python

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

-Lecture4.1 - Understanding Modules in Python

-Lecture4.2 - Working with NumPy Module

-Lecture4.3 - Using Python Pandas Module

-Lecture4.4 - Data Pre-processing, Data Cleaning, and Data Engineering

-Lecture4.5 - Introduction to MatPlotLib in Python

-Lecture4.6 - Data Visualization using Python Programming

MODULE 5 - DATA MINING PROCESSES FOR DATA ANALYTICS

-Lecture5.1 - Fundamentals of Data Mining

-Lecture5.2 - Objectives of Data Mining

-Lecture5.3 - Key aspects of Data Mining

-Lecture5.4 - Concepts of Knowledge Discovery in Databases (KDD)

-Lecture5.5 - Models in Data Mining

-Lecture5.6 - Data Mining Model vs Statistical Model

-Lecture5.7 - Data Mining Processes

MODULE 6 - DATA MINING TECHNIQUES

-Lecture6.1 - Different Data Mining Techniques

-Lecture6.2 - Data Classification

-Lecture6.3 - Clustering Analysis

-Lecture6.4 - Regression Analysis

-Lecture6.5 - Association Rules

-Lecture6.6 - Outliers Analysis

-Lecture6.7 - Sequential Patterns

-Lecture6.8 - Predictive Analytics

MODULE 7 - DEEP DIVE INTO VISUALIZATION WITH PYTHON

-Lecture7.1 - Data Visualization and Data Exploration

-Lecture7.2 - Comparison Plots (Line Chart, Bar Chart, Radar Chart)

-Lecture7.3 - Relation Plots (Scatter Plot, Bubble Plot, Correlogram, Heatmap)

-Lecture7.4 - Composition Plots (Pie Chart, Stacked Bar Chart, Stacked Area Chart)

-Lecture7.5 - Distribution Plots (Histogram, Density Plot, Box Plot, Violin Plot)

-Lecture7.6 - Geo Plots (Dot Map, Choropleth Map, Connection Map)

-Lecture7.7 - Enhancement of Visualization

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MODULE 8 - MATPLOTLIB AND SEABORN LIBRARIES IN PYTHON FOR VISUALIZATION

-Lecture8.1 - Deep Dive into Matplotlib in Python

-Lecture8.2 - Essential Matplotlib Components for Plotting

-Lecture8.3 - Basic Plots, Layouts, Images, and Mathematical Expressions using Matplotlib

-Lecture8.4 - Introduction to Seaborn in Python

-Lecture8.5 - Kernel Density Estimation, Bivariate Distribution, Pairwise Relationships

-Lecture8.6 - Multi-plots, Regression Plots, Squarify, and Geospatial Plotting using Seaborn

MODULE 9 - UNDERSTANDING MACHINE LEARNING

-Lecture9.1 - Statistical Learning vs. Machine Learning

-Lecture9.2 - Iteration and Evaluation

-Lecture9.3 - Supervised, Unsupervised, and Reinforcement Learning

-Lecture9.4 - Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validating)

Host or Publisher Casugol