Data Analytics Essentials Using Python Programming - Onsite @ Cebu

For more information: www.casugol.com/dae

Duration: 4 Day / 32 Hours

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

Who Should Attend: Aspiring Data Scientist, Data Analyst, HR Analyst, and Anyone interested in pursuing a career in the areas of Business Analytics / 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

Pre-Requisite

No pre-requisite. Data Analytics Essentials (DAE) is suitable for anyone who is interested in Data Analytics and does not have any prior technological experience

Examination

Participants are required to attempt an examination upon completion of the course. This exam tests a candidate’s knowledge and skills related to Data Analytics 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
• Introduction to Classes in Python
• What is Object-Oriented Programming (OOP)

Module 4 Working with Key Modules / Packages

• 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 Data 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 extensive hands-on exercises / practical, rigorous usage of real-time case studies, hands-on exercises and group discussion