[ONLINE] Data Analytics Essentials (DAE) with Python

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Acquire the essential knowledge on how to use data analytics to make better business or organizational decisions.

<a href="http://www.casugol.com/dae" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">http://www.casugol.com/dae</a>Course Information
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 on how to use data analytics to make better business or organisational decisions.

Learn the different components of Data Analytics, Data Mining, Data Warehousing and Visualization using Python


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 course. This exam tests a candidate’s knowledge and skills related to Data Analytics and Python Programming based on the syllabus covered

Module 1 Introduction to Data Analytics

Topics Covered
<ul><li>Data Analytics Overview
</li><li>Concepts of Data Analytics
</li><li>Importance and Advantages of Data Analytics
</li><li>Developing / Application of Data Analytics Strategies
</li><li>Data Analytics Maturity Model
</li><li>Understanding Descriptive, Predictive and Prescriptive Analytics
</li></ul>Module 2 Different Types of Analytics and Application

Topics Covered
<ul><li>Different Application of analytics method
</li><li>Concepts of Text Analytics and Web Analytics
</li><li>Different Application of Analytics Methods
</li><li>Data / information architecture
</li><li>ETL Architecture
</li><li>What is Data Warehouse
</li><li>Business intelligence vs Data Analytics
</li><li>Application of Analytics in an Organisation
</li></ul>Module 3 Deep Dive into Python Programming

Topics Covered
<ul><li>Introduction to Python Programming
</li><li>Setting up Python IDE and Programming Environment
</li><li>Understanding Structure of Python Programming
</li><li>Python Variables: Integer, Floats, Strings
</li><li>Using of List vs. Dictionary
</li><li>Operators and Loops: If-Else, For, While, Break, Continue
</li><li>Types of Functions in Python
</li><li>Introduction to Built-In Functions in Python
</li><li>Introduction to Classes in Python
</li><li>What is Object-Oriented Programming (OOP)
</li></ul>Module 4 Working with Key Modules / Packages

Topics Covered
<ul><li>Understanding Modules in Python
</li><li>Working with NumPy Module
</li><li>Using Python Pandas Module
</li><li>Data Pre-processing, Data Cleaning, and Data Engineering
</li><li>Introduction to MatPlotLib in Python
</li><li>Data Visualization using Python Programming
</li></ul>Module 5 Data Mining Processes for Data Analytics

Topics Covered
<ul><li>Fundamentals of Data Mining
</li><li>Objectives of Data Mining
</li><li>Key aspects of Data Mining
</li><li>Concepts of Knowledge Discovery in Databases (KDD)
</li><li>Models in Data Mining
</li><li>Data Mining Model vs Statistical Model
</li><li>Data Mining Processes
</li></ul>Module 6 Data Mining Techniques

Topics Covered
<ul><li>Different Data Mining Techniques
</li><li>Data Classification
</li><li>Clustering Analysis
</li><li>Regression Analysis
</li><li>Association Rules
</li><li>Outliers Analysis
</li><li>Sequential Patterns
</li><li>Predictive Analytics
</li></ul>Module 7 Understanding Machine Learning

Topics Covered
<ul><li>Statistical learning vs Machine learning
</li><li>Iteration and evaluation
</li><li>Supervised, Unsupervised and Reinforcement Learning
</li><li>Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
</li></ul>Module 8 Data Visualization with Python

Topics Covered
<ul><li>Introduction Exploratory Data Analysis
</li><li>Descriptive statistics, Frequency Tables and summarization
</li><li>Univariate Analysis (Distribution of data, Graphical Analysis)
</li><li>Bivariate Analysis (Cross Tabs, Distributions &amp; Relationships, Graphical Analysis)</li></ul>

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[ONLINE] Data Analytics Essentials (DAE) with Python

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