Data Analytics Essentials (DAE) with Python

  Learn Online    4-day / 32 Hours   International Certification

    14 -17 Sep 2021  I   12 – 15 Oct 2021   9:30am – 5.30pm

  Learn Online   

4-day / 32 Hours 

 International Certification

  14 -17 Sep 2021  I   12 – 15 Oct 2021

9:30am – 5.30pm

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

Who Should Attend?

  • Aspiring Data Scientist
  • Data Analyst
  • HR Analyst
  • Anyone interested in pursuing a career in the areas of Business Analytics / Data Analytics. 

Programme Details

Data Analytics Essentials (DAE) with Python involves rigorous usage of real-time case studies, hands-on exercises and group discussion

  • 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
  • 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
  • Statistical learning vs Machine learning
  • Iteration and evaluation
  • Supervised, Unsupervised and Reinforcement Learning
  • Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
  • 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
  • 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
  • Introduction Exploratory Data Analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data, Graphical Analysis)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • 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)
  • Different Data Mining Techniques
  • Data Classification
  • Clustering Analysis
  • Regression Analysis
  • Association Rules
  • Outliers Analysis
  • Sequential Patterns
  • Predictive 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
  • 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
  • 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)
  • 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
  • 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
  • Different Data Mining Techniques
  • Data Classification
  • Clustering Analysis
  • Regression Analysis
  • Association Rules
  • Outliers Analysis
  • Sequential Patterns
  • Predictive Analytics
  • Statistical learning vs Machine learning
  • Iteration and evaluation
  • Supervised, Unsupervised and Reinforcement Learning
  • Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
  • Introduction Exploratory Data Analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data, Graphical Analysis)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

HRDF

All our training programs are HRDF approved, under the “SBL-Khas” scheme. Fee will be paid by PSMB to REDtone on behalf of employers. No upfront payment is required from the participants. For more information, please visit www.hrdf.com.my

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