Foundation Of Statistics For Data Analytics

In the fast-paced landscape of data analytics, the ability to decipher complex data and derive meaningful insights is a game-changer. Introducing our comprehensive training program, "Foundation of Statistics for Data Analytics," tailored for learners eager to delve into the realms of algorithms, predictive data modeling, and data visualization. Whether you are an individual seeking to enhance your analytics prowess or a company striving for data-driven excellence, this program is your gateway to unlocking the full potential of your data.

Navigating Organizational Domains: Gain a profound understanding of organizational domains and key business processes. The program equips you with the knowledge to apply analytics to tell the story of data, ensuring your insights align with organizational goals.
Mastering Predictive Modeling Techniques: Uncover methods to use exploratory visual analysis and predictive modeling. From identifying and prioritizing problems to developing prototype algorithms, the program guides you through the intricacies of predictive data modeling.
Deriving Insights from Data Mining: Explore methods to use data mining to discover new business insights. The program empowers you to interpret patterns in data and assess their relevance to critical business issues, paving the way for data-driven decision-making.

Training Duration: 5 Days

SBL-Khas Claimable
Certificate Of Completion Available
Private In-House Class Available
ILT & VILT Class Available

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DC-FSDA: Foundation Of Statistics For Data Analytics

RM9,000.00

RM7,500.00

This programme is created specifically for learners who wish to develop, apply, and evaluate algorithms, predictive data modeling and data visualisation to identify underlying trends and patterns in data.
Organisational domain(s) and key business processes

Methods to use analytics to tell the story of the data

Methods to use exploratory visual analysis and predictive modelling

Methods to identify and prioritise the problems to be solved

Methods to develop prototype algorithms

Methods to build a data model

Methods to use data mining to discover new business insights

Methods to interpret patterns in data and their relevance to business issues

Range of established and novel tools and techniques used in developing new business insights

Methods to apply complex software tools to analyse data

Use of statistical techniques, experimental techniques, and hypothesis testing

Apply predictive data modelling techniques to identify underlying trends and patterns in data using statistical computing tools, methods and procedures

Identify patterns across multiple data sets to derive insights

Develop prototype algorithms and proof of concept demonstrations

Make decisions about which patterns are meaningful, and which to further analyse

Assemble data aggregations to build data models to help test problem hypotheses

Use machine learning techniques to gain new insights from data

Mine data to find relevant insights to develop ongoing improvements

Asses the business insights presented to determine impact of insights on organisation

Manage the creation of interactive visualisations of data and data study outcomes

Use industry standard tools and techniques for data visualisation in line with organisational procedures
No prerequisites.
Individuals actively engaged in the field of data analytics.
Module 1:   Introduction To Foundation Of Statistics

Introduction to Business Analytics

Use cases of Predictive Analytics in Business

Introduction to Foundation of Statistics

Module 2:   Data Scoping, Exploration And Sense Making

Business measurement and data

Data connection, size, and quality

Data exploration visually

Module 3:   Data Insight Extraction And Explanation

Data insight extraction

Data analytics results interpretation

Data analytics results recommendation

Module 4:   Data Models And Prototyping Algorithms

Algorithm development process framework

Data selection and model development to support algorithms training and testing

Algorithm comparison

Algorithm recommendation

Module 5:   Data Storytelling

Introduction to Data Storytelling

Intrinsic characteristics of data

Overview of data visualisation and patterns

Alignment of reporting needs and choice of metrics and visualisation

DC-FSDA: Foundation Of Statistics For Data Analytics

OVERVIEW

OBJECTIVES

PREREQUISITES

AUDIENCE

COURSE MODULES

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