DNA-105: Foundation of Statistics for Data Analytics

Name: DNA-105: Foundation of Statistics for Data Analytics
Brand: DNA Capitals

RM6,000.00

RM4,500.00

Attend our specialized training program to develop, apply, and evaluate algorithms, predictive data modeling, and data visualization techniques. Acquire knowledge of statistical foundations, exploratory visual analysis, data mining, and data sense-making. Learn to extract insights from data, develop data models, and prototype algorithms. Gain expertise in data storytelling and the use of industry-standard tools for interactive visualizations.

Enhance your ability to identify patterns, make data-driven decisions, and communicate impactful insights. Join us to unlock the power of data analytics and visualization for driving organizational success.

Training Duration: 3 Days

Certificate Of Completion Available
Group Private Class
VILT Class Available
SBL-Khas Claimable

Download Course Outline

This programme is created specifically for learners who wish to develop, apply and evaluate algorithms, predictive data modelling 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

Assess 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
MODULE 1: INTRODUCTION TO FOUNDATION OF STATISTICS FOR DATA ANALYTICS
Topics:

Introduction to business analytics

Use cases of predictive analytics in business

Introduction to Foundation of Statistics

Mapped to:

K1 Organisational domain(s) and key business processes

K4 Methods to identify and prioritise the problems to be solved

K11 Use of statistical techniques, experimental techniques, and hypothesis testing

A1 Apply predictive data modelling techniques to identify underlying trends and patterns in data using statistical computing tools, methods and procedures

Rationale for Sequencing of the Units

The first learning unit starts with an overview and introduction of the Foundation of Statistics course, to begin the building blocks in statistical techniques, hypothesis testing and predictive analytics.

MODULE 2: DATA SCOPING, EXPLORATION AND SENSE MAKING
Topics:

Business measurement and data

Data connection, size and quality

Data exploration visually

Mapped to:

K3 Methods to use exploratory visual analysis and predictive modelling

K7 Methods to use data mining to discover new business insights

A2 Identify patterns across multiple data sets to derive insights

A4 Make decisions about which patterns are meaningful, and which to further analyse

Rationale for Sequencing of the Units

Subsequently, the next learning unit teaches learners on data scoping, data exploration and data sense making through predictive modelling, data mining, etc.

With the data set, they begin to learn about data pattern recognition and which patterns to analyse and investigate further.

MODULE 3: DATA INSIGHT EXTRACTION AND EXPLANATION
Topics:

Data insight extraction

Data analytics results interpretation

Data analytics results recommendation

Mapped to:

K8 Methods to interpret patterns in data and their relevance to business issues

A6 Use machine learning techniques to gain new insights from data

A7 Mine data to find relevant insights to develop ongoing improvements

A8 Assess the business insights presented to determine impact of insights on organisation

Rationale for Sequencing of the Units

In the next learning unit, the learners move to the next step in the sequence whereby they extract data for insight and analysis.

Learners are taught methods such as clustering and decision tree.

MODULE 4: DATA MODELS AND PROTOTYPING ALGORITHMS
Topics:

Algorithm development process framework

Data selection and model development to support algorithms training and testing

Algorithm comparison

Algorithm recommendation

Mapped to:

K5 Methods to develop prototype algorithms

K6 Methods to build a data model

A3 Develop prototype algorithms and proof of concept demonstrations

A5 Assemble data aggregations to build data models to help test problem hypotheses

Rationale for Sequencing of the Units

In the next learning unit, the learners are brought through data models and developing proof of concept algorithms.

MODULE 5: DATA STORYTELLING
Topics:

Introduction to Data Storytelling

Intrinsic characteristics of data

Overview of data visualisation and patterns

Alignment of reporting needs and choice of metrics and visualization

Mapped to:

K2 Methods to use analytics to tell the story of the data

K9 Range of established and novel tools and techniques used in developing new business insights

K10 Methods to apply complex software tools to analyse data

A9 Manage the creation of interactive visualisations of data and data study outcomes

A10 Use industry standard tools and techniques for data visualisation in line with organisational procedures

Rationale for Sequencing of the Units

The last learning unit pulls the building blocks together.

Learners have been brought through data analysis, to data analytics and learn about data visualisation and data storytelling.