Data Analytics for Managers

Programme Overview

Understanding data analytics is a key skill that every manager needs. With the enormous explosion of data that organizations have access to, managers must know where their analysts are getting evidence from and how the data was generated. In addition, it has become imperative for managers to understand how data can be used to drive organizational decisions.

Participants in this course will be able to understand how robust data analysis can be used to drive decision making and how organizations uncover new information to provide avenues for strategic moves. No prerequisite in statistics or data analytics is needed for this course.

Programme Objectives

By the end of the course, participants will be able to:

Justify the need for investing organizational resources in data analytics
Ask the right questions to get the information needed for a robust data analysis project
Describe the methods and techniques that are used for data analytics projects
Explain how machine learning and artificial intelligence can help with data analytics
Communicate and defend data-driven findings and decisions to stakeholders

Target Audience

This course is ideal for leaders, managers, and professionals who want to learn how data analytics can help improve organizational decision-making processes.

Target Competencies

Understanding complex data
Analyzing data
Structuring data
Balanced decision-making
Visualizing data
Assessing data patterns
Influencing

Programme Outline

Overview of data analytics

An introduction to business data analytics
Quantitative data analysis
Navigating big data
Data scientists: The job of the 21st century
Thinking like a data scientist
The process of data analytics

Gathering the right information

Focused research
Getting the data you need
Asking for data and analytics
Designing business experiments
Using the scientific method
The difference between data and metrics
Understanding what is being measured
Trusting your data
Ensuring data is safe to use

Analyzing data

The value of data
Data visualization
Overview of predictive analytics
Looking to the future by looking at the past
Understanding regression analysis
Evaluating the relationship between variables
Acting on correlation
Assessing confidence in findings

Data analysis tools and techniques

Machine learning
Investing in artificial intelligence
Statistical significance
Linear thinking in a non-linear world
The pitfalls of data-driven decisions
Avoiding cognitive traps
Analytics that can cheat
Paying attention to outliers

Communicating findings

The value of data
Making meaning out of data
Making charts that persuade
Giving meaning to numbers
Communicating uncertainty
Understanding the likelihood of events
Challenging data findings
Ensuring that data is thorough
Decisions and data
Influencing through story telling

Programme Objectives

By the end of the course, participants will be able to:

Justify the need for investing organizational resources in data analytics
Ask the right questions to get the information needed for a robust data analysis project
Describe the methods and techniques that are used for data analytics projects
Explain how machine learning and artificial intelligence can help with data analytics
Communicate and defend data-driven findings and decisions to stakeholders

Target Audience

This course is ideal for leaders, managers, and professionals who want to learn how data analytics can help improve organizational decision-making processes.

Target Competencies

Understanding complex data
Analyzing data
Structuring data
Balanced decision-making
Visualizing data
Assessing data patterns
Influencing

Programme Outline

Overview of data analytics

An introduction to business data analytics
Quantitative data analysis
Navigating big data
Data scientists: The job of the 21st century
Thinking like a data scientist
The process of data analytics

Gathering the right information

Focused research
Getting the data you need
Asking for data and analytics
Designing business experiments
Using the scientific method
The difference between data and metrics
Understanding what is being measured
Trusting your data
Ensuring data is safe to use

Analyzing data

The value of data
Data visualization
Overview of predictive analytics
Looking to the future by looking at the past
Understanding regression analysis
Evaluating the relationship between variables
Acting on correlation
Assessing confidence in findings

Data analysis tools and techniques

Machine learning
Investing in artificial intelligence
Statistical significance
Linear thinking in a non-linear world
The pitfalls of data-driven decisions
Avoiding cognitive traps
Analytics that can cheat
Paying attention to outliers

Communicating findings

The value of data
Making meaning out of data
Making charts that persuade
Giving meaning to numbers
Communicating uncertainty
Understanding the likelihood of events
Challenging data findings
Ensuring that data is thorough
Decisions and data
Influencing through story telling

Overview of data analytics

An introduction to business data analytics
Quantitative data analysis
Navigating big data
Data scientists: The job of the 21st century
Thinking like a data scientist
The process of data analytics

Gathering the right information

Focused research
Getting the data you need
Asking for data and analytics
Designing business experiments
Using the scientific method
The difference between data and metrics
Understanding what is being measured
Trusting your data
Ensuring data is safe to use

Analyzing data

The value of data
Data visualization
Overview of predictive analytics
Looking to the future by looking at the past
Understanding regression analysis
Evaluating the relationship between variables
Acting on correlation
Assessing confidence in findings

Data analysis tools and techniques

Machine learning
Investing in artificial intelligence
Statistical significance
Linear thinking in a non-linear world
The pitfalls of data-driven decisions
Avoiding cognitive traps
Analytics that can cheat
Paying attention to outliers

Communicating findings

The value of data
Making meaning out of data
Making charts that persuade
Giving meaning to numbers
Communicating uncertainty
Understanding the likelihood of events
Challenging data findings
Ensuring that data is thorough
Decisions and data
Influencing through story telling



Programme Objectives

By the end of the course, participants will be able to:

Justify the need for investing organizational resources in data analytics
Ask the right questions to get the information needed for a robust data analysis project
Describe the methods and techniques that are used for data analytics projects
Explain how machine learning and artificial intelligence can help with data analytics
Communicate and defend data-driven findings and decisions to stakeholders



Target Audience

This course is ideal for leaders, managers, and professionals who want to learn how data analytics can help improve organizational decision-making processes.



Target Competencies

Understanding complex data
Analyzing data
Structuring data
Balanced decision-making
Visualizing data
Assessing data patterns
Influencing



Blockchain-based certification

BLOCKCHAIN-BASED CERTIFICATION

LIBT now offers learners Blockchain-based Certificates upon successful completion of academic and corporate training programmes. The single-click verification makes third-party authentication highly convenient. LIBT’s blockchain-based credentials are offered via Accredible, a specialized platform with bank-level encryption, which makes forgery and manipulation of credentials impossible. Shareable across social media & professional networking platforms, this accreditation is a golden opportunity for learners looking to attract better opportunities & prospects.

Programme Outline

Overview of data analytics

An introduction to business data analytics
Quantitative data analysis
Navigating big data
Data scientists: The job of the 21st century
Thinking like a data scientist
The process of data analytics

Gathering the right information

Focused research
Getting the data you need
Asking for data and analytics
Designing business experiments
Using the scientific method
The difference between data and metrics
Understanding what is being measured
Trusting your data
Ensuring data is safe to use

Analyzing data

The value of data
Data visualization
Overview of predictive analytics
Looking to the future by looking at the past
Understanding regression analysis
Evaluating the relationship between variables
Acting on correlation
Assessing confidence in findings

Data analysis tools and techniques

Machine learning
Investing in artificial intelligence
Statistical significance
Linear thinking in a non-linear world
The pitfalls of data-driven decisions
Avoiding cognitive traps
Analytics that can cheat
Paying attention to outliers

Communicating findings

The value of data
Making meaning out of data
Making charts that persuade
Giving meaning to numbers
Communicating uncertainty
Understanding the likelihood of events
Challenging data findings
Ensuring that data is thorough
Decisions and data
Influencing through story telling

Overview of data analytics

An introduction to business data analytics
Quantitative data analysis
Navigating big data
Data scientists: The job of the 21st century
Thinking like a data scientist
The process of data analytics

Gathering the right information

Focused research
Getting the data you need
Asking for data and analytics
Designing business experiments
Using the scientific method
The difference between data and metrics
Understanding what is being measured
Trusting your data
Ensuring data is safe to use

Analyzing data

The value of data
Data visualization
Overview of predictive analytics
Looking to the future by looking at the past
Understanding regression analysis
Evaluating the relationship between variables
Acting on correlation
Assessing confidence in findings

Data analysis tools and techniques

Machine learning
Investing in artificial intelligence
Statistical significance
Linear thinking in a non-linear world
The pitfalls of data-driven decisions
Avoiding cognitive traps
Analytics that can cheat
Paying attention to outliers

Communicating findings

The value of data
Making meaning out of data
Making charts that persuade
Giving meaning to numbers
Communicating uncertainty
Understanding the likelihood of events
Challenging data findings
Ensuring that data is thorough
Decisions and data
Influencing through story telling

Total Fee

Classroom -

£1390

Virtual-

£990

Programme Details

Code

TR-1075

Duration

5 Days

Delivery

Classroom or Virtual