Dealing with Analytics through Artificial Intelligence
Course Info
Code IND02-109
Duration 5 Days
Format Classroom Online
Course Summary
For an organisation or business to function successfully, there needs to be great focus on analysing and processing data from a variety of sources. As the business, market and industry develop, there is an increasing amount of data being generated which is too vast for an individual or team to manually process. However, with new artificial intelligence technology arises, data analysis processes can be automated.
The primary use of artificial intelligence is to gather, analyse and present data. Artificial intelligence systems can be assigned with a vast amount of tasks, such as product ranking and customer segmentation, to reduce the workload of those responsible for data analysis. To implement these systems, there must be an understanding of the different types of artificial intelligence systems, their structures, and inner algorithms.
However, it is essential to manage these systems and consistently monitor performance. There are several factors that can negatively influence collected data, and these will need to be examined and resolved to ensure all future results are viable and free from inaccuracies or bias.
During this course, you’ll learn:
- To understand the importance of managing analytics within an organisation.
- To establish organisational goals and objectives and create action plans detailing them.
- To utilise artificial intelligence to gather, analyse and present key data.
- To evaluate the various types of artificial intelligence systems and which ones are ideal for conducting different tasks.
- To identify ideal circumstances for using artificial intelligence.
- To assess the reliability of different artificial intelligence systems and minimise associated risks.
- To align artificial intelligence with analytic goals to improve organisational functions and processes.
This course is designed for anyone with the responsibility of data analytics within an organisation. It would be most beneficial for:
- Data Analysts
- Business Analysts
- Operations Managers
- Chief Information Officers (CIOs)
- Artificial Intelligence Engineers
- Machine Learning Engineers
- Quality Assurance Managers
- Finance Managers
This course uses a variety of adult learning styles to aid full understanding and comprehension. Participants will review established businesses who utilise various AI systems to highlight efficient data analysis processes and potential areas for improvement.
To successfully partake in a range of learning exercises, participants will be supplied with all the necessary tools. In conjunction with these, they will also partake in seminars, group discussions, demonstrations, and group activities. This is to guarantee full comprehension of the taught content and related skills.
Course Content & Outline
Section 1
- Intelligent Decisions with Artificial Intelligence
- The importance of effective decision making in business.
- Guaranteeing intelligent decision making through data analysis.
- Explaining the concepts, principles and purpose of artificial intelligence and machine learning.
- Common types of AI systems and their typical uses.
- Exploring the benefits and limitations of different AI systems.
- How AI can encourage intelligent decision making.
Section 2
- Analysing Data
- The vitality of gathering, analysing, and recording data.
- Methods of data analysis – Monte Carlo simulation, cohort, cluster, sentiment, and factor analysis.
- Identifying the advantages and disadvantages of different data analysis methods.
- Integrating AI systems to automatically analyse datasets.
- Increasing the efficiency of gathering and analysing data with AI.
- Using data to explain why particular events occurred and predict future market and business changes.
Section 3
- Machine Learning
- The role of machine learning within an AI system.
- Understanding the main types of machine learning – supervised, reinforced and unsupervised.
- The processes of classification, clustering, and regression for different datasets.
- Using these processes for customer segmentation and the ranking of products, services, and users.
- Conducting a basket analysis with machine learning systems.
Section 4
- Thinking like Humans
- Balancing human knowledge with machine knowledge.
- Automating predictive modelling and analysis through deep learning.
- How deep learning structures and algorithms imitate the way the human brain gains knowledge.
- Deep learning structures and algorithms – neural networks, node layers, input layer, hidden layers, and output layers.
Section 5
- Measuring Performance
- Ai system optimisation through genetic algorithms and swarm intelligence.
- Evaluating the internal and external factors that influence the success of AI and data analytic projects.
- Managing system risks and minimising potentially damaging influences.
- Methods of effectively monitoring AI system performance.
- Examining inaccurate results, carefully identifying the cause, and rectifying them.
Enhancing Decision-Making: The Power of Analytical Thinking
This blog post explores the definition of analytical thinking, its skills, and its importance in the workplace. Discover examples, tips to develop analytical thinking, and understand its significance in an AI-driven world.
This blog post explores the definition of analytical thinking, its skills, and its importance in the workplace. Discover examples, tips to develop analytical thinking, and understand its significance in an AI-driven world.
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