Duration
1 day
Overview
- Get familiar with essential concepts of Data Analytics, Data Science, Artificial Intelligence, and Machine Learning
- Practically understand how to map a business problem in your organization into an analytics solution
- Explore the data analytics and AI practical use cases and how it can boost business performance
- Learn how to evaluate Analytics and AI solutions and the impact they could have on your business
- Learn how to build the in-house data analytics team in your organization
- Know what kind of training or skills people need to build a successful data science team
- Know the concepts and applications of some advanced topics in data analytics like deep learning, process mining, and further related topics.
Course Outline
- Introduction to Data Analytics
- What should know about Data Analytics, Data Science (DS), Artificial Intelligence (AI), and Machine Learning (ML)
- Types and Sources of Data
- Data Repositories
- How to design an ETL in Data Analytics Projects
- What is Data Democratization?
- Data vs. Information vs. Actionable Insights
- Exploratory Analytics: Concept, Usage, and Techniques
- Predictive Analytics: Concept, Usage, and Techniques
- Prescriptive Analytics: Concept, Usage, and Techniques
- Recommender Systems
- How to differentiate the Data Scientists, Data Analysts, and Data Engineers?
- Success Stories: Data Analytics and AI for Business Improvement
- Data Visualization and Analytics War Rooms: Visualization and interactive dashboards that help managers making data driven decisions
- Process Mining: To detect process bottlenecks in your organization and improve your business performance
- Market Basket Analysis: Predicting the purchasing behaviour of your customer
- Social Media Analytics: How your customers talk about your brand in social media?
- Organizational Network Analysis: Who are main influencers in your organization?
- Deep Learning: How Artificial Neural Networks can improve the business
- Bringing AI to your Organization
- How to ask a right questions in a data science project?
- How to prepare the right data for your data analytics project?
- As a manager, How to guide your data science teams to success?
- The whole pipeline of a data science project
- The iterative nature of data analytics projects
- How to identify the project objectives in your organization using SMART methodology?
- Introduction to Industry 4.0
- How machine learning works?
- How to measure the accuracy of machine learning solution?
- What is deep learning? When to use it?
- Big Data and its applications
- Introduction to Big Data concepts and technologies
- What is the role of each Hadoop essential components such as Pig, Hive, Flume, Spark, …
- The phases of development of a Big Data strategy within a corporation An introduction to Industry 4.0
- 9 pillars of IR 4.0
- An example of augmented reality
- How to get prepared for IR 4.0
- Plan a Real Analytics Project for Your Business
- Domain study and identifying the business problem
- Identifying your analytics project objective using the S.M.A.R.T method.
- What data you have or need to prepare?
- Talent Management: how to build your analytics team and what are the expected qualifications?
- Analytics project planning
- Mapping your business problem into analytics solution
- What elements you need in your Analytics War Room?
Programme scope, delivery arrangements and suitability are confirmed during enquiry.