Duration
2 days
What You will Learn
- Get familiar with essential concepts of Data Science, Artificial Intelligence, and Machine Learning
- Practically understand how to map a business problem in your organization into an analytics solution
- Explore the 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 science team in your organization
- Know what kind of training or skills do people need to build a successful data science team
- Know the concepts and applications of some advanced topics in data science like deep learning, Machine learning, and further related topics.
Outline
- 1. Introduction to AI
- What managers should know about Data Science(DS), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)
- 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?
- What is Data Democratization?
- 2. Success Stories: AI for Business Improvement
- Analytics War Rooms: 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
- Machine Learning: Help to predict the future situation of the organization
- 3. 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?
- 4. 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?
- Analytics project planning
- Mapping your business problem into analytics solution
- What elements you need in your Analytics War Room?
- 5. Building a Machine Learning model
- Introduction to machine learning
- Training and test datasets
- How to prepare datasets in Python
- Introduction to Regression
- Simple Linear Regression
- Multivariate regression
- Logistic Regression
- Polynomial Regression
- How to measure the accuracy of regression models
- Introduction to Classification models
- Decision trees
- K-Nearest Neighbours
- Logistic Regression
- SVM
- How to measure the accuracy of classification models
- Step by Step on Clustering
- 6. Deep Learning
- What is deep learning?
- Getting started with neural networks
- Deep learning for computer vision
- How to do deep learning with Python
Programme scope, delivery arrangements and suitability are confirmed during enquiry.