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, block chain, and further related topics.
Course Outline
- 1. Introduction to Data Analysis
- Data vs. Information vs. Actionable Insights
- Exploratory Analytics: Concept, Usage, and Techniques
- Predictive Analytics: Concept, Usage, and Techniques
- Prescriptive Analytics: Concept, Usage, and Techniques 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?
- Analytics project planning
- Mapping your business problem into analytics solution
- 2. Plan a Real Analytics Project for Your Business
- 3. Introduction To python for data analysis
- Introduction to Numpy
- Introduction to Pandas
- Introduction to Data Visualizations
- Statistical Data Analysis-Basic
- Data preparation and data cleaning
- Introduction to machine learning
- Supervised and unsupervised Learning
- Training and test datasets
- How to prepare datasets
- Error rates in machine learning
- How to measure the accuracy of machine learning algorithms
- How to improve the accuracy of machine learning algorithms
- Some case studies on usage of Machine Learning in real world
- Introduction to Regression
- Simple Linear Regression
- Multivariate regression
- Logistic Regression
- Polynomial Regression
- Ensemble methods
- How to measure the accuracy of regression models
- 4. Building a Machine Learning model
To build a machine learning model, we need to prepare the training and test datasets, and then a mechanism to validate the built model. This topic includes:
- 5. Regression Models (Supervised Learning)
Regression is a statistical measure used in finance, investing and other disciplines that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables). This topic includes:
Programme scope, delivery arrangements and suitability are confirmed during enquiry.