Course Outline

1: Introducing data analytics – 1 day

• What is data analytics
• Why data analytics is important
• Current status of data analytics
• Concepts and terminologies: dataset, filter, noise,
• Real business case study

2: Concepts and terminologies – 2 day

• Dataset, big data, data warehouse and variation
• Filter, noise, and data cleaning
• Bell curve and normal distribution
• Bias and skew
• Causes of bias and skew
• Supervised vs unsupervised
• Supervised learning: training set and verification set
• Unsupervised learning: clustering

3. Data analytics process – 2 day

• Four steps of data analytics
• Prerequisite of each step
• Expected result of each step
• Overfitting and underfitting
• Causes of overfitting and underfitting

4: Popular data analytics tool: R – 3 days

• What R made of
• Required dataset form
• Data filtering operation in R
• Evaluation of filtering result
• Supported analytics algorithms
• How to choose an algorithm
• Analytics result interpretation and final report criterias
• Case study and wrap up

5: Popular data analytics tool: Hadoop/MapReduce – 3 days

• Background and history of Apache Hadoop/MapReduce
• Characteristics of Hadoop
• Dataset requirements
• Working process
• Mapping and reducing
• Generating final report
• Performance monitoring
• Case study and wrap up

6: Popular data analytics tool: Tensorflow – 3 days

• Past, present, and future of Tensorflow
• Features and strength of Tensorflow
• Some Python knowledge before hand
• Data filtering as usual
• Basics of built-in neural network algorithm
• Simple explanation of neural network
• Potential customization of algorithm
• Diagnosing some obvious errors and mistakes
• Case study, and warp up

7: Popular data analytics tool: Weka – 3 days

• Origin, history, and background of Weka
• General analysis process and operations
• Supported algorithms out of the box
• Setup for supervised and unsupervised
• fundamental of neural network
• Final report and interpretation
• Wrap up

8: Summarize and course wrap up – 1 day

• Community of data analytics
• Perspective of the industry
• Where to seek improvement in the future

Tuition：$8,900