On May 23-25, 2018, CDA Data Analysis Institute conducted a technology sharing activity on the theme of "Python Application of Artificial Intelligence" in Beijing Research Institute of China Telecom Corporation Limited. The employees of the various departments of the center actively registered, and a total of 70 employees participated in the concentrated learning. Teachers and relevant colleagues in the data analysis department actively communicated, and this activity was a complete success.
brief introduction
Day 1: Basics of Python programming (all explained through practical cases)
1. Preliminary Grammar
2. Data structure (list, string, tuple, collection, dictionary and string)
3. Conditional selection structure and cyclic structure (random simulation)
4. Application of several important built-in functions
5. Functions, modules and their applications
6. Use of arrays
7. File operations
8. Sorting and searching, recursive algorithm
9. Introduction to Regular Expressions
10. Introduction to object-oriented programming
Day 2: Use numpy, pandas, etc. for data cleaning and sorting, statistical analysis
1. Organize data (slicing, generating random numbers, copying, broadcasting, sorting, etc.)
2. Various methods of data indexing and selection
3. Grouping, dividing, merging and transforming data
4. Text data processing skills
5. Sampling distribution and hypothesis testing (including non-parametric hypothesis testing)
6. Construction and prediction of linear models (including regression analysis and discrete dependent variable models)
7. Principal component analysis and factor analysis
8. Contingency table and corresponding analysis
9. Time series data processing, modeling and forecasting (ARIMA)

Day 3: Python machine learning algorithm and data mining case combat
1. Probability, approximation and EM algorithm principles and examples
2. Nearest neighbor k-NN algorithm and application examples (customer segmentation, digital image recognition)
3. Naive Bayes algorithm
4. Several cases of machine learning using scikit-learn module (including regression, decision tree, support vector machine, gradient descent, integrated learning, random forest and other algorithm models)
5. k-means clustering (unsupervised learning, how to determine the optimal number of clusters)
6. Feature engineering and variable selection
7. Classic case of prediction using XGBoost (cross-validation, grid search, parameter optimization)
8. Talking about logistic regression and case analysis
9. Text mining principles and cases (word segmentation, TF-IDF criteria, text classification, unstructured data analysis)
10. Deep learning principles and several application cases
Employee experience and evaluation
This event is rich in content and basically covers the commonly used algorithms and skills of machine learning, which is exactly what the project needs. With more knowledge points, he has expanded his data thinking, gained a deeper understanding of data methods, and improved his hands-on practical ability. Digest for a while, and hope to have more exchanges and study with CDA Data Analysis Institute in the special subject courses.