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2023 HOT NEWS
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Introduction to course advantages
●Leading digital intelligence empowerment, proficient in model application
In the era of digital economy, the use of digital knowledge can enable enterprises to get rid of a single supply, dig deep into user needs, and explore multiple business scenarios. This course will explain the construction ideas of data applications at different stages from the perspective of enterprises, train students to master the agile algorithm modeling ability needed by enterprises, and plan the roadmap for future development.
●Covering common tools and perfecting technology
The course covers the application and implementation of common tools such as Sklearn, LightGBM, NLP, PyTorch, Transformer, etc., and analyzes business needs according to the output results, providing data support for reasonable and effective strategy optimization.
●Fun with the actual combat of the case, direct access to the enterprise employment
course involves a large number of enterprise project cases: accurate marketing forecasting, marketing strategy optimization, customer behavior analysis, risk management, customer management, intelligent recommendation, sentiment analysis, anti-fraud, etc. Enter famous enterprises to provide project endorsement. Students who have a relatively shallow understanding of data science positions can choose a career development path that suits them with the help of teachers from the career planning team.▼
·CDA data analyst
2023 Agile Algorithmic Modeling Course Starts
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Broad target audience
On-the-job promotion crowd
Change career data analysis crowd
CDA applicants for on-the-job promotion and job transfer
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The course covers a comprehensive
Chapter 1: Pre-class Basics - Database SQL
Chapter 2: Pre-class Basics - Python
Chapter 3: Pre-class Basics - Basics of Mathematical Statistics
Chapter 4: SQL
Chapter Five: Index System + Statistical Analysis
Chapter 6: Pandas
Chapter 7: Analysis of Variance and Linear Regression
Chapter 8: Logistic Regression and Principal Component Analysis
Chapter 9: Label System and User Portrait
Chapter 10: Time Series
Chapter 11: Data Processing and Feature Engineering
Chapter 12: Cluster Analysis and Decision Trees
Chapter Thirteen: Digital Work Methods
Chapter Fourteen: ETL and Data Warehouse
Chapter 15: Data Access and Big Data
Chapter 16: Decision Trees
Chapter 17: Data Mining and Pipeline
Chapter 18: Regular regression, SVM
Chapter 19: Association Rules and Collaborative Filtering
Chapter 20: Integration and Improvement Methods
Chapter 21: Advanced Feature Engineering
Chapter 22: Deep Learning Basics
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The case applies to a wide range of industries
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main lecturer
Chang Guozhen
Doctor of Accounting, Peking University, executive director of CDA Data Science Research Institute, member of the Expert Committee of China Big Data Industry Ecological Alliance.
• Served as KPMG Consulting Big Data Director. External tutor of Beijing Language and Culture University for Master of Finance, external lecturer of University of Chinese Academy of Social Sciences and many other institutions.
• Has 18 years of experience in data mining, lean data governance, and data planning consulting.
Li Qi
• Chairman of the Spreadsheet Application Conference, once served as the data analysis project leader of the IBM sales management team, and a senior consultant of the Deloitte data analysis team.
• Has rich online and offline training experience in data analysis, including 1,000 offline trainings, and the total number of online + offline training exceeds 500,000 people.
Zhang Zhiqi
• Senior consultant of Deloitte's data analysis team, involving consulting projects in multiple fields such as FMCG, communications, Internet catering, and banking.• Senior data analyst of Fosun Group's investment due diligence team.
• Startup company data scientist, trained more than 700 times and trained more than 300,000 people
Ding Yajun
• Director of Data Analysis of Nanjing Shangdu Consulting, moderator of SAS and SPSS editions of Economic Management Home Forum.• Lecturer of CDA Data Science Research Institute and SAS, SPSS software lecturer, IBM SPSS-China/SAS-Taiwan consultant.
• Author of Statistical Analysis: From Small Data to Big Data.
Xu Yang
• Graduated from Glasgow University with a master's degree in econometrics. He has worked for the Chinese Academy of Social Sciences and the Bank of China, and has long been engaged in algorithm research and development.
• Under the tutelage of Hisayuki Yoshimoto, he mainly focuses on the direction of spatial measurement, and has in-depth research on various regression models and simultaneous equation models.
• The doctoral research topic is parameter identification of spatial matrix instrumental variables of neural network.
More info ↓ ↓ ↓
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CDA data analyst
2023 Agile Algorithmic Modeling Course Starts
1 set of curriculum system allows you to be qualified for 6 emerging positions
2023 Agile Algorithmic Modeling Course starts //
Live start time
March 25, 2023
