The details of the data analysis and mining courses in Jami Valley, from theory to cloud practice environment to actual projects, will teach you to master data analysis and mining technology from 0, and lead you into the data age.
Phase 1 (python basics)
Introduction to python: 1. Introduction to Python version features 2. Python application scenarios and trends 3. Python development environment construction 4. Python development tools and operating environment 5. Identifiers and keywords, notes 6. Python installation in various systems 7 , Application scenarios and data storage design 8, Basic guide for Python program development 9, How to run python code
Basic python syntax: 1. Python selection and looping 2. Python string processing 3. Visual python programming 4. Data and type manipulation 5. Python objects, numbers, sequences 6, Python mapping and collection types 7, Python conditions and loops 8, Python files and I/O 9, python errors and exceptions
Python advanced syntax: 1. Functions and functional programming 2. Python object-oriented programming 3. Python regular expressions 4. Python functional programming 5. Python multi-threaded programming 6. Python graphical interface programming 7. Python database programming creation 8, Python extensions
Python programming development: 1. PYQT implements GUI tools 2. How to run python code 3. Development of Python in Linux 4. Use of GitHub 5. Python program development 6. Python api usage and secondary development
The second stage (relational database MySQL)
Database design: 1. Database design and operation management 2. Explanation of database design process 3. Conceptual structure design and ER diagram 4. Logical structure design and ER conversion rules 5. Data flow diagram and data dictionary 6. Database design physical model 7. Database Transactions and isolation levels
Database paradigm and ACID features: 1. Examples of database paradigms 2. Application of three paradigms in database design 3. Database management system transactions 4. Four features of database ACID 5. Application of four database features Solution 7. Implementation and analysis of sub-database and sub-table
Database basics: 1. Introduction to database concepts 2. MySQL installation and login 3. Database creation and deletion 4. Table structure creation/view 5. Field types and data types 6. Field addition, renaming, deletion
Views and indexes: 1. Add, modify, delete records 2. Table query 3. Conditional query 4. Fuzzy query 5. View creation and operation 6. Index creation and operation
The third stage (document database MongoDB)
First knowledge and deployment of Mongodb: 1. Introduction to Mongodb 2. Application scenarios of Mongodb 3. Rapid deployment of Mongodb 4. Mongodb configuration guide
Mongodb basic operations: 1. Mongodb database operations 2, Mongodb collection operations 3, Mongodb document operations
Mongodb advanced operations: 1, Mongodb stored procedure 2, Mongodb aggregation pipeline 3, Mongodb batch write 4, Mongodb MapReduce
Mongodb operation and maintenance and programming: 1. Mongodb data import/export/backup/restore2, Mongodb security3, Mongodb permission control4, Mongodb package introduction5, Mongodb Python API
The fourth stage (in-memory database Redis)
Redis lecture, Redis operation, Redis programming
The fifth stage (web crawler)
urllib.lib library, requests library, css selector and Xpath, advanced crawler technology
Stage 6 (Data Analysis)
Data analysis tool explanation, numerical calculation package learning, data processing package PandasPandas and database
Stage 7 (Data Processing)
Data cleaning and preparation, data processing: merge and reshape, data aggregation and group operations
The eighth stage (advanced data analysis and processing)
Matplotlib Practice, Plotting and Visualization, Statistics Fundamentals, Time Series Analysis Fundamentals
The ninth stage (project combat)
Personal user credit evaluation, operator data statistical analysis, e-commerce website evaluation sentiment analysis, stock data fitting and recommendation
What are the job prospects for Big Data Analysts?
从20世纪90年代起，欧美国家开始大量培养数据分析师，直到现在，对数据分析师的需求仍然长盛不衰，而且还有扩展之势。
根据美国劳工部预测，到2018年，数据分析师的需求量将增长20%。就算你不是数据分析师，但数据分析技能也是未来必不可少的工作技能之一。在数据分析行业发展成熟的国家，90%的市场决策和经营决策都是通过数据分析研究确定的。
大数据分析师薪资待遇：
有媒体报道，在美国，大数据分析师平均每年薪酬高达17.5万美元，而国内顶尖互联网公司，大数据分析师的薪酬可能要比同一个级别的其他职位高20%至30%，且颇受企业重视。
国内某大型招聘平台给出的数据分析师平均薪酬为：9724K（取自 1139 份样本），且北京、上海、广州、深圳、杭州、南京、武汉、成都、长沙为大数据分析师需求量前十的城市。
加米谷大数据整理了几份不同平台给出的大数据分析师薪资水平，以及经验要求，用图表的形式直观表现出来，希望能够帮助大家。
