Welcome To My Journey

Week One

VaR - “Value at Risk”

• Lets’s say 1%, one-year value at risk of 10 million, it means that there is a 1% chance that the portfolio will lose 10 million in one year.

Cauchy Distribution

• dfdf

Central Limit Theorem

• Average of a large amount of independent identically distributed stocks are approximately normally distributed
• Can fail if the underlying stocks are fat tailed
• Can fail if the underlying stocks lose their independence

Covariance

• Covariance between two investments or more is the central to Captial Asset Pricing Model, because you always want to ask for a low covariance.
• Risk is determined by the covariance.

Captial Asset Pricing Model (CAPM)

• Based on Rational market and rational investors
• The CAPM implies that the expected return on the $i_(th)$ asset is determined from its Beta(𝛽).
• Beta(𝛽) is the regression slope coefficient when the return on the $i_(th)$ asset is regressed on the return on the market

沙利文行研的核心 — 沙利文360°行研

区别于传统的市场调研，沙利文运用的是一套自己研发出的360度行业研究方法论，这个方法论将从7个不同的维度进行深度探究，分析其对每一个行业的影响。这七个维度包括：跟踪分析竞争程度（Monitoring and Analyzing Competitive Intensity)、客户动态（Customer Dynamics)、行业融合（Industry Convergence)、颠覆性科技（Disruptive Technologies）、全球大趋势（Global Mega Trends)、新型商业模式（New Business Models）以及新兴市场（Emerging Markets).

沙利文行业研究方法论

一、市场分析 Market Analysis

1. 重点分析

1. 对某个市场或分割市场进行定义

• 目标分割市场是否在制造成本或者分销成本上有巨大的共同成本？
  • 公司本身是否有很多分割市场？
  • 成本优势是否作用于多个分割市场？
• 目标市场分割有没有大量的共同用户？
  • 客户是否会组合购买还是分开购买？（比如是否买主机的人会同时买显示器？）
• 目标市场的区域特征是不是特别明显？
• 目标市场的整个价值链条中，是否存在经济优势或者用户优势？
• 是否存在某些市场分割在目标市场/市场分割的价值链中有绝对的控制地位？（比如垄断资源对某些行业会产生绝对的控制）
• 目标市场是否有可能纵向衍生？（产生新的需求？）

2. 市场大小，增长以及驱动进行分析

• 目标市场有哪些相关市场？
• 相关市场的市场规模有多大？
• 若要驱使目标市场增长，那么需要哪些潜在假设？
  • 需求驱使（比如：生活品质上升，法律变化等等）
  • 供给驱使（比如：竞争加大，新科技等等）
• 如果增长缓慢，那么面临哪些风险？
• 历史增长率与预测增长率对比，有哪些发现？

2.分析成果（报告）

• XX市场规模，市场未来增长趋势预测以及行业天花板
• XX分割市场大小以及变化趋势

1.Identifying the data quality issues and how this may impact our analysis going forward?

Since the company didn’t define its own business definition for data quality evaluation, I will follow the general process.

To evaluate the quality of data, we can follow the Six Standard Data Quality Dimension:

1. Data preparation and customer analytics

Background:

The Manager wants to better understand the types of customers who purchase Chips and their purchasing behaviour within the region.
The insights from analysis will feed into the supermarket’s strategic plan for the chip category in the next half year.

End Goal:

To form a strategy based on the findings to provide a clear recommendation to Manager so make sure the insights can have a commercial application.

Bitcoin analysis for fun.Typical time-series data analysis for reference.

Abstract

　This paper mainly discusses the factors in the market that are going to influence the Bitcoin price. The Bitcoin price formula is derived from the Barro(1979) model which also provides the concept of selecting the market factors in the formula. Since Bitcoin is not a traditional currency, it is not appropriate to use the law of supply and demand to determine its value. By adding some other market factors, the formula will become more theoretically feasible. Time-series analytic mechanism will be applied in this project

1. Write Function

We add a Leap Day on February 29, almost every four years. The leap day is an extra, or intercalary day and we add it to the shortest month of the year, February.
In the Gregorian calendar three criteria must be taken into account to identify leap years:

• The year can be evenly divided by 4, is a leap year, unless:
  • The year can be evenly divided by 100, it is NOT a leap year, unless:
    • The year is also evenly divisible by 400. Then it is a leap year.

This means that in the Gregorian calendar, the years 2000 and 2400 are leap years, while 1800, 1900, 2100, 2200, 2300 and 2500 are NOT leap years.

In 2017, I started my first coding practice by using R. Then, I realize that computer language is not rocketscience that I can’t never understand.

Practice 1

The potential relationship between United States GDP and Some other economic indicators (from year 1960 to 2015)

　The data set pick for first practice is aimed to study the potential relationship between United States GDP and some other economic indicators such as population, exports of goods and service, electric power consumption, unemployment rate and average rate index from year 1960 to 2015. The source of data is from World Bank Open Data ( https://data.worldbank.org) and National Average Wage Index (https://www.ssa.gov/oact/cola/AWI.html.)
In order to find the relationship, I first need to find out the trend of indicators from 1960 to 2015 and make some logical assumptions in mind.

In retrospect my first NLP project,it was a really great learning experience that got me through the process of nature language processing, and piqued my interest to the Data Science.

Abstract

　In the past one year, PUBG became one of the most popular games in the word and at the same time it also received thousands of negative comments and thus I planned to start a project to help those developers and operators extract valuable information from those critics. The purpose of my project is to get suggestions from those critics, and then I plan to assign labels for players manually to classify those clear-minded supporters and critics and then train some classification models to discover more critics with firm stand.Finally, I will propose some future improvements for this project which I could have done better and also as reference for my next project.

Course 1 Introduction to Big Data

Week 1

• In-Situ analytical processing: Bringing the computation to where data is located.

• SCADA(Supervisory Control and Data Acquisition)
  　　It is a type of industrial control system for remote monitoring and control of industrial processes that exists in the physical world, potentially including multiple sites, many types of sensors.In addition to monitoring and control, SCADA system can be used to define actions for reduced waste and improved efficiency in industrial processes,public or private infrastructure processes,facility processes.