Implementing Personal Assistant for Personal Instant Messaging Research Plan
哈佛大学经济学术水准属于世界顶级，长期霸占全美第一，这里有Morton L. and Carole S. Olshan学者，这里还有 Moise Y. Safra学 者······六大商业经济学教授科研组虚以待位，招募科研助理：
Corporate Finance [公司金融]
International Business [国际商务]
Business strategy [商务战略]
Corporate government [公司管理]
Business and government [商业和政法]
How do/should individuals make decisions in this interactive world? Through this research program,students are introduced to the game theoretic perspective and economic activities. More importantly,students will learn to read papers in the frontier of economics and appreciate their strengths and weaknesses. Toward the end of the program,students will be guided to initiate their own reserach on a topic of their choice.Hopefully the research is continued in the future and turned into a paper.
Martingale Capital Investment (MCI) is a Hangzhou based investment firm founded in 2014 with an AUM on the orders of tens of millions. MCI partly inherits its trading strategy from Martingale Asset Management (a Boston based asset management corporation founded in 1987) and partly leverages its own means and intelligence to provide investors with a set of powerful tools suitable for the Chinese trading environment.
MCI looks for intelligent and dedicated individuals who are willing to work long hours on mentally challenging financial conundrums. Its basic trading strategies include statistical arbitrage, low-vol anomaly, constrained mean-variance optimization, and fundamental analysis. It strives to harmoniously combine modern computational advantages with Graham-Buffett School value-based principles to obtain sizeable alpha that delivers satisfactory returns to our investors.
Interns can look forward to participate in any of the following four domains of analysis:
1.Low-vol Anomaly Portfolio Construction [Requires Programming Skills and Basic Financial & Economic Knowledge]
–Deeply understand the behavioral foundation of low-vol anomaly from the perspective of both private and institutional investors;
–Employ volatility-based algorithm to categorize securities into quantiles, compare vol-measures;
–Investigate inter-quantile behavior, compute KPI for each quantile, fine tune categorization parameter, employ non-parametric methodologies to improve analytical results.
–Panel data cross-country comparison of low-vol phenomena.
2.Constrained Mean-Variance Optimization [Requires Knowledge in Programming and Optimization]
–Institutional value-based investment strategy involves effective diversification. High dimensional clusters of historical return and risk characteristics have to be projected into accessible dimensional representations. For instance, our May, 2016 portfolio consists of 25 distinct securities in addition to a risk free asset. Identify and locate the so-called “Capital Market Line” is of primary importance in selecting a desirable portfolio with satisfactory risk characteristics.
3.Data Science [Requires Knowledge in Basic Statistics and Programming]
–The Chinese stock market exhibit different characteristics with respect to the US market. As is evident from the illustration on the right of the 25 securities of our August, 2016 portfolio, temporary suspension of public trading along with price volatility constraints (10% in both directions) present challenges in history-based statistical analysis. These market-relevant idiosyncrasies coupled with the non-normality nature of return distribution require analytical efforts. The intern is expected to clean the data-set in a statistically intelligent way, and this part of the intern is certainly a valuable opportunity to apply one’s statistical knowledge for practical applications.
4.Fundamental Analysis [Requires Basic Knowledge in Accounting]
–Though not the most mathematically demanding segment of our enterprise, fundamental analysis is the most skill-demanding and most critical element in our entire portfolio construction process. The intern will learn how to proficiently read financial statements, how to distill important information from hundreds of pages of endless rambling, and how to apply historical financial statement analysis for intelligent and informed decision making.
土木工程专业越来越难申请？GT成绩对于申请成功帮助太低，赶快来做科研项目吧！一个暑假，完成弯道超车。It’s time to challenge yourself！
This summer tutoring research is a summary of several important subjects in the Construction Management Master Program in Columbia University. In this research, students will be introduced to basic concepts in the managerial aspects of construction industry, e.g. construction planning and scheduling processes, procurement/delivery methods, cash flow in construction, risk analysis, information technologies in construction, safety aspects of construction, situational leadership etc. The students will receive abundant exposure to real‐world problems and solution in construction industry, with optional site‐visit opportunities*. Upon completion of this research, students should have comprehensive knowledge about construction management in general, as well as an understanding of innovative industrial practices in America and other countries.
Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing.
Who is this research project for?
- If you consistently hear the buzz word ‘data science’ and want to know what’s it about.
- If you are into latest technology and wonder what’s the secret of the success of Google, Facebook, and LinkedIn.
- If you are a college student majoring in Statistics, Econ, Finance, Computer Science and want to pursue a degree in Data Science in the top universities.
- If you want to dive into and be successful in the career of Data Science.
What are you going to learn?
Step 1: The Data Scientist’s Toolbox
In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, Python, R, Spark and Hadoop.
Step 2: Python Programming – Intro
In this course you will learn how to program in Python and how to use Python for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in Python, reading data into Python, accessing Python packages, writing Python functions, debug
Step 3: Python Programming – Packages and Application
In this course you will learn how to program in Python and how to use Python for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in Python, reading data into Python, accessing Python packages, writing Python functions, debugging, profiling Python code, and organizing and commenting Python code. Topics in statistical data analysis will provide working examples.
Step 4: Getting and Cleaning Data
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Step 5: Applied Data Mining
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Step 6: Applied Machine Learning
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Step 7: Data Visualization