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上周和亚马逊Advertisting 的同事聊了一下,她很热心的分享了他们招人的tech bar, 包括Marketing Science 和 Data Science 两部分。这个list 很全面,但面试的时候只是选几个自己的强项考核。希望对大家有帮助,求大米:). .и
. 1point 3acres
1. Marketing Science Methodologies
a) Basics of Descriptive Analytics:
i. Measures of central tendency (mean, median, mode).google и
ii. Measures of spread (range, standard deviation)
iii. Quantiles and inter-quantile distance
b) Basics of Probability:. .и
i. Calculate probability of union and intersection of multiple of events
ii. Ability to use Bayes Theorem
iii. Know the difference between independent and un-correlated random variables
iv. Simple combinatorics, e.g., probability of 4 aces in deck of cards
c) Basic Probability Distributions:
i. What is a probability distribution function? (Univariate)
ii. Understanding differences between basic continuous distributions (e.g. uniform, normal, exponential: memoryless property).
iii. Understanding basic discrete distributions (e.g. Bernoulli or coin toss)
iv. What is mean/variance?. 1point3acres.com
d) Basics of Estimation and Hypothesis Testing:
i. Recognize difference between population parameters versus sample statistic. 1point3acres
ii. Ideas behind simple Hypothesis Testing and its relation to Confidence Intervals
iii. Type 1 error, Type II error
iv. P-values, statistical significance.google и
v. Basics of A/B testing.
e) Basics of Regression:
i. Concepts, and terminology
ii. Simple linear regression model, assumptions, Ordinary Least Squares (OLS)
f) Basics of Forecasting:
iii. Moving Averages
iv. Serial correlation and seasonality ..
2. Data Science Disciplines
. Χ
a) Data Analysis:
i. Best practices for displaying data
ii. Boxplots
iii. Scatterplots
iv. Contingency tables (2x2 tables)
v. Simpson’s paradox
vi. Regression to the mean-baidu 1point3acres
vii. Data manipulation questions (e.g., wide to long format conversions, creation of graphs that require non-trivial data manipulation)
viii. QQ-plots
ix. Histograms
x. Dimension reduction techniques (e.g., PCA)
b) Estimation and Hypothesis Testing:
i. Frequentist sampling concepts
ii. Simple random sample (SRS) definition
iii. Properties of estimators, bias and variance
iv. Power
v. Confidence interval interpretation
vi. Multiple testing issues (familywise error, Bonferroni, FDR)
vii. One-sample versus two-sample hypothesis testing, paired versus unpaired data tests
viii. Test for proportions versus tests for population mean (tweak).google и
ix. Chi-squared tests for contingency tables
x. Basics of maximum likelihood estimation
xi. Basic bootstrap methods to obtain confidence intervals (e.g., percentile method)
xii. Tests for equal variance
xiii. ANOVA concepts
xiv. Likelihood ratio test
c) Regression:
i. The concept of Best Linear Unbiased Estimator (BLUE)
ii. Statistical properties of least squares estimates (model assumptions). 1point 3acres
iii. Residual analysis (error term diagnostics)
iv. Confidence intervals vs prediction intervals
v. Data transformations (log, square root, Box-Cox). 1point 3acres
vi. Multiple linear regression model; interpretation of multiple regression coefficients
.--vii. Interaction effects (mechanics and interpretation). Χ
viii. Regression diagnostics. Χ
ix. Multicollinearity
x. Hypothesis tests in regression modeling
xi. Stepwise variable selection methods and their associated problems
xii. Logistic regression
xiii. Weighted least squares
xiv. Regression methods for repeated measures or longitudinal data (fixed effects versus random effects models)
xv. Fixed effects estimator
xvi. Random effects estimator along with Shrinkage concepts
d) Experiments:
i. Knowledge of different types of bias (selection, nonresponse, misreporting)
ii. Correlation vs causation
iii. Benefits of randomization
iv. Multiple testing issues
e) Basic Machine Learning:
i. Supervised vs unsupervised learning
ii. Bias-variance tradeoff
iii. Cross-validation. From 1point 3acres bbs
iv. Use of logistic regression for classification problems
v. Confusion matrix, precision, recall, F1 score, AUC
vi. Basics of text data mining (bag-of-words data structure)
vii. Boosting
viii. Decision Trees, Random forest (bagging)
ix. Regularization methods
x. K-means (k-medians); Distance Measures. 1point3acres.com
xi. Support vector machines
xii. Convexity
xiii. K-nearest neighbors
xiv. Different loss functions
f) Causal Inference:
i. Randomized control trials
ii. What is a treatment effect? SATE, SATT, SATC, selection problem
iii. What is endogeneity and您好! 本帖隐藏的内容需要积分高于 188 才可浏览 您当前积分为 0。 使用VIP即刻解锁阅读权限或查看其他获取积分的方式 游客,您好! 本帖隐藏的内容需要积分高于 188 才可浏览 您当前积分为 0。 VIP即刻解锁阅读权限 或 查看其他获取积分的方式 ching
ii. Look-alike modelling
iii. Programmatic advertising
iv. Search engine optimization methods
h) Market Structure:. ----
i. Types of Market Structure (e.g. Perfect competition, Monopoly, Oligopoly, Monopolistic competition, Contestable markets)-baidu 1point3acres
ii. Market Concentration Measures (e.g. N-firm concentration ratio, Herfindahl-Hirschman Index)
iii. Market Analysis Dimensions (e.g. Market size, Market trends, Market growth rate, Market profitability, Industry cost structure, Distribution channels)
iv. Market Research Techniques (e.g. Secondary Data Sources, Unstructured Research, Surveys)
i) Online Content:
i. User-generated or professional content: natural language processing (volume, sentiment and topic analysis)
ii. Representativeness of online content (e.g. customer reviews): self-selection, extremes bias, J-shaped star ratings
iii. Influence on brand reputation plus as input to media planning
j) Product launch:
i. Product innovation, quality improvements and differences in customer vs manufacturer perception of these terms
ii. New product diffusion at aggregate (diffusion model) and audience segmentation level (innovators, early adopters…)
iii. Drivers of successful new product launch: roles of customer information, perceived risk, product price and advertising. .и
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