[Book Summary - CtDSI] Cracking the Data Science Interview Ch. 1

 

1. What is Data Science?
   • definition: deriving insights from data. these insights are then used to provide value to a business
   • main factors
     • explosion of data (like MapReduce)
     • technological advances (like GPUs)
     • success stories
2. Data Science ≠ Machine Learning
   • Machine learning is based on the idea that algorithms can identify and learn patterns from data and make decisions with minimal human intervention
   • two diciplines that provide suitable tools to help solve data science problems
     • Operations Research: problem-solving techniques ranging from mathematical modeling and stochastic processes to mathematical optimization to simulation in order to improve decision-making
     • Statistics: for collecting, analyzing, interpreting and presenting empirical data
   • The most important for a data scientist to have a solid foundation in exploratory data analysis, data visualization, probability and statistics, optimization, mathematical modeling and computer science!
3. What Makes a Good Data Scientist?
   • identify the relevant questions
   • acquire and clean the right data
   • analyze that data to obtain results
   • clearly communicate the findings
   • conver the results into solutions
4. The Data Science Process Workflow
   • Specify Objective ⇒ Data Acquisition ⇒ Explore the Data ⇒ Establish a Baseline ⇒ Model the Data ⇒ Analyze Results ⇒ Communicate Findings ⇒ Iterate
5. Data Science Deliverables: Prediction, Forecasts, Anomaly Detection, Recognition, Optimization, Segmentation, Recommendations, etc.
6. Writing a Great Data Science Resume
   • One Page
   • Relevant Coursework
   • Relevant Skills
   • Relevants Projects: try not to include the common projects everyone has worked on
   • Relevant Experience: relevant experience and employment history with impactful bullet points
   • Include Accomplishments (put NUMBERS into your resume!): specific impact, business impact, competition ranking etc.
   • Customize: customize your resume to specific jobs
7. Data Science Interview Topics
   • Probability & Statistics: Conditional probability (Bayes’ Theorem), Probability Distributions, Hypothesis Testing, Covariance and correlation
   • Computer Science: Coding (Python or R), Data Structures (Lists, Hash Tables, Stacks, Queues, Treesm Graphs), Algorithms (searching, sorting, graph traversals), Databases (SQL, NoSQL), Distributed Computing (MapReduce, Spark, Hadoop)
   • Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, General Predictive Modeling (choosing the right evaluation metrics, train and test sets, cross-validation)
   • Data Engieering: Data Wrangling, Cleaning and Visualization, Feature Engineering
   • Doman Knowledge: depneding on the company and industry
   • Behavioral and “Fit” Questions
8. Data Science Interview Process
   • Coding Challenge ⇒ HR Screen (asking you behavioral questions), Technical Screen (questions ranging from computer science to machine learning to statistics) ⇒ Take Home Project (testing your coding, analytical, and communication skills - Be aware of the target audience!) ⇒ Onsite ⇒ Offer & Negotiation
9. Behavioral & Fit Questions: Teamwork, Ability to Adapt, Communication using the STAR (Situation, Task, Action, Result) method

[Paper Review - Psycholinguistics] Syntactic Categories in the Speech of Young Children (Valian, 1986)

Syntactic categories in the speech of young children

Valian (1986)

 2;0, 2;5 MLU 2.93-4.14 

• Determiner, adjective, noun, noun phrase, preposition, & prepositional phrase 
• Showed evidence all categories 
• Children are sensitive very early in life to abstract, formal properties of the speech / syntactic knowledge at an earlier point  

1. Semantic viewpoint: treatments of semantic roles presuppose a phrasal segmentation of the sentence (e.g., NP -> agent) 
2. Syntactic viewpoint: most treatments of grammatical relations define such relations over syntactic categories. 
3. Developmental viewpoint: category acquisition puts temporal constraints on theories of how syntactic knowledge is acquired. 

• Subjects: 6 children (2;0-2;5), MLU 2.93-4.14, utterances 52-689 

• Recording & transcribing: tape-recorded & transcribed by the observer 
  

• Utterances: by intonation and syntax 
• Preliminary category assignment: the child's error of not knowing which category a word belonged to would be misdescribed as the child's having deficient understanding of how adjectives pattern.  

• Procedures used to test category assignment 

• What expressions another expression can precede and follow 
• Use of the single-word or single-expression "substitutability" test 
• Multiple-appearance test: a category should show up in all its existing syntactic variations in each location where it is allowed 
• Subcategories method: the main method used here is the restriction of different words to different subclasses (e.g., the restriction of a to singular Ns.)  
• Specific category criteria