Designing flexible data representations. Considerations include:

• future advances in data technology
• changes to business requirements
• awareness of current state and how to migrate the design to a future state
• data modeling
• tradeoffs
• distributed systems
• schema design

Designing data pipelines. Considerations include:

• future advances in data technology
• changes to business requirements
• awareness of current state and how to migrate the design to a future state
• data modeling
• tradeoffs
• system availability
• distributed systems
• schema design
• common sources of error (eg. removing selection bias

Designing data processing infrastructure. Considerations include:

• future advances in data technology
• changes to business requirements
• awareness of current state, how to migrate the design to the future state
• data modeling
• tradeoffs
• system availability
• distributed systems
• schema design
• capacity planning
• different types of architectures: message brokers, message queues, middleware, serviceoriented

Building and maintaining flexible data representations
Building and maintaining pipelines. Considerations include:

• data cleansing
• batch and streaming
• transformation
• acquire and import data
• testing and quality control
• Connecting to new data sources

Building and maintaining processing infrastructure. Considerations include:

• provisioning resources
• monitoring pipelines
• adjusting pipelines
• testing and quality control

Analyzing data. Considerations include:

• data collection and labeling
• data visualization
• dimensionality reduction
• data cleaning/normalization
• defining success metrics

Machine learning. Considerations include:

• feature selection/engineering
• algorithm selection
• debugging a model

Machine learning model deployment. Considerations include:

• performance/cost optimization
• online/dynamic learning

Performing quality control. Considerations include:

• verification
• building and running test suites
• pipeline monitoring

Assessing, troubleshooting, and improving data representations and data processing
infrastructure.

Recovering data. Considerations include:

• planning (e.g. fault-tolerance)
• executing (e.g., rerunning failed jobs, performing retrospective re-analysis)
• stress testing data recovery plans and processes

• Applications of Data structures
• Types of Collections
• Sequence
• Strings, List, Tuple, range
• Non sequence
• Set, Frozen set, Dictionary
• Strings
• What is string
• Representation of Strings
• Processing elements using indexing
• Processing elements using Iterators
• Manipulation of String using Indexing and Slicing
• String operators
• Methods of String object
• String Formatting
• String functions
• String Immutability
• Case studies

• What is tuple?
• Different ways of creating Tuple
• Method of Tuple object
• Tuple is Immutable
• Mutable and Immutable elements of Tuple
• Process tuple through Indexing and Slicing
• List v/s Tuple

• Building (or selecting) data visualization and reporting tools. Considerations include:
  automation
• decision support
• data summarization, (e.g, translation up the chain, fidelity, trackability, integrity)

Advocating policies and publishing data and reports.

Designing secure data infrastructure and processes. Considerations include:

• Identity and Access Management (IAM)
• data security
• penetration testing
• Separation of Duties (SoD)
• security control
• Designing for legal compliance. Considerations include:
  legislation (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s
  Online Privacy Protection Act (COPPA), etc.)
• audits

• Arithmetic Operators
• Comparison Operators
• Python Assignment Operators
• Logical Operators
• Bitwise Operators
• Shift operators
• Membership Operators
• Identity Operators
• Ternary Operator
• Operator precedence
• Difference between “is” vs “==”

• Conditional control statements
• If
• If-else
• If-elif-else
• Nested-if
• Loop control statements
• for
• while
• Nested loops
• Branching statements
• Break
• Continue
• Pass
• Return
• Case studies