Data Analyst Job Profile

What is a Data Analyst?

The Data Analyst examines data from multiple disparate sources with the goal of providing security and privacy insight. Designs and implements custom algorithms, workflow processes, and layouts for complex, enterprise-scale data sets used for modeling, data mining, and research purposes.

Data Analyst must know:

  • computer networking concepts and protocols, and network security methodologies.
  • risk management processes (e.g., methods for assessing and mitigating risk).
  • laws, regulations, policies, and ethics as they relate to cybersecurity and privacy.
  • cybersecurity and privacy principles.
  • cyber threats and vulnerabilities.
  • specific operational impacts of cybersecurity lapses.
  • computer algorithms.
  • computer programming principles
  • data administration and data standardization policies.
  • data mining and data warehousing principles.
  • database management systems, query languages, table relationships, and views.
  • digital rights management.
  • enterprise messaging systems and associated software.
  • low-level computer languages (e.g., assembly languages).
  • mathematics (e.g. logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis).
  • network access, identity, and access management (e.g., public key infrastructure, Oauth, OpenID, SAML, SPML).
  • operating systems.
  • policy-based and risk adaptive access controls.
  • programming language structures and logic.
  • query languages such as SQL (structured query language).
  • sources, characteristics, and uses of the organization’s data assets.
  • the capabilities and functionality associated with various technologies for organizing and managing information (e.g., databases, bookmarking engines).
  • command-line tools (e.g., mkdir, mv, ls, passwd, grep).
  • interpreted and compiled computer languages.
  • secure coding techniques.
  • advanced data remediation security features in databases.
  • database access application programming interfaces (e.g., Java Database Connectivity [JDBC]).
  • applications that can log errors, exceptions, and application faults and logging.
  • how to utilize Hadoop, Java, Python, SQL, Hive, and Pig to explore data.
  • machine learning theory and principles.
  • Information Theory (e.g., source coding, channel coding, algorithm complexity theory, and data compression).
  • database theory.

Key skills of the Data Analyst include:

  • conducting queries and developing algorithms to analyze data structures.
  • creating and utilizing mathematical or statistical models.
  • data mining techniques (e.g., searching file systems) and analysis.
  • developing data dictionaries.
  • developing data models.
  • generating queries and reports.
  • writing code in a currently supported programming language (e.g., Java, C++).
  • using binary analysis tools (e.g., Hexedit, command code xxd, hexdump).
  • one-way hash functions (e.g., Secure Hash Algorithm [SHA], Message Digest Algorithm [MD5]).
  • reading Hexadecimal data.
  • identifying common encoding techniques (e.g., Exclusive Disjunction [XOR], American Standard Code for Information Interchange [ASCII], Unicode, Base64, Uuencode, Uniform Resource Locator [URL] encode).
  • assessing the predictive power and subsequent generalizability of a model.
  • data pre-processing (e.g., imputation, dimensionality reduction, normalization, transformation, extraction, filtering, smoothing).
  • identifying hidden patterns or relationships.
  • performing format conversions to create a standard representation of the data.
  • performing sensitivity analysis.
  • developing machine understandable semantic ontologies.
  • Regression Analysis (e.g., Hierarchical Stepwise,
  • Generalized Linear Model, Ordinary Least Squares, Tree-Based Methods, Logistic).
  • transformation analytics (e.g., aggregation, enrichment, processing).
  • using basic descriptive statistics and techniques (e.g., normality, model distribution, scatter plots).
  • using data analysis tools (e.g., Excel, STATA SAS, SPSS).
  • using data mapping tools.
  • using outlier identification and removal techniques.
  • writing scripts using R, Python, PIG, HIVE, SQL, etc.
  • the use of design modeling (e.g., unified modeling language).
  • identify sources, characteristics, and uses of the organization’s data assets.

Data Analyst must be able to:

  • build complex data structures and high-level programming languages.
  • dissect a problem and examine the interrelationships between data that may appear unrelated.
  • identify basic common coding flaws at a high level.
  • use data visualization tools (e.g., Flare, HighCharts, AmCharts, D3.js, Processing, Google Visualization API, Tableau, Raphael.js).
  • accurately and completely source all data used in intelligence, assessment and/or planning products

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