Ryangineer

Data Science Path & Life Cycle

  Data Science   ⇒   practice of transforming raw, challenging data into actionable insights; foreseen by Isaac Asimov in his Foundation series

  Data Analytics   ⇒   application of data science skills in the business world; the science of extracting trends, patterns & useful information from datasets to increse profits, better utilize resources & improve managerial operations

Planning | Discovery & Business Understanding

• Define goals

• Organize resources

• Coordinate people

• Schedule project

Wrangling | Data Acquisition, Cleaning & Exploration

• Get data

• Clean data

• Explore data

• Refine data

Modeling | Predictive Modeling, Feature Engineering & Machine Learning

• Create model

• Validate model

• Evaluate model

• Refine model

Applying | Data Visualization, Reporting & Decision Making

• Present model

• Deploy model

• Revise model

• Archive assets

Data Science as Business Intelligence

The application of the Scientific Method to make decisions rather than intuition, anecdotal evidence, gut instinct, executive opinions or coin flip

Scientific Method

• Ask a question

• Form a hypothesis

• Design an experiment

• Collect data

• Analyze the data

• Draw a conclusion

• Take action

Justification

• Evidence-based

• Objectively measurable

• Reproducible

• Transparent

Data Analytics Mathematics

Regression Analysis

An understandable equation to predict a single outcome based on multiple predictor variables

• Multiple Linear Regression

  $y_i \; =\; {\beta }_0 \; +\; {\beta }_1{x}_1{\mathrm{}}_i \; +\; {\beta }_2{x}_2{\mathrm{}}_i \; +\; {\beta }_3{x}_3{\mathrm{}}_i \; +\; {\epsilon }_i$

• Linear Regression with Linear Algebra

  • Matrix-Vector Multiplication Function

    $\left[\begin{array}{c}{y}_1\\ y_2\end{array}\right] \; =\; \left[\begin{array}{cccc}1& x_{11}& x_{12}& x_{13}\\ 1& x_{21}& x_{22}& x_{23}\end{array}\right]\left[\begin{array}{c}{\beta }_0\\ {\beta }_1\\ {\beta }_2\\ {\beta }_3\end{array}\right] \; +\; \left[\begin{array}{c}{\epsilon }_1\\ {\epsilon }_2\end{array}\right]$
      ↳ where:
          $\mathrm{Vector\; of\; Outcomes\; for\; Each\; Case} \; =\; \left[\begin{array}{c}{y}_1\\ y_2\end{array}\right]$
          $\mathrm{Matrix\; of\; Scores\; for\; Each\; Case} \; =\; \left[\begin{array}{cccc}1& x_{11}& x_{12}& x_{13}\\ 1& x_{21}& x_{22}& x_{23}\end{array}\right]$
          $\mathrm{Vector\; of\; Regression\; Coefficients\; (y-intercept\; \&\; slopes)} \; =\; \left[\begin{array}{c}{\beta }_0\\ {\beta }_1\\ {\beta }_2\\ {\beta }_3\end{array}\right]$
          $\mathrm{Vector\; of\; Error\; Terms} \; =\; \left[\begin{array}{c}{\epsilon }_1\\ {\epsilon }_2\end{array}\right]$

Data Acquisition

Structured Query Language (SQL)

• Basic Anatomy & Physiology of SELECT Query

  1. Operation:  Describes what is going to be done with SELECT keyword followed by the names of columns to combine with functions.

  2. Data:  FROM keyword followed by one ore more tables connected together with reserved keywords, indicating what data should be scanned for filtering, selection & calculation.

  3. Conditional:  Filters the data to only rows that meet a condition, usually indicated by WHERE keyword.

  4. Grouping:  Special clause that takes rows of a data source, assembles them together using a key created by a GROUP BY clause, then calculates a value using the values from all rows with the same value.

  5. Post-processing:  Takes the results of the data & formats them by sorting & limiting the data using keywords such as ORDER BY & LIMIT.

Brilliant Tutorial Resources

• A Curious Moon from Big Machine

  • Rockets. Moons. PostgreSQL. What else do you need?!

About Ryan L Buchanan

I am re-skilling as a Data Analyst & Machine Learning Engineer.  I am currently enrolled in a Masters in Data Analytics.  I am also acquiring certifications as an ML Engineer & Algorithmic Trader from Udacity.   I have an MBA & an MS in Instructional Design.

I have a multi-displinary background including military intelligence, psychology, linguistics, economics, virtual reality & educational technology.  I have worked abroad for ten years with military, universities & vocational schools.   I have working knowledge of Arabic, Chinese & French.  I am very mobile, able to relocate quickly, adapt easily to diverse working conditions & have a current passport.

I have a passion for mathematics, statistics & artificial intelligence.  I am enthusiastic, highly self-motivated & enjoy presenting informative data to decision makers.  I am eager to work with dynamic teams to create high quality products & services.