Data Science Foundation: Fundamentals Guide
Questions Graded A+ 2025/2026
.
Python & R - Programming languages for data manipulation and modeling
C, C++, Java - General purpose languages for back end and maximum speed. (JSON)
SQL - Language for relational database queries and manipulations
TensorFlow - Open source library used for deep learning. Deep learning neural
networks
Forms of mathematics - Probability, linear algebra, calculus and regression.
You can choose the procedures to judge the fit between your questions and your data
and your procedure.
Diagnose Problems: know what to do when it fails or gives you impossible results.
Substantive Expertise - Each domain has its own goals, methods, constraints.
What constitutes value
How to implement insights.
Data science pathway - 1) Planning: define goals; organize resources (right computers,
software), coordinate people; schedule the project.
2) Wrangling: get data; clean data (fits into the program); explore (visualizations); Refine
data
3) Modeling: create the statistical model; validate it; evaluate the model; refine the
model.
4) Applying: presenting the model; deploy the model; revisit the model (how well is it
performing); archive the assets.
Data engineer - Developers, architects
Focus on hardware and software
Machine Learning Specialits - Extensive work in computer science and mathematics.
Deep Learning
Artificial Intelligence
Researcher - Focus on domain-specific research.
Physics and genetics r common
More statistical expertise.
Analyst - Day to day data tasks
Web analytics, SQL, visualizations.
Good for business decision-making.
, Project Managers - Manage the project
Big Picture: frame business relevant questions
Must "speak data" - may not be able to do.
A data science manager oversees the entire project and helps place it in a business
context.
Entrepreneur - Data Based startups
Often need all skills, including business
Creativity in planning and execution.
The first step in the data science pathway is "define goals." Why is the best place to
start a data science project? - Clarifying your project's goals up front will help you at
every step of the project pathway, from framing questions, to choosing data and
algorithms, and interpreting and applying your results.
Feedback
Goals influence every step of the data science pathway, from planning to wrangling to
modeling to applying.
What is one of the rare qualities that creates such a high demand for data scientists? -
the ability to find order, meaning, and value in unstructured data
Feedback
Data scientists are valuable because they are able to find value in unstructured data,
but they're also able to predict outcomes and automate processes.
Artificial Intelligence - Algorithms that learn from data; broadly: machine learning.
Strong or General AI: a replica of the human brain that can solve any cognitive task.
Weak or Narrow AI: algorithms that focus on specific well-defined tasks.
You can't do AI without data science
Data Science - The skills and techniques for dealing with challenging data. Not mutually
exclusive from AI
You can do Data Science without AI, machine learning or big data, or predictive
analytics or prescriptive.
Machine Learning - The ability of algorithms to learn from data and improve their
function in the future.
Memorization is easy; spotting patterns is hard; new situations are challenging.
Machine learning really can't be done without data science. Sub discipline of data
science.
Neural Networks - Growth in machine learning. Takes information for processing - it
approximates how the human brain works. Computing power and raw data has
exploded.
Tiny steps with data leads to amazing analytics results.
Inference: need to infer how it is functioning.
Questions Graded A+ 2025/2026
.
Python & R - Programming languages for data manipulation and modeling
C, C++, Java - General purpose languages for back end and maximum speed. (JSON)
SQL - Language for relational database queries and manipulations
TensorFlow - Open source library used for deep learning. Deep learning neural
networks
Forms of mathematics - Probability, linear algebra, calculus and regression.
You can choose the procedures to judge the fit between your questions and your data
and your procedure.
Diagnose Problems: know what to do when it fails or gives you impossible results.
Substantive Expertise - Each domain has its own goals, methods, constraints.
What constitutes value
How to implement insights.
Data science pathway - 1) Planning: define goals; organize resources (right computers,
software), coordinate people; schedule the project.
2) Wrangling: get data; clean data (fits into the program); explore (visualizations); Refine
data
3) Modeling: create the statistical model; validate it; evaluate the model; refine the
model.
4) Applying: presenting the model; deploy the model; revisit the model (how well is it
performing); archive the assets.
Data engineer - Developers, architects
Focus on hardware and software
Machine Learning Specialits - Extensive work in computer science and mathematics.
Deep Learning
Artificial Intelligence
Researcher - Focus on domain-specific research.
Physics and genetics r common
More statistical expertise.
Analyst - Day to day data tasks
Web analytics, SQL, visualizations.
Good for business decision-making.
, Project Managers - Manage the project
Big Picture: frame business relevant questions
Must "speak data" - may not be able to do.
A data science manager oversees the entire project and helps place it in a business
context.
Entrepreneur - Data Based startups
Often need all skills, including business
Creativity in planning and execution.
The first step in the data science pathway is "define goals." Why is the best place to
start a data science project? - Clarifying your project's goals up front will help you at
every step of the project pathway, from framing questions, to choosing data and
algorithms, and interpreting and applying your results.
Feedback
Goals influence every step of the data science pathway, from planning to wrangling to
modeling to applying.
What is one of the rare qualities that creates such a high demand for data scientists? -
the ability to find order, meaning, and value in unstructured data
Feedback
Data scientists are valuable because they are able to find value in unstructured data,
but they're also able to predict outcomes and automate processes.
Artificial Intelligence - Algorithms that learn from data; broadly: machine learning.
Strong or General AI: a replica of the human brain that can solve any cognitive task.
Weak or Narrow AI: algorithms that focus on specific well-defined tasks.
You can't do AI without data science
Data Science - The skills and techniques for dealing with challenging data. Not mutually
exclusive from AI
You can do Data Science without AI, machine learning or big data, or predictive
analytics or prescriptive.
Machine Learning - The ability of algorithms to learn from data and improve their
function in the future.
Memorization is easy; spotting patterns is hard; new situations are challenging.
Machine learning really can't be done without data science. Sub discipline of data
science.
Neural Networks - Growth in machine learning. Takes information for processing - it
approximates how the human brain works. Computing power and raw data has
exploded.
Tiny steps with data leads to amazing analytics results.
Inference: need to infer how it is functioning.
