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Problem-solving tools offered by digital technology

February 15, 2023

Simplify complex modern life with problem-solving tools. Digital tech created an abundance of tools, but a simple set can solve everything.

In last week’s post, DS-Dojo introduced our readers to this blog series’ three focus areas, namely:

1) software development

2) project management

3) data science.

This week, we continue that metaphorical (learning) journey with a fun fact. Better yet, a riddle. What do ALL jobs have in common?

One can (correctly) argue that essentially, all jobs require the worker in question to accomplish one basic or vital goal: solve (a) problem(s). Indeed, one can earnestly argue that the three interdisciplinary fields of this series (software development, project management, and data science) are iconic vis-a-vis their problem-solving characteristics. 

Advanced problem-solving tools for a (post-) modern world

One of the paradoxes of our (post-)modern era is this fact: our lives have become so much easier, safer, and much more enjoyable, thanks to digital technology. And yet simultaneously, our lives have gotten so complicated, with an overwhelming glut of technological tools at our disposal. 

And I suppose one can view this as a “rich person-problem,” akin to a kid in a candy store, indeed. In any case, here is the good news: “As luck would have it,” we can utilize a simple (set of) tool(s), with which we can both solve problems expansively and/or simplify our lives as needed. 

To the rescue (!): Google, checklists, algorithms and data structures, and project management

Incidentally, a Google search using search terms related to the topic at hand suggests a consensus vis-a-vis best practices for solving problems, and/or simplifying our lives.

Ultimately, we can use two or three vital tools: 1) [either] a simple checklist, 2) [or,] the interdisciplinary field of project management, and 3) algorithms and data structures.

Here’s a fun question for you, dear reader: can you think of a tool that can simplify both simple and complex tasks such as i) grocery shopping, ii) surgery, and iii) safely flying an airplane? If you answered, “a checklist,” you’re correct. 

But for more complicated problems, the interdisciplinary field of project management might be useful–i.e., via the 12 (project-management) elements introduced in last week’s post. To recap, those twelve elements (e.g. as defined by Belinda Goodrich, 2021) are: 

  • Project life cycle, 
  • Integration, 
  • Scope, 
  • Schedule, 
  • Cost, 
  • Quality, 
  • Resources, 
  • Communications, 
  • Risk, 
  • Procurement, 
  • Stakeholders, and 
  • Professional responsibility/ethics. 

In addition to the mindful use of the above twelve elements, our Google search might reveal that various authors suggest some vital algorithms for data science. For instance, in the table below, we juxtapose four authors’ professional opinions with DS-Dojo’s curriculum.

What problem-solving tools next digital age have to offer 

Thanks to Moore’s law (e.g., as described via the relevant Wikipedia article about Moore’s law and other factors, the digital age will keep producing hardware and software tools that are both wondrous and/or overwhelming (e.g., IoT, Web 3.0, metaverse, quantum-computing, etc.).

In this blog post, DS-Dojo provides a potential remedy to our readers vis-a-vis finding easier solutions to our world’s problems, and the avoidance of that “spoilt for choice” dilemma.

By using checklists and tools derived from the three interdisciplinary fields of this blog series, we can solve our world’s ever-growing/evolving problems, and/or simplify our lives as needed.

 

Sample Overview of Data-Science Dojo’s Curriculum:

  • Weeks 1 to 3: Introduction to Quantitative Data Analysis
  • Weeks 4 to 8: Classification
  • Week 9: Applications of Classification
  • Week 10: Special Topic: Text Analysis Fundamentals
  • Week 11: Unsupervised Learning
  • Weeks 12 and 13: Regression
  • Weeks 14 to 16: More Applications of Previously-Learned Concepts
VS.
Tech-Vidvan’s 

“Top 10”:

  1. Linear Regression
  2. Logistic Regression
  3. Decision Trees
  4. Naive Bayes
  5. K-Nearest Neighbors
  6. Support Vector Machine
  7. K-Means Clustering
  8. Principal Component Analysis
  9. Neural Networks
  10. Random Forests
P. Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2

1) Big O Notation

2) Search

3) Sort

3)–i)–Quicksort

3)–ii–Mergesort

4) Stack

5) Queue 

6) Array

7) Hash Table

8) Graph

9) Tree (e.g., Decision Tree)

10) Breadth-First Search

11) Depth-First Search

12) Dijkstra’s Algorithm

Disha Ganguli’s Top 10

  1. Linear Regression  
  2. Logistic Regression  
  3. Decision Trees  
  4. ID3 Algorithm  
  5. Cart Algorithm  
  6. Naïve Bayes  
  7. K-nearest neighbors (KNN) 
  8. Support vector machine (SVM) 
  9. K-means clustering 
  10. PCA Algorithm
Data-Quest’s Top 10:

5 Supervised Learning Techniques: 

1) Linear Regression 

2) Logistic Regression

3) CART 

4) Naïve Bayes 

5) KNN

3 Unsupervised Learning Techniques

6) Apriori

7) K-means 

8) PCA

2 Ensembling Techniques

9) Bagging with Random Forests 

10) Boosting with XGBoost.

Written by Seif Sekalala
Data Science Dojo | data science for everyone

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