One can (correctly) argue that essentially, all jobs require the worker in question to accomplish one basic or vital goal: solve (a) problem(s). And 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,
- 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 has 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:
|P. Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2
1) Big O Notation
7) Hash Table
9) Tree (e.g., Decision Tree)
10) Breadth-First Search
11) Depth-First Search
12) Dijkstra’s Algorithm
|Disha Ganguli’s Top 10
||Data-Quest’s Top 10:
5 Supervised Learning Techniques:
1) Linear Regression
2) Logistic Regression
4) Naïve Bayes
3 Unsupervised Learning Techniques
2 Ensembling Techniques
9) Bagging with Random Forests
10) Boosting with XGBoost.