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Beyond technical expertise: 10 soft skills to elevate your data science career
Ruhma Khawaja
| March 29, 2023

As a data scientist, it’s easy to get caught up in the technical aspects of your job: crunching numbers, building models, and analyzing data. However, there’s one aspect of your job that is just as important, if not more so: soft skills. 

Soft skills are the personal attributes and abilities that allow you to effectively communicate and collaborate with others. They include things like communication, teamwork, problem-solving, time management, and critical thinking. While these skills may not be directly related to data science, they are essential for data scientists to be successful in their roles. 

Data science success: Top 10 soft skills you need to master

The human aspect is crucial in data science, not just the technical side represented by algorithms and models. In this blog, you will learn about the top 10 essential interpersonal skills needed for professional success in the field of data science.

10 soft skills to thrive as a data scientist
10 soft skills to thrive as a data scientist – Data Science Dojo

1. Communication 

The ability to effectively communicate with clients, stakeholders, and team members is essential for data science professionals working in professional services. This includes the ability to clearly explain complex technical concepts, present data findings in a way that is easy to understand and to respond to client questions and concerns. 

One of the biggest reasons why soft skills are important for data scientists is that they allow you to effectively communicate with non-technical stakeholders. Many data scientists tend to speak in technical jargon and use complex mathematical concepts, which can be difficult for non-technical people to understand. Having strong communication skills allows you to explain your findings and recommendations in a way that is easy for others to understand. 

2. Problem-solving 

Data science professionals are often called upon to solve complex problems that require critical thinking and creativity. The ability to think outside the box and come up with innovative solutions to problems is essential for success in professional services. 

Problem-solving skills in data scientist are crucial as it allows data scientists to analyze and interpret data, identify patterns and trends, and make informed decisions. Data scientists are often faced with complex problems that require creative solutions, and strong problem-solving skills are essential for coming up with effective solutions. 

3. Time management 

Data science projects can be complex and time-consuming, and professionals working in professional services need to be able to manage their time effectively to meet deadlines. This includes the ability to prioritize tasks and to work independently. 

4. Project management 

Effective project management is a crucial skill for data scientists to thrive in professional services. They must be adept at planning and organizing project tasks, delegating responsibilities, and overseeing the work of other team members from start to finish. The ability to manage projects efficiently can ensure the timely delivery of quality work, boost team morale, and establish a reputation for reliability and excellence in the field.

5. Collaboration 

Next up on the soft skills list is collaboration. Data science professionals working in professional services often work in teams and need to be able to collaborate effectively with others. This includes the ability to work well with people from diverse backgrounds, to share ideas and knowledge, and to provide constructive feedback. 

6. Adaptability 

Data science professionals working in professional services need to be able to adapt to changing client needs and project requirements. This includes the ability to be flexible and to adapt to new technologies and methodologies. 

Moreover, adaptability is an important skill for data scientists because the field is constantly evolving, and techniques are being developed all the time. Being able to adapt to these changes and learn new tools and methods is crucial for staying current in the field and being able to tackle new challenges. Additionally, data science projects often have unique and changing requirements, so being able to adapt and find new approaches to problems is essential for success. 

7. Leadership 

Data science professionals working in professional services often need to take on leadership roles within their teams. This includes the ability to inspire and motivate others, to make decisions, and to lead by example. 

Leadership is an important skill for data scientists because they often work on teams and may need to coordinate and lead other team members. Additionally, data science projects often have a significant impact on an organization, and data scientists may need to be able to effectively communicate their findings and recommendations to stakeholders, including senior management.

Leadership skills can also be useful in guiding a team towards a shared goal, making sure all members understand and support the project’s objectives, and making sure that the team is working effectively and efficiently. Furthermore, Data Scientists are often responsible for not only analyzing the data but also communicating the insights and results to different stakeholders, which is a leadership skill. 

8. Presentation skills 

Data science professionals working in professional services need to be able to present their findings and insights to clients and stakeholders in a clear and engaging way. This includes the ability to create compelling visualizations and to deliver effective presentations. 

9. Cultural awareness 

Data science professionals working in professional services may work with clients from diverse cultural backgrounds. The ability to understand and respect cultural differences is essential for building strong relationships with clients. 

10. Emotional intelligence 

Data science professionals working in professional services need to be able to understand and manage their own emotions, as well as the emotions of others. This includes the ability to manage stress and maintain a positive attitude even in the face of challenges. 

Bottom line 

In conclusion, data science professionals working in professional services need to have a combination of technical and soft skills to be successful. The ability to communicate effectively, solve problems, manage time and projects, collaborate with others, adapt to change and emotional intelligence are all key soft skills that are necessary for success in the field.

By developing and honing these skills, data science professionals can provide valuable insights and contribute to the success of their organizations.  

Problem-solving tools offered by digital technology
Seif Sekalala
| February 13, 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, and 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). 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, 
  • 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 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:

  • 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

“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



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.

Introducing the trio of software development, project management, and data science
Seif Sekalala
| January 24, 2023

In this blog post, the author introduces the new blog series about the titular three main disciplines or knowledge domains of software development, project management, and data science. Amidst the mercurial evolving global digital economy, how can job-seekers harness the lucrative value of those fields–esp. data science, vis-a-vis improving their employability?



To help us launch this blog series, I will gladly divulge two embarrassing truths. These are: 

  1. Despite my marked love of LinkedIn, and despite my decent / above-average levels of general knowledge, I cannot keep up with the ever-changing statistics or news reports vis-a-vis whether–at any given time, the global economy is favorable to job-seekers, or to employers, or is at equilibrium for all parties–i.e., governments, employers, and workers.
  2. Despite having rightfully earned those fancy three letters after my name, as well as a post-graduate certificate from the U. New Mexico & DS-Dojo, I (used to think I) hate math, or I (used to think I) cannot learn math; not even if my life depended on it!



Following my undergraduate years of college algebra and basic discrete math–and despite my hatred of mathematics since 2nd grade (chief culprit: multiplication tables!), I had fallen in love (head-over-heels indeed!) with the interdisciplinary field of research methods. And sure, I had lucked out in my Masters (of Arts in Communication Studies) program, as I only had to take the qualitative methods course.


Data Science Blog Series
A Venn-diagram depicting the disciplines/knowledge-domains of the new blog series.


But our instructor couldn’t really teach us about interpretive methods, ethnography, and qualitative interviewing etc., without at least “touching” on quantitative interviewing/surveys, quantitative data-analysis–e.g. via word counts, content-analysis, etc.

Fast-forward; year: 2012. Place: Drexel University–in Philadelphia, for my Ph.D. program (in Communication, Culture, and Media). This time, I had to face the dreaded mathematics/statistics monster. And I did, but grudgingly.

Let’s just get this over with, I naively thought; after all, besides passing this pesky required pre-qualifying exam course, who needs stats?!


About software development:

Fast-forward again; year: 2020. Place(s): Union, NJ and Wenzhou, Zhejiang Province; Hays, KS; and Philadelphia all over again. Five years after earning the Ph.D., I had to reckon with an unfair job loss, and chaotic seesaw-moves between China and the USA, and Philadelphia and Kansas, etc. 

Thus, one thing led to another, and soon enough, I was practicing algorithms and data-structures, learning about the basic “trouble-trio” of web-development–i.e., HTML, CSS, and JavaScript, etc.! 


Read more about Programming Languages


But like many other folks who try this route, I soon came face-to-face with that oh-so-debilitative monster: self-doubt! No way, I thought. I’m NOT cut out to be a software-engineer! I thus dropped out of the bootcamp I had enrolled in and continued my search for a suitable “plan-B” career.


About project management:

Eventually (around mid/late-2021), I discovered the interdisciplinary field of project management. Simply defined (e.g. by Te Wu, 2020; link), project management is

“A time-limited, purpose-driven, and often unique endeavor to create an outcome, service, product, or deliverable.”

One can also break down the constituent conceptual parts of the field (e.g. as defined by Belinda Goodrich, 2021; link) as: 

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


Ah…yes! I had found my sweet spot, indeed. or, so I thought. 


Hard truths:

Eventually, I experienced a series of events that can be termed “slow-motion epiphanies” and hard truths. Among many, below are three prime examples.


Hard Truth 1: The quantifiability of life:

For instance, among other “random” models: one can generally presume–with about 95% certainty (ahem!)–that most of the phenomena we experience in life can be categorized under three broad classes:


  1. Phenomena we can easily describe and order, using names (nominal variables);
  2. Phenomena we can easily group or measure in discrete and evenly-spaced amounts (ordinal variables);
  3. And phenomena that we can measure more accurately, and which: i)–is characterized by trait number two above, and ii)–has a true 0 (e.g., Wrench et Al; link).


Hard Truth 2: The probabilistic essence of life:

Regardless of our spiritual beliefs, or whether or not we hate math/science, etc., we can safely presume that the universe we live in is more or less a result of probabilistic processes (e.g., Feynman, 2013). 


Hard truth 3: What was that? “Show you the money (!),” you demanded? Sure! But first, show me your quantitative literacy, and critical-thinking skills!

And finally, related to both the above realizations: while it is true indeed that there are no guarantees in life, we can nonetheless safely presume that professionals can improve their marketability by demonstrating their critical-thinking-, as well as quantitative literacy skills.


Bottomline; The value of data science:

Overall, the above three hard truths are prototypical examples of the underlying rationale(s) for this blog series. Each week, DS-Dojo will present our readers with some “food for thought” vis-a-vis how to harness the priceless value of data science and various other software-development and project-management skills / (sub-)topics. 


No, dear reader; please do not be fooled by that “OmG, AI is replacing us (!)” fallacy. Regardless of how “awesome” all these new fancy AI tools are, the human touch is indispensable!

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