I admit, I originally took the 7 Habits course offered by FranklinCovey because my employer offered it for free, and it offered a way to get out of my windowless office…
In taking the course however, I discovered the value and widespread applicability of Stephen Covey’s 7 Habits of Highly Effective People. The paradigms, practices, and principles of the 7 Habits provide a great framework for understanding an individual’s role in any context — including data science.
I previously wrote about David Allen’s Getting Things Done, which provided a practical framework through which to understand the data science project lifecycle — call it the “what”. 7 Habits provides a framework through which to understand the “who” — the data scientist. “7 Habits” has become so ubiquitous that often, people will invoke the name, and then list 7 unrelated/random habits. Or, they’ll list the habits correctly, without the complete context. 7 Habits of Highly Effective People (let’s call it 7H from here on out…) is a paradigm, not a [list]. Don’t worry, though, I’ve got you covered — before getting into the list, we’ll review the foundational framework underlying this paradigm.
Maturity Continuum from Stephen Covey’s 7 Habits
The goal of the 7 Habits is to move from the basic child-like state of dependence through the just-graduated-college state of independence, to land in the 7 Habits nirvana of interdependence in which you understand your mutual dependence on others. Achieving independent status from a state of dependence requires mastery of the private victory, and the subsequent achieving of the public victory. In non-fancy terms — you gotta know how to have your house in order before Marie-Kondo-ing other people’s houses!
So how does this apply to data science? Well, let’s understand it through roles. Novice data science students (self-guided, textbook learner, youtube video-watcher, or bootcamper, etc.) are in a state of dependence. Like children, we extract resources from module documentation, O’Reilly, StackOverflow, YouTube professors, and Learning Advisors. We’re generally given a path, and follow it. In order to be more effective as data practitioners, however, we’ll have to move out of this phase.
Advanced data science students and entry-level data scientists/analysts are in a state of independence. These self-starters may be competent and able enough to manipulate data and operate the program of their choice, without assistance from others. If someone fed them data and an appropriately framed question, they could easily create a visualization, regression, or classification that answers that question in isolation, perhaps asking others for help when they need it. This autonomy may be good enough for winning Kaggle competitions, but does not an effective data scientist make.
Practitioners, Professors of the Practice, industry experts, etc. comprise the third and last category of Interdependence. These academics, policy experts, practitioners, and department heads have (theoretically) reached what I’ll call “7 Habits nirvana”: they know that their individual continued success, growth, and development depends on the value delivered to the business, or to others around them. They’re the not only actively engaging others by answering the StackOverflow questions and various community questions, but also amplifying the magnitude of others’ diverse voices.
Now that you’ve read and understood the foundation of the Maturity Continuum, I can share the secret 7 Habits of a Highly Effective Data Scientist. Turns out…they’re the same! Ready?
**Habits 1–3 **will move us from dependence to independence
**Habits 4–6 **will move us from independence to interdependence
**Habit 7 **will bring us to “7H nirvana”
#7-habits #personal-growth #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this article, we list down 50 latest job openings in data science that opened just last week.
(The jobs are sorted according to the years of experience r
Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.
Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.
Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.
Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.
**Location: **Bibinagar, Telangana
Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.
#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Around once a month, I get emailed by a student of some type asking how to get into Data Science, I’ve answered it enough that I decided to write it out here so I can link people to it. So if you’re one of those students, welcome!
I’ll segment this into basic advice, which can be found quite easily if you just google ‘how to get into data science’ and advice that is less common, but advice that I’ve found very useful over the years. I’ll start with the latter, and move on to basic advice. Obviously take this with a grain of salt as all advice comes with a bit of survivorship bias.
#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists
According to a recent study on analytics and data science jobs, the number of vacancies for data science-related jobs in India has increased by 53 per cent, since India eased the lockdown restrictions. Moreover, India’s share of open data science jobs in the world has seen a steep rise from 7.2 per cent in January to 9.8 per cent in August.
Here is a list of 5 such companies, in no particular order, in India that are currently recruiting Data Scientists in bulk.
#careers #data science #data science career #data science jobs #data science recruitment #data scientist #data scientist jobs