It has been 2 years ever since I started my data science journey. Boy, that was one heck of a roller coaster ride!
There were many highs and lows, and of course, countless cups of coffee and sleepless nights.
I failed a lot, learned a lot, and of course, grew a lot as a data scientist along the journey.
Throughout my journey in these 2 years, from writing on Medium, speaking at meetups and workshops, sharing my experience on LinkedIn, consulting clients on data science projects, to the current stage of teaching data science in education, I find joy and fulfilment in sharing and teaching to help others in data science and make an impact.
At the end of the day, it all boils down to one simple fact — that I’m moving towards my mission — Making data science accessible to everyone.
If you’re interested, feel free to check my previous LinkedIn post on why I decided to transition from a data scientist to becoming a data science instructor — a.k.a teacher.
In this article, for the first time, I’ll consolidate everything that I’ve learned and condense all of these into 5 lessons that I’ve learned in 2 years as a data scientist.
If you’re just starting out in data science and wondering what to learn…
Or you’re looking for a job in data science…
Or you’re already working in data science space…
I hope you’ll find these 5 lessons helpful to you as a data scientist!
Enough of talking… Let’s get started!
#2020 sep opinions #career advice #data scientist
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
Want to know how to become a data scientist from scratch? This comprehensive guide will take you through every necessary step to become a successful data scientist.
A Complete Career Guide on How to Become a Data Scientist
Data science has become the hottest career option for students. It’s become one of the fastest-growing career paths. In this high-tech world, every business and organization needs data scientists to leverage their data to the fullest extent. This provides ongoing opportunities for those who want to get hired into a data scientist role. This blog post will take you through all the necessary steps you need to know to become a successful data scientist.
#learn-data-science #data-science #data-science-skills #become-a-data-scientist #data-scientist