While working in a software project it is very common and, in fact, a standard to start right away versioning code, and the benefits are already pretty obvious for the software community: it tracks every modification of the code in a particular code repository. If any mistake is made, developers can always travel through time and compare earlier versions of the code in order to solve the problem while minimizing disruption to all the team members. Code for software projects is the most precious asset and for that reason must be protected at all costs!
Well, for Data Science projects, data can also be considered the crown jewels, so why us, as Data Scientists, don’t treat as the most precious thing on earth through versioning control?
For those familiar with Git, you might be thinking, _“Git cannot handle large files and directories… at least it can’t with the same performance as it deals with small code files. So how can I version control my data in the same old fashion we version control code?”. _Well, this is now possible, and it’s easy as just typing
git cloneand see the data files and ML model files saved in the workspace, and all this magic can be achieved with DVC.
First things first, we have to get DVC installed in our machines. It’s pretty straightforward and you can do it by following these steps.
As I’ve already mentioned, tools for data version control such as DVC makes it possible to build large projects while making it possible to reproduce the pipelines. Using DVC it’s very simple to add datasets into a git repository, and when I mean by simple, is as easy as typing the line below:
dvc add path/to/dataset
Regardless of the size of the dataset, the data is added to the repository. Assuming that we also want to push the dataset into the cloud, it is also possible with the below command:
dvc push path/to/dataset.dvc
Out of the box, DVC supports many cloud storage services such as S3, Google Storage, Azure Blobs, Google Drive, etc… And since the dataset was pushed to the cloud through the version control system, if I clone the project into another machine, I’m able to download the data, or any other artifact, using the following command:
Well, now that you know how to start with DVC, I suggest you to go and further explore the tool, or similar ones. Version control should be your best friend as a Data Scientist, as they allow not only to version datasets but also to create reproducible pipelines, while keeping all the developments traceable and reproducible.
If this hasn’t yet convinced, next I’ll tell why **_you must start versioning control _**your data!!
#data #machine-learning #data-science #software-development #version-control
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
We’re living in a data-driven world. All the more reason for companies to hire data scientists to work on a variety of issues.
One of the most popular terms these days is data scientist. Although the word has recently become a great buzzword in the industry, the area of data science isn’t unique. Lots of data scientists have been operating in various industries for quite some experience now.
The purpose of building machines as intelligent as human beings has also been attempted for some time now. Therefore, why is the word appearing in a lot of advertising these days? To explain the appearance, let’s probe deeper into what a data scientist is, take a glance at the attributes that one wants to control to work in this domain, and examine why it has become necessary for companies to hire these professionals.
Data scientists typically have expertise in a few main areas, including machine learning, mathematics, or statistics, software engineering/coding, and expertise in the business in which they explore employment. Most experienced data scientists are influential professions in a broad variety of fields. They often contribute to software development, act as a classical statistician or researcher, or engage in data pipelines and business intelligence roles. Someone with extended experiences in all three areas is an exceptional person and authority. In addition to these abilities, a reliable data science applicant should understand scientific research methods and solid conversation talents to turn results into viable business solutions.
#analytics #big data #big data architectures #data science #data scientist #data scientists