This is the first part of a follow-along series on GitHub collaboration. With this article, I aim to explain how two or more people can collaborate, version control and proofread their codes on GitHub for Data Science projects.
We will be covering specific topics like:
In these articles, we will have two people collaborating on GitHub. Let’s give them two pseudonyms, Sofi and Alec.
Since this is not an article on data cleaning, we will just focus on writing a few functions to see how collaboration and version controlling works. We will be working with a used cars dataset. You can download the dataset from here.
Sofi and Alec: are working on a data cleaning project named “autos“. Sofi takes the initiative of gathering data, creating required .py, .ipynb files for the project.
Exploratory data analysis (EDA) was carried out on the dataset. Please refer to this article for the EDA. Based on EDA report tasks are planned for the project. We will only look at 3 tasks in this article.
Sofi creates a project folder (…/autos) with two files, auto.csv and autos_analysis.ipynb.
In software engineering, version control (also known as revision control, source control, or source code management) is a class of systems responsible for managing changes to computer programs, documents, large web sites, or other collections of information. Version control is a component of software configuration management.
There are many version control systems out there. Often they are divided into two groups: “centralized” and “distributed”.
CVCS are based on the idea that there is a single “central” copy of your project somewhere (probably on a server), and programmers will “commit” their changes to this central copy. The most popular CVCS is Subversion.
#git-pull #git-clone #git-commands #git-push #git-branch
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