A Guide to Git for Data Scientists

For quite some time, git had been this nebulous, terrifying, thing for me. It was kind of like walking on a tight rope holding a bunch of fine china. Yeah I knew how to git add, git commit, and git push. But if I had to do anything outside of that, I’d quickly lose my balance, drop the fine china, and git would inevitable break my project into unrecognizable pieces.

If you’re a developer, you probably know git pretty well. But git is now becoming an inescapable skill for anyone in a field involving programming and collaboration, especially data science.

So I finally bit the proverbial bullet and tried to understand the more holistic picture of git and what its commands were doing with my code. Fortunately, it turned out to be something that wasn’t as complicated as I had thought and by correcting my underlying mental model of it, I had more confidence and less anxiety when tackling new projects.

The four areas of git

There are four areas in git: stash, working area, index, and repository.

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Your typical git workflow works from left to right, starting at the working area. When you make any changes to files in a git repository, these changes show in the working area. To view which files were changed along with new files you created, simply do a git status. To show the more granular details about what exactly was changed in a file, just do a git diff [file name].

Once you’re satisfied with your changes, you then add the changed files from the working area to the index with a git add command.

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The index functions as a sort of staging area. It exists because you might be trying some things out and changing a lot of code in the working area but you don’t necessarily want all those changes to be in the repository area just yet.

So the idea is to just selectively add the changes you want to the index and it’s best practice for the changes you add to be some logical unit or collection of things that are related. For example, you might decide to add all the files with changes that relate to the new preprocessing function you wrote in Python.

Once you’ve added all relevant files to the index, finally move them to the repository by doing a git commit -m 'Explanation of my changes'. You should see that there are no difference between the index and repository now - something you can verify with a git diff --cached.

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Mistakes were made

But life is not so ideal. Eventually you’ll make a commit with an inappropriate message that you want to change, or you’ll decide that all those new things you added totally broke everything and now you want to go back to how it was before. Ever wanted to include a coworker’s updates into the code you’re working on only to find there are file conflicts?

You might have seen some git commands like rebase, reset, and revert. If these commands scare you, you’re not alone. Some of them are powerful, and can destroy your project if you don’t know how to use them. But fret not, you’re about to learn how to use them. 😊

#data-science #git #developer #github #programming

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A Guide to Git for Data Scientists
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iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

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

Java Questions

Java Questions


50 Data Science Jobs That Opened Just Last Week

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

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

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.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

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.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**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

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

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

Ian  Robinson

Ian Robinson


Data Science: Advice for Aspiring Data Scientists | Experfy Insights

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.

Less Basic Advice:

1. Find a solid community

2. Apply Data Science to Things you Enjoy

3. Minimize the ‘Clicks to Proof of Competence’

4. Learn Through Research or Entry Level Jobs

#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists

5 Indian Companies Recruiting Data Scientists In Large Numbers

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