So You Want to be a Data Scientist?


Over the past few years, I have interviewed with multiple companies and have found success in landing the data science job, while also having tons of failures along the way. I made sure to keep note of what I was doing well and what I was not doing well during these interviews. Sometimes, I was lucky enough to get straightforward advice from the companies themselves. This article will lay out how to land the job as a data scientist, as well as some useful tips on the interview process.

Data Scientist

What is a data scientist?

Before focusing on how to interview for this position, it is integral in knowing the definition of a data scientist.

With my experience in several roles and also applying to countless companies, I have come up with my own definition. While it might not be 100% accurate for all positions, I think it serves as a valuable way to look at data science as a career.

  • data science — automating processes with machine learning algorithms and other statistical methods with the use of programming languages like Python to ultimately solve a business problem.

Say you want to perform a linear regression or A/B test, this role might be more known as a statistician. Data science differs in that it requires coding languages and packages like sklearn and TensorFlow to automatically implement machine learning algorithms like random forest, for example. This algorithm would then be incorporated into a holistic data science pipeline that would:

— draw new training and evaluation data in with SQL

— clean that data and prepare it for the model

— apply a machine learning algorithm from a package

— evaluate new data and output predictions

— input predictions into a data table and/or user-interface

With a pretty inclusive and complicated process, data science can be overwhelming and there can be key points in the process that calls for specialization. This point is important in landing a job as it will determine what type of data scientist you will be. I believe there are two main types of data scientists.

While both roles have overlap, there are key differences in what each role focuses on. 

Business Data Scientist: 
- establishing the business problem
- collaborating with stakeholders
- explaining and interpreting model results
A data analyst or business analyst can also focus on establishing the business problem and coming up with key metrics or features that will ultimately be what trains the machine learning model.
Machine Learning Data Scientist:
- ingesting and querying data
- cleaning data and transforming
- coding the model
- deploying model into production
A machine learning engineer can also be a separate position that takes some of the roles from above and focuses even more on the last point - deploying the model into production.
A data engineer can focus on those first two points from above including ingesting and querying data, and sometimes cleaning and transforming the data.

#machine-learning #interview #towards-data-science #data-science #work #data analysis

What is GEEK

Buddha Community

So You Want to be a Data Scientist?
 iOS App Dev

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