Shamsheer shah


What does a typical day of a data scientist look like?

The exponential growth is a dream come true for firms and organizations that can learn from the data. But data is meaningless without tools to acquire and analyze it, which drives the high demand for data scientists.

A job as a data scientist promises plenty of potential and high-paying salaries. The 21st century's sexiest job, according to Harvard Business Review. At the moment, there are thousands of unfilled jobs, with many more to come: IBM expects demand for data scientists to double by 2020.

The career potential seems great, but what exactly does a data scientist do all day? We gathered facts to assist you comprehend a data scientist's everyday activities so you can see yourself in that capacity and decide if it's time to train.

There is No Typical Day

First, a disclaimer. If you ask a data scientist about their usual day, they might laugh out loud. If you are versatile and want variation in your job, then a data scientist's day should be perfect for you. While these days are full of change, several components of the day stay constant: dealing with data, people, and keeping up with the field.

Working With Data, Data Everywhere

As the job title implies, a data scientist's everyday activities focus around data. Data scientists spend a lot of time obtaining, analyzing, and shaping data for various purposes. Typical data scientist tasks include:

  • Obtaining information
  • combining data
  • Data analysis
  • Patterns or trends to look for
  • R, Tableau, Python, Matlab, Hive, Impala, PySpark, Excel, Hadoop, SQL, and/or SAS are among the tools used.
  • Creating and evaluating new algorithms
  • Attempting to make data challenges easier to understand
  • Predictive models development
  • The creation of data visualizations
  • Creating a report to share the findings with others
  • Putting together proof-of-concept documents

But these activities are secondary to a data scientist's primary role: problem solving. Working with data entails knowing the aim. Data scientists must also identify the questions that need to be answered before attempting to solve the problem.

Communicating With a Wide Range of Stakeholders

A normal data scientist's day also includes communicating with non-data professionals, which is an element of the atypical data scientist's day. This may sound trivial, but it is important because ultimately your job is to solve issues, not develop models.

Remember that while a data scientist works with data and figures, the motivation is driven by a business requirement. It's vital to see the big picture from a department's perspective. So is the ability to help others realize the implications of their decisions.

Like other people in the corporate world, data scientists attend meetings and react to emails. Communication skills are crucial for data scientists. This means you must be able to communicate the science underlying the data to non-scientists and comprehend their challenges, not just your own.

Keeping up with the Changes

If you decide to become a data scientist, you will spend a lot of time working with data and interacting with others. The rest of the day will be spent learning about data science. Every day, new data scientists solve problems and share their findings with the world. A data scientist usually spends part of the day reading industry blogs, newsletters, and forums. They may attend conferences or network online. And they may occasionally share new information.

You don't want to reinvent the wheel as a data scientist. You want to know whether someone else has a better solution. Only through keeping up with change will you be able to do so.

Getting Started As a Data Scientist 

Are you certain this is the right job for you, and that you'll be able to handle the unique character of each workday? Consider Learnbay’s Data science course, which was created by Ronald Van Loon, who was recognised as one of the world's top 10 Big Data and data science influencers. With only 8 hours of study time each week, you can become a certified data scientist in 21 weeks if you follow the recommended learning path.

You'll learn statistics and statistical procedures, hypothesis testing, clustering, decision trees, linear and logistic regression, R, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS macros, advanced analytics, Matplotlib, Excel analytics functions and more with Learnbay’s Data Science course. You'll learn data mining, data management, and exploration, as well as carry out various industry-relevant projects for hands-on experience, through high-quality online learning resources, simulation tests, and a community monitored by experts.

If you want to learn how to become a Data Scientist, we have the perfect resource for you. The Data Science Career Guide will provide you with information on the hottest technologies, the best firms to work for, the skills you'll need to get started in the booming field of Data Science, and a tailored roadmap to becoming a successful Data Scientist.




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 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