As we inch further into the year, I have seen more and more postings for data science positions, especially on LinkedIn, and other similar job-posting sites. After an expected lull due to current events, companies have figured out their budget and focus. Some of those companies include newer data science positions that they need to hire as soon as possible or in the near future.
There are several reasons for becoming a data scientist. I am going to highlight five main reasons I became a data scientist, and hopefully, it can align with some of the reasons why you would become one as well.
As with many positions that have any general set of expected skills, data science is no exception, and can usually be thought to have these skills that I will outline below. Of course, there are others, but I will focus on the skills I come across the most at various companies as a data scientist.
— the heavily debated Python versus R is usually controversial, but ultimately, it just depends on what the company is already using as their main programming language. Sometimes, data scientists can work alone and form models and output results directly to a stakeholder, and usually refer more to R in this case. However, in my experience, it has been easier to work cross-functionally with both data engineers and software engineers with the use of Python. This language is oftentimes used for deployment purposes, so, it can be easier to start with Python from the start. The benefit is that in the process of learning data science, you will learn Python or R, which will help you earn a variety of skills that can support you better down the road if you chose a different career path such as software development.
— another popular skill for data scientists is SQL. Sometimes, online courses and universities neglect to stress the importance of how widely used this language is for data scientists. It is nearly used for every project I work on because the dataset is not simply given to you. You have to make your own dataset, and that involves querying your database tables with SQL. Like Python (and somewhat R), learning SQL is useful not only for data science but for data engineering and data analytics as well.
— while this skill is not a programming language, it is still important. Business, more so a concept, is something every data scientist learns. Similarly to SQL, it is not taught in education settings nearly as much as it should. What I mean by the business is that you need to really get used to jumping into situations that are not strictly just data science. The business uses data scientists to either make a process more efficient or find insights that will change the business in the future. Oftentimes, education for data science will focus so much on obtaining the highest accuracy for say, segmenting different types of customers. It can be great to achieve 98% accuracy, but if you are not able to come up with a plan for how you would implement the model and its results thereafter, then your model is useless.
You need to know that stakeholders, CEO’s, C-Suite/higher leadership, will ask what you will do with your results to change the business. So in turn, you would want to apply those customer segmentation groups to a marketing campaign through various, targeted emails. Then, you would create a test of some sorts to see how the emails performed, say with an AB test. As you can see, just having an extremely accurate model is just one part of the data science and business process. Practicing this business process over and over again is extremely beneficial.
— there was more focus on statistics in school, and it can prove to solve many problems for a data scientist. Knowing statistics is critical for data scientists, as it is the foundation of machine learning models. Practicing analysis of variance, or population sampling, etc, is useful in several forms of the business, say marketing campaigns again, or AB testing.
#towards-data-science #data-science #technology #work #machine-learning #data analysis
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
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
Want to know how to become a data scientist from scratch? This comprehensive guide will take you through every necessary step to become a successful data scientist.
A Complete Career Guide on How to Become a Data Scientist
Data science has become the hottest career option for students. It’s become one of the fastest-growing career paths. In this high-tech world, every business and organization needs data scientists to leverage their data to the fullest extent. This provides ongoing opportunities for those who want to get hired into a data scientist role. This blog post will take you through all the necessary steps you need to know to become a successful data scientist.
#learn-data-science #data-science #data-science-skills #become-a-data-scientist #data-scientist
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