Sasha  Lee

Sasha Lee

1624616460

University Degree vs Summer School vs Self-Learning: Data Science & Data Analytics

Fuelled by big data and AI, demand for data analytics and data science skills is growing exponentially, according to job sites. As companies are searching for approaches to harness the power of Big Data, technology professionals who are experts in data analytics and data science are in high demand. The supply of skilled applicants, however, is growing at a slower pace which makes these sorts of jobs great for career changers.

In this blog post, I will assume that you are not straight out of high school, but either already have a university degree (not in computer science or statistics) and/or have already worked for several years and are now considering how to become a qualified data analyst or scientist. There are, of course, many factors to consider when deciding how to enhance your current skillset like previous experience, financial resources, and how much time you want to invest into this, and my intention is to outline the pros and cons of the most common ways to upskill: a university degree, a crash-course (like a Summer School), and online courses of various types.

I am currently a teaching fellow at University College London in the Computer Science department, have taught a Summer School in Data Science in the past, and did a lot of self-learning because my Bachelor’s degree was in Law, so I believe I can give a good overview of the pros and cons on all of the above.

Master’s degree in data science, machine learning or data analytics

**Cons: **The main drawback of university degrees (in the UK) is, obviously, the cost. If you are considering studying full time, you do not just need to cover the tuition fees but also living costs for a whole year. A second problem is the fact that it is application based and you might not be accepted for the programme that you are interested in. In order to do a Master’s degree in data science or machine learning, you need to have a Bachelor’s degree in a quantitative subject. So, unless you have studied maths, engineering, economics, or finance, you will not be eligible for such a Master’s programme and will have to do a conversion course first (MSc in Computer Science). For someone, who has no background in computer science, but has the financial resources and the time, I can highly recommend doing a conversion course, as I did back in 2015, after finishing my Bachelor’s degree in Law. But this is, for now, out of the scope of this article.

Another con is that by the end of the degree you might not necessarily have a portfolio of different projects which you could show to potential employers. The courseworks often do not reflect real business problems and your Master thesis might be more on the academic, “researchy” side.

Pros: If you have the necessary quantitative background and the financial resources (or access to a student loan) then doing a Master’s in data science, machine learning, data analytics, or something more specialised like financial computing, definitely has its benefits: you will have access to a variety of modules as part of a structured course, meet leading academics in the field, meet interesting people and make new friends and (hopefully, in a post-COVID-19 world) experience student life.

#data-analytics #upskilling #training #data-analysis #data-science #data science & data analytics

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University Degree vs Summer School vs Self-Learning: Data Science & Data Analytics
Sasha  Lee

Sasha Lee

1624616460

University Degree vs Summer School vs Self-Learning: Data Science & Data Analytics

Fuelled by big data and AI, demand for data analytics and data science skills is growing exponentially, according to job sites. As companies are searching for approaches to harness the power of Big Data, technology professionals who are experts in data analytics and data science are in high demand. The supply of skilled applicants, however, is growing at a slower pace which makes these sorts of jobs great for career changers.

In this blog post, I will assume that you are not straight out of high school, but either already have a university degree (not in computer science or statistics) and/or have already worked for several years and are now considering how to become a qualified data analyst or scientist. There are, of course, many factors to consider when deciding how to enhance your current skillset like previous experience, financial resources, and how much time you want to invest into this, and my intention is to outline the pros and cons of the most common ways to upskill: a university degree, a crash-course (like a Summer School), and online courses of various types.

I am currently a teaching fellow at University College London in the Computer Science department, have taught a Summer School in Data Science in the past, and did a lot of self-learning because my Bachelor’s degree was in Law, so I believe I can give a good overview of the pros and cons on all of the above.

Master’s degree in data science, machine learning or data analytics

**Cons: **The main drawback of university degrees (in the UK) is, obviously, the cost. If you are considering studying full time, you do not just need to cover the tuition fees but also living costs for a whole year. A second problem is the fact that it is application based and you might not be accepted for the programme that you are interested in. In order to do a Master’s degree in data science or machine learning, you need to have a Bachelor’s degree in a quantitative subject. So, unless you have studied maths, engineering, economics, or finance, you will not be eligible for such a Master’s programme and will have to do a conversion course first (MSc in Computer Science). For someone, who has no background in computer science, but has the financial resources and the time, I can highly recommend doing a conversion course, as I did back in 2015, after finishing my Bachelor’s degree in Law. But this is, for now, out of the scope of this article.

Another con is that by the end of the degree you might not necessarily have a portfolio of different projects which you could show to potential employers. The courseworks often do not reflect real business problems and your Master thesis might be more on the academic, “researchy” side.

Pros: If you have the necessary quantitative background and the financial resources (or access to a student loan) then doing a Master’s in data science, machine learning, data analytics, or something more specialised like financial computing, definitely has its benefits: you will have access to a variety of modules as part of a structured course, meet leading academics in the field, meet interesting people and make new friends and (hopefully, in a post-COVID-19 world) experience student life.

#data-analytics #upskilling #training #data-analysis #data-science #data science & data analytics

Uriah  Dietrich

Uriah Dietrich

1618449987

How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

Sasha  Lee

Sasha Lee

1624013340

Data Analytics vs Data Science: What Better Suits your Needs?

Analytics Insight takes you through the basics of data analytics vs data science to give a broad outlook.

What makes the 21st century different from the 20th century? Just 21 years have beaten the past hundred years. Yes, the main driver of this shift is the debut of data. Big data has become a major component of everyday life, thanks to the actionable insights and results in which businesses can glean. From big data emerged the big two trends: Data analytics and data scienceData analytics vs data science, the two sides of the technology are fighting over dominance. Even though both are very important trends in the digital sphere, people can’t choose both at a time.

The evolution of big data has moved out of the technology sector long back. Today it is everywhere. Without the help of big data, almost all the industries will get jeopardized. The World Economic Forum stated that by the end of 2020, the daily global data generation will reach 44 zettabytes, and it will further surge to 463 exabytes in 2025. However, the creation of such large datasets also requires understanding and having proper tools on hand to parse through them to uncover the right information. They can’t be directly used in any sector.

Data undergoes many routine processes before it is used effectively in an organization. To better comprehend big data, the fields of data science and data analytics have escalated their stance. The duo technologies have moved out of the academic, to instead become a core element of business intelligence and big data analytics tools. But the war between data analytics vs data science is still on. While some organizations can afford to choose both data analytics and data science for their routine functionalities, some others can’t do that. When people are bound to select and support one of the technologies, the conflict breaks out. Picking a career option between these two is also a pain point. Henceforth, this article will take you through the basics of data analytics vs data science and let you know which one will better suit your strategy.

Description

Data analytics: Data analytics is the concept of processing and performing statistical analysis of existing datasets. It is seen as the initial step that analysts create to capture, process, and organize data to uncover actionable insights for business problems. In a nutshell, data analytics provides an answer for complicated data-based or data-related questions which could lead to immediate improvement. It also encompasses a few different branches of broader statistics and analysis which help combine a diverse source of data and locate connections while simplifying the results. Some of the main purposes why the technology is leveraged are listed below,

  • By assessing a company’s historical revenue, sales, and costs with its goals, an analyst could identify the budget and investments required to make those goals a reality.
  • A data analyst can make cost-effective recommendations to help mitigate business risks.
  • With the help of data, marketing analysts can identify the number of leads their efforts must generate to fill the sales pipeline.

Data science: Data science is a versatile and multidisciplinary field focused on finding actionable insights from large sets of raw and structured data. The technology unearths answers for complicated business questions. Data scientists use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in an effort to establish solutions to problems that haven’t been thought of yet. Some of the reasons to pick data science are listed below,

  • Data scientists identify and avoid mistakes that commonly arise while interpreting datasets, metrics, and visualization.
  • The technology embraces data-driven decision-making and ensures that the business decisions are backed by numbers.
  • Data science understands the market size, buyer trends, competition, and opportunities, and risks your business faces.
Skills

Data analytics: Data analysts are expected to show much importance to a single or a couple of topics and reflect on them with data. Some of the other skills are,

  • Data wrangling
  • Understand PIG/HIVE
  • Fluent understanding of R and Python
  • Knowledge of mathematical statistics

Data science: Since data science revolves around analytics, programming, and domain knowledge, the professionals are expected to be experts in the three departments. Some of the other mandatory skills are,

  • Hands-on experience in SQL database coding
  • Strong knowledge of Python, SAS, R, Scala, etc
  • Understanding multiple functions
  • Mandatory knowledge about machine learning

#data science #latest news #data analytics vs data science #data science #data analytics

 iOS App Dev

iOS App Dev

1620466520

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

'Commoditization Is The Biggest Problem In Data Science Education'

The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

#people #data science aspirants #data science course director interview #data science courses #data science education #data science education market #data science interview