Master Of Data Science Degree – The future of data science jobs in 2020 continues to be brighter than ever. Why? According to Glassdoor’s list of the best jobs for the past four years, data scientists rank highest in job demand, job satisfaction and salary, with an average salary of more than $100,000 a year.
So who are today’s data scientists and what does it take to become one? Our friends at 365 Data Science used public data from 1,001 LinkedIn professionals, including junior, expert, and senior data scientists, to answer these questions.
Master Of Data Science Degree
40% of surveyed data scientists currently work in the US; 30% of the sample is located in Great Britain; 15% work in India; and 15% are from the collections of other countries.
M.s. In Data Science
50% of the group currently work for a Fortune 500 company, and the remaining 50% work for an unranked company.
According to the study, there are twice as many men as women scientists. They usually speak two languages, usually English and their mother tongue; four out of five have at least a master’s degree; and 8.5 years of total work experience is required to become a data scientist. Nine out of ten use Python or R, and about 80% of the cohort has at least a master’s degree. Furthermore, the typical data scientist in 2020 has held this prestigious title for an average of 3.5 years.
Education is one of the three main components of most resumes. It makes perfect sense because an education demonstrates your knowledge to your future employers, especially when you lack years of work experience.
According to our data, the typical data scientist in 2020 has a bachelor’s degree (13%), a master’s degree (56%), or a Ph.D. (27%) as the highest academic qualification. Yes, data science requires advanced expertise. So, while the field of data science welcomes candidates with a PhD, a master’s degree will definitely increase your chances of success. As for Ph.D. degree? Well, based on the numbers, that’s more of a bonus than a guess. That explains why its percentage has remained roughly the same based on the last 3 years of research
Master Of Science In Software Engineering, Specialization In Data Science
Based on the sample, more than half of the currently employed data scientists were already employed. 11% started as a data analyst. The rest came from academia (8.2%), started as a trainee (7%), IT professional (2.4%), consultant (3.8%) or engineer (2.7%). And 12.5% moved from other professions.
The data showed that data science and analytics, which accounted for 21% of data scientists, was the degree most likely to get you into data science in 2020, followed by previous major computer science (18.3%) and traditional statistics and mathematics (16.3%).
Admittedly, data scientists come from diverse backgrounds. However, no degree can prepare a person for a real job in data science.
Therefore, it should come as no surprise that data scientists rely heavily on self-training. In fact, 41% of the sample had an online course on their LinkedIn profile. And there has been a steady trend over the past two years (40% in 2018 and 43% in 2019).
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74% of the cohort use Python, 56% are proficient in R, and 51% are proficient in SQL. In terms of popularity, SQL has grown by 40% (36%) since 2019, approaching Rs.
When it comes to Fortune 500 companies and programming languages, the common use of Python and R indicates that large companies are rethinking how they organize their data. Although open source frameworks may be unstructured, unlike Matlab and C, more and more companies are turning to user-friendly Python and R these days.
What about coding language and country of work? In 2020, Python is the undisputed king of the coding language in all countries reflected in the survey. SQL seems to be unsettled by Python’s reign and has even grown in popularity compared to previous years.
Data speaks louder than words. And by the numbers, data science is thriving and continues to grow. Furthermore, in 2020, universities begin to meet the growing demand by expanding the scope of education and the relevant degrees.
Master’s Programme In Data Science (eit Digital Master School) Ets De Ingenieros Informáticos (upm)
However, you never know what’s in store for data science in the next 2-5 years. So, if you want to keep up with the latest developments, you can follow the 365 Data Science blog for more learning material. It’s been about two months since I finished my last course in the Master’s in Data Science at UW. Considering I got this app in the mail this week, it seems like a good time to address the schedule.
With apologies to David Letterman, I’m going to write this as a top 10 countdown. I think I put 1,000 hours into the 12 classes over three years, so getting to the 10 main points makes a lot of sense. But hey, this is a blog post…
DS 780, Strategic Decision Making, required multiple essays with academic paper sources. At first I thought that it was some kind of extra expense. But I began to see the value of this activity and that I had underestimated this resource in my work and research.
The DS780 also gave perspective on a different concept of digital conversion. As someone who spent his career in product engineering, it was interesting to learn how the world of marketing has changed in the digital world.
Top 10 Online Master’s In Data Science Degree Programs In 2019
You can always improve your communication skills. Through lectures and exercises in DS 735, I was taught the importance of being direct and concise in my writing. I have less skill and practice in presentation skills than writing skills. The opportunity to learn new data presentation skills, practice them and receive feedback was valuable.
When I started the program, I didn’t really know the difference between predictive technology and prescriptive technology. Learning action research techniques like linear programming and stochastic simulation helped me understand that data science is more than ML. This is an underrated feature in this industry.
Speaking of ML, we covered a lot of it in the DS 740. Gaining insight into how each algorithm works as well as the assumptions was two valuable things. I also have one of the most popular data science references in Springer’s Elements of Statistical Learning for further reference.
Before this time, I had been exposed to the big data query language, Hive, but honestly didn’t understand it. Creating a map reduction program directly before learning Hive and Piggy was a great way to understand what was going on.
M.s. In Data Analytics And Visualization
The null hypothesis is a classical statistic that is necessary and something that is not intuitive. The process and the language are very unique. It takes practice to do it well, and practice is what we got in the DS 705. And we learned about the importance of the normal distribution, what to do when you don’t have a normal distribution, t-tests, ANOVA, and more. .
Sociotechnical issues are complex because they are constantly emerging and changing. Learning how to apply an ethical framework to DS 760 and realizing that technical publications from 15-20 years ago tended to address these issues was an eye-opener. I have a lot more respect for some of the work Microsoft and other leaders are doing in this space right now, and a lot less patience for companies that aren’t proactive about privacy, security, and other ethical concerns.
The bias comes from a model that underrepresents the real problem… an unsophisticated model. Drift occurs when a model changes significantly with new data … an overtrained model. Both are sources of model error, and a middle ground between these errors must be reached to obtain an optimal model. This is necessary to apply ML.
Admission #1 is related to breadth of course content. Unlike a boot camp where you learned how to code in R or a MOOC on neural networks, the curriculum touched on a wide variety of data science topics. As this top 10 shows, many of these topics are not technical in nature. For technical topics, understanding the underlying concepts is essential to having the intuition to apply the technology.
Course Overview: Data Science, Two Year Master
I could write a lot more about the program itself, but this post is already long. However, I will emphasize one point. The UW program is a multi-campus, multi-faculty program. This diversity is a particular strength of the program and was an important factor in its success.
Given the variety of prerequisites covered in the program, this list will vary significantly from student to student. You don’t see a lot of software engineering or programming stuff on my list, for example. For others, proficiency in R or Python may be the primary concern.
I was lucky enough to work for a company, Microsoft, with a tuition reimbursement program. I would be remiss if I didn’t post this as it was a huge plus for me. Tuition reimbursement is an untapped benefit… take advantage of it if you can.
From a personal perspective, my goal was to capitalize on this investment