AI News, Why should learn data science?
- On 30. september 2018
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Why should learn data science?
Data Scientist is the sexiest job in the 21st century.
Now let’s see what is exactly Data Science: Data Science is a field that encompasses related to data cleansing, preparation, and analysis.
Data scientist understands data from a business point of view.His work is to give the most accurate prediction.
To know more refer below link: What is Data Science Now let’s see the skills needed to become a Data Scientist Skills needed to become Data ScientistApproximately more than 40% of data scientist positions need an advanced degree.
The following are the required data science skills- For more information of Data science refer below link: Data science A complete Guide
A framework for evaluating data scientist competency
For example, “statistics” is often considered an important dimension of the data scientist skills set, but then you get data scientists who come from more of a computer science background than a statistics background.
A data scientist who exhibits all the skills in a well-designed rubric should necessarily exhibit coding skills simply as a matter of course, because a company has a tech stack and data scientists need the skills to integrate with that tech stack in order to ensure that their work is reproducible and scalable.
Even in cases where finances or other business considerations require a single person to occupy two or more of those roles, I think that should be viewed as one person wearing multiple hats rather than a sign that the data scientist hat is big enough to encompass the other two.
I’ve come to think of “good data science” as something that doesn’t really exist at the individual level: while individual team members are all very good at certain skills, building a robust data science capability is something more than any one individual can accomplish.
We all have gaps in our skill sets, but as long as the team doesn’t have any gaps, that’s ok: it ensures that we’re collectively able to do what we need to do and still leaves us all with lots of opportunity to grow.
The decisions data scientists use these skills to make partially depend upon the structure, type, and amount of data available, but also depend on business needs that exist regardless of what the data look like.
It’s always useful when an engineer can clean, structure, and locate data in exactly the way the data scientist needs, but that should be a way to increase efficiency, not a prerequisite for being able to do one’s job.
I’ve found the conscious competence model of learning to be a convenient way of thinking about skills levels: Unconscious incompetence means a data scientist is unskilled but for the most part does not realize it — in fact, does not realize it is important to even have the skill.
For example, people in technical professions generally are often stereotyped as being unconsciously incompetent in the area of soft skills — they are bad at interacting with people, don’t realize they are bad at it, and sometimes even deny that it is important to be good at that sort of thing in the first place.
This stage of learning is rough — a data scientist may see mistakes as an indictment of their value as an employee, perhaps closely mimicing widely-recognized best practices longer than is wise before asking for a mentor to point out a more nuanced approach.
For example, here is how I filled out the rubric for myself after being in my current position for only a couple months: Because there are dozens or even hundreds of ways a data scientist could be said to exemplify a particular skill, I’ve delineated where I think my minimum (<), median (|), and maximum (>)performance in each skill falls.
Not when we have so many job advertisements stipulating that a data scientist must have an advanced degree in a STEM field, or must pass a set of toy coding challenges, or must have on-the-job experience in an impossibly broad set of technical tools.
The Must-Have Skills You Need to Become a Data Scientist
From my perspective as a recruiter, I wanted to put together a list of technical and non-technical skills that are critical to success in data science careers, and at the top of hiring managers’ lists.
Data scientists are highly educated – 91% have at least a Master’s degree and 48% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.
In terms of data science careers, this means that being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.
A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately.
Here are a few of the resources we’ve found to be helpful: I’m sure there are items I may have missed, so if there’s a crucial skill or resource you think would be helpful to any data scientist hopefuls, feel free to share it in the comments below!
Click to download our free salary reports Learn more about the latest salaries and hiring market insights for the 2018 data science hiring market in our 10-minute Burtch Works Study recap video below, or watch the full-length version on YouTube!
What are the skills required to become a Data Scientist?
Although initially many rejected as a simple fashion, but now over the years several organizations have realized the potential of data science to generate useful information from structured and unstructured data.
From banks to e-commerce companies to manufacturing industries, they all understood the importance of data science career and adopted it in their daily activities to improve their performance.
With respect to India, some studies suggest that the data analysis and analysis industry in India is at a stage where it is about 10-15 years old and that we can expect a boom In the field of analytical outsourcing in India.
It is expected that a data scientist will understand the business problem, develop a hypothesis, understand the type of data required, perform data clean-up and preliminary data analysis, build statistical models to solution and ultimately effectively communicate ideas to the client.
For a graduate in engineering or mathematics / statistics, the emphasis is placed more on solving analytical problems and exposure to certain programming languages.
How to start a Data Science career Analytics or Data Science Recruiters are looking for relevant skills and therefore the trick is to acquire these skills over a period of time and exploit them during an interview.
Build Statistics / Machine learning foundations It is expected that a researcher in charge of data mining will have some knowledge of the various statistical methods or automatic learning in the industry.
We can start from the base, ie the normal distribution, the central limit theorem, the test hypothesis and then move on to advanced techniques.
Gain technical skills in Analytics With regard to tools in the analytical industry, SAS and SPSS were popular before the open source revolution took the industry by storm.
Read up on business applications of Data Science Given that the science of data is not only a matter of technique, it would be really useful if one understands the commercial applications of it and one is also aware of various cases of successful use.
For example, how market basket analysis is used for grouping products by retailers, how cluster analysis can be used for customer segmentation for a new product launch, how logistic regression can be used For the detection of fraud in the banking 5.
What Is Data Science, and What Does a Data Scientist Do?
This definition is somewhat loose since there really isn’t a standardized definition of the data scientist role, and given that the ideal experience and skill set is relatively rare to find in one individual.
By Calvin.Andrus (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons While these, and other disciplines and areas of expertise (not shown here), are all characteristics of the data scientist role, I like to think of a data scientist’s foundation as being based on four pillars.
Based on these pillars, a data scientist is a person who should be able to leverage existing data sources, and create new ones as needed in order to extract meaningful information and actionable insights.
This is done through business domain expertise, effective communication and results interpretation, and utilization of any and all relevant statistical techniques, programming languages, software packages and libraries, data infrastructure, and so on.
Author: Stephan Kolassa This diagram, and others like it, attempt to assign labels and/or characterize the person or field that lies at the intersection of each of the primary competencies shown, which I’m calling pillars here.
While many executives are exceptionally smart individuals, they may not be well versed on all the tools, techniques, and algorithms available to a data scientist (e.g., statistical analysis, machine learning, artificial intelligence, and so on).
Even if an executive is able to determine that a specific recommendation engine would help increase revenue, they may not realize that there are probably many other ways that the company’s data can be used to increase revenue as well.
It can therefore not be emphasized enough that the ideal data scientist has a fairly comprehensive understanding about how businesses work in general, and how a company’s data can be used to achieve top-level business goals.
With significant business domain expertise, a data scientist should be able to regularly discover and propose new data initiatives to help the business achieve its goals and maximize their KPIs.
In the phase where results are communicated and delivered, the magic is in the data scientist’s ability to deliver the results in an understandable, compelling, and insightful way, while using appropriate language and jargon level for her audience.
For all of the other phases listed, data scientists must draw upon strong computer programming skills, as well as knowledge about statistics, probabilities, and mathematics in order to understand the data, choose the correct solution approach, implement the solution, and improve on it as well.
It’s true that many of these off-the-shelf products can be used relatively easily, and one can probably obtain pretty decent results depending on the problem being solved, but there are many aspects of data science where experience and chops are critically important.
In addition to traditional degree and certification programs, there are bootcamps being offered that range from a few days or months to complete, online self-guided learning and MOOC courses focused on data science and related fields, and self-driven hands-on learning.
No matter what path is taken to learn, data scientist’s should have advanced quantitative knowledge and highly technical skills, primarily in statistics, mathematics, and computer science.
By ArchonMagnus (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons Generally speaking, both traditional scientists and data scientists ask questions and/or define a problem, collect and leverage data to come up with answers or solutions, test the solution to see if the problem is solved, and iterate as needed to improve on, or finalize the solution.
Some of these shared skills include the ability to: Some of the key differences however, are that data analysts typically are not computer programmers, nor responsible for statistical modeling, machine learning, and many of the other steps outlined in the data science process above.
The data used by data scientists and big data applications often come from multiple sources, and must be extracted, moved, transformed, integrated, and stored (e.g., ETL/ELT) in a way that’s optimized for analytics, business intelligence, and modeling.
As mentioned, data scientists can have a major positive impact on a business’ success, and sometimes inadvertently cause financial loss, which is one of the many reasons why hiring a top notch data scientist is critical.
Alex spent ten years as a race strategist, data scientist, vehicle dynamicist, and software engineer for IndyCar and Indianapolis 500 racing teams. Alex also founded InnoArchiTech, and writes for the InnoArchiTech blog at www.innoarchitech.com. For updates or to learn more, follow @innoarchitech on Twitter, or sign up for the InnoArchiTech newsletter.
- On 31. oktober 2020
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