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What are the requirements to become a data scientist?

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Richard’s Answer

I am not a data scientist but in my job I have worked with a few. The data scientist found correlations between multiple data-sets to help our company adjust our strategy after introducing a new product. On the team some of the members were data scientists but many had a few of the "Skill Set's" mentioned below and were learning "On the Job". I hope this helps.

I found this information https://www.discoverdatascience.org/career-information/data-scientist/

Majors for data science are statistics, computer science, information technologies, mathematics, or data science (if available). Minoring in one of the aforementioned fields is also recommended.

Data Scientist Skill Set

  1. Experience and Fluency in many of these computer/coding programs: SAS, SPSS, MATLAB R, Python, Java, C/C++, Hadoop Platform, SQL/NoSQL Databases.
  2. Business Savviness: Data scientists need to understand the business sector they are working in and create solutions to complex problems that align with business logic/objectives.
  3. Communication skills: A data scientist can clearly and fluently translate their technical and analytical findings to a non-technical department. They must also be able to understand the needs of their non-technical departments (such as business development or marketing teams) in order to analyze the data correctly. A data scientist must empower the business to make decisions by presenting robust and verifiable information.
  4. Expert Technical skillsin the following:
  • Math (g., linear algebra, calculus, and probability)
  • Statistics
  • Machine learning tools and techniques
  • Data mining
  • Data cleaning and munging
  • Data visualization and reporting techniques
  • Unstructured data techniques

Richard recommends the following next steps:

Here are a few on-line classes (some free) https://www.forbes.com/sites/bernardmarr/2017/06/06/the-9-best-free-online-big-data-and-data-science-courses/7e0045a343cd
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Mohamed’s Answer

There are three general steps to becoming a data scientist:

Earn a bachelor's degree in IT, computer science, math, physics, or another related field;
Earn a master's degree in data or related field;
Gain experience in the field you intend to work in (ex: healthcare, physics, business).

Data Scientist Education Requirements
There are many paths to landing a career in data science, but for all intents and purposes, it is completely impossible to launch a career in the field without a college education. You will, at the very least, need a four-year bachelor degree. Keep in mind, however, that 73% of the professionals working in the industry have a graduate degree and 38% have a PhD. If your goal is an advanced leadership position, you will have to earn either a master’s degree or doctorate degree.

Some schools offer data science degrees, which is an obvious choice. This degree will give you the necessary skills to process and analyze a complex set of data, and will involve lots of technical information related to statistics, computers, analysis techniques, and more. Most data science programs will also have a creative and analytical element, allowing you to make judgment decisions based on your findings.

While a data science degree is the most obvious career path, there are also technical and computer-based degrees that will help launch your data science career. Common degrees that help you learn data science include:

Computer science
Statistics
Physics
Social science
Mathematics
Applied math
Economics

At the end of one or more of these degrees, you’ll likely have a wide range of skills that apply to data science. These skills include experimentation, coding, quantitative problem solving, handling large sets of data, and more.

The ability to understand people, businesses, and marketing is also a powerful tool in a data science career. The skills are often highlighted in business, psychology, political science, and various liberal arts degrees. These are often a great minor, complementing a data science degree or a technical degree.

Data Science Specializations
Data science is needed by nearly every business, organization, and agency in the country and across the globe, so there is certainly the chance for specialization. Many data scientists will be heavily specialized in business, often specific segments of the economy (such as automotive or insurance) or business-related fields like marketing or pricing. For example, a data scientist may specialize in helping car dealerships analyze their customer information and create effective marketing campaigns. Another data scientist may help large retail chains determine the perfect price range for their products.
source: https://www.geteducated.com/careers/how-to-become-a-data-scientist/
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Yubing’s Answer

There are usually 4 types of skills required:
- Math and statistics
- Programming and database
- Domain knowledge
- Communication and visualization

Here is a good overview:
https://towardsdatascience.com/a-long-term-data-science-roadmap-which-wont-help-you-become-an-expert-in-only-several-months-4436733e63ff
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Charley’s Answer

It honestly depends on the industry and the company. You probably can't go wrong knowing SQL, Python, R and some data visualization tools like Tableau.
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Joshua’s Answer

Use the scientific method with regards to data in all forms and how they relate. Science has no requirements aside from workable tests and provable results. Feedback loop with quantifiable results in any direction.


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Lilian’s Answer

I would just like to add the important of subject matter expertise in the area as data cleansing and wrangling is what you would spend most of your time on and you can only do that effectively or ask better questions of your data if you understand the data you are looking at.
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Sean’s Answer

In addition to the skills mentioned above, the best data scientists also have the right attitude.

1) *Curiosity* - A great data scientist will keep asking questions to find more valuable insights. This includes asking questions of the business partner and of the data itself.
2) *Empathy* - A great data scientist will need to understand the viewpoint of a user who will consume their analysis. What information do they actually need? What is an easily consumable way to give that information? How can you read between the lines of what they're asking you to find since they may not always have the language for it.
3) *Initiative* - A great data scientist is more than just a lifelong learner, they are a lifelong hacker. By this I mean they recognize that often what they are asked to do is something they have never done before, and the best way to learn is by doing. So they search the internet, copy & paste code, run the program, examine the output, and adjust as needed. To build a new model or complete a new analysis, you do not first need to complete a course on it. Rather, start by doing the analysis or a piece of it, communicate back with the stakeholders, get feedback, and iterate from there.

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Lynn’s Answer

It is important for you to know how to use one of the following tools, R, Python, SAS. these tools will help you a lot on your day to day function.
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