10 Questions to Consider Before Pursuing a Career in Data Science 在追求数据科学职业之前需要...

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Analyzing several parameters & conditions before jumping into Data Science career

Data Science, Machine Learning, and Analytics are considered to be among the hottest career paths. The demand for skilled data science practitioners in industry, academia, and the government is rapidly growing. The ongoing "data rush" is, therefore, attracting so many professionals with diverse backgrounds such as physics, mathematics, statistics, economics, and engineering. The job outlook for data scientists is very positive. The IBM predicts the demand for a data scientist to soar 28% by 2020: https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/#7916f3057e3b.{#210e }

数据科学,机器学习和分析被认为是最热门的职业道路之一。工业,学术界和政府对熟练数据科学从业者的需求正在快速增长。因此,正在进行的"数据热潮"吸引了众多具有不同背景的专业人士,如物理,数学,统计学,经济学和工程学。数据科学家的就业前景非常乐观。 IBM预测到2020年数据科学家的需求将飙升28%:https: //www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-飙升-28-通过-2020 /#7916f3057e3b。{#} 210E

This article will discuss 10 important questions that everyone interested in data science should consider before pursuing a career as a data scientist.{#9517}

本文将讨论在追求数据科学家职业之前,每个对数据科学感兴趣的人都应该考虑的10个重要问题。{#9517}

1. What does a data scientist do?{#92d0}

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. A data scientist works with data to draw out meaning and insightful conclusions that can drive decision making in an institution. Their job role includes data collection, data transformation, data visualization, and analysis, building predictive models, providing recommendations on actions to implement based on data findings. Data scientists work in different sectors such as healthcare, government, industries, energy, academia, technology, entertainment, etc. Some top companies that hire data scientists are Amazon, Google, Microsoft, Facebook, LinkedIn, and Twitter.{#592e}

数据科学是一个广泛的领域,包括数据准备和探索等几个细分领域;数据表示和转换;数据可视化和演示;预测分析;机器学习等数据科学家使用数据来绘制可以推动机构决策的意义和深刻见解。他们的工作职责包括数据收集,数据转换,数据可视化和分析,构建预测模型,根据数据结果提供有关实施的行动建议。数据科学家在医疗保健,政府,工业,能源,学术,技术,娱乐等不同领域开展工作。一些聘请数据科学家的顶级公司是亚马逊,谷歌,微软,Facebook,LinkedIn和Twitter。{#592e}

  1. How much do data scientists make? {#db7b}

How much you make as a data scientist depends on the organization or company you are working for, your educational background, number of years of experience, and your specific job role. Data scientists make anywhere from $50,000 to $250,000, with the median salary being about $120,000. This

您作为数据科学家所取得的成就取决于您所在的组织或公司,您的教育背景,经验年数以及您的具体工作角色。数据科学家的工资从50,000美元到250,000美元不等,工资中位数约为120,000美元。这个 article discusses more about the salaries of data scientists.{#c388}

讨论了有关数据科学家工资的更多信息。{#c388}

  1. What is the job outlook for data scientists? {#fb45}

The job outlook for data scientists is very positive. IBM predicts the demand for data scientists to soar 28% by 2020: https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/#7916f3057e3b.{#c640 }

数据科学家的就业前景非常乐观。 IBM预测到2020年数据科学家的需求将飙升28%:https: //www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar- 28按2020 /#7916f3057e3b。{#} C640

4. Do I have a solid background in an analytical discipline such as mathematics, physics, computer science, engineering or economics?{#7aa2}

A strong background in an analytic discipline is a plus. Data science is heavily math-intensive and requires knowledge in the following:{#e238}

分析学科的强大背景是一个优势。数据科学是数学密集型的,需要以下知识:{#e238}

a. Statistics and Probability{#0e1f}

一种。统计和概率{#0e1f}

b. Multivariable Calculus{#d10e}

湾多变量微积分{#d10e}

c. Linear Algebra{#6f1c}

C。线性代数{#6f1c}

d. Optimization Methods{#8ab3}

d。优化方法{#8ab3}

5. Do I love working with data and writing programs to analyze the data?{#683d}

Data science requires a solid programming background. The

数据科学需要扎实的编程背景。该 top 5 programming languages mentioned in most data science job listings (

在大多数数据科学工作列表中提到( The Most in Demand Skills for Data Scientists --- Towards Data Science ) are:{#8772}

) are:{#8772}

a. Python{#c11f}

a. Python{#c11f}

b. R{#68df}

b. R{#68df}

c. SQL{#3ca1}

c. SQL{#3ca1}

d. Hadoop{#cd51}

d. Hadoop{#cd51}

e. Spark{#3fad}

e. Spark{#3fad}

If you have not read this article:

如果您还没有读过这篇文章: "Teach Yourself Programming in Ten Years" by Peter Norvig (Director of Machine Learning at Google) , I encourage you to do so. Here is a link to the article: http://norvig.com/21-days.html . The point here is that you don't need ten years to learn the basics of programming, but learning programming in a rush is certainly not helpful. It takes time, effort, energy, patience and commitment to become a good programmer and data scientist.{#9e70}

,我鼓励你这样做。以下是该文章的链接:http: //norvig.com/21-days.html。这里的重点是,您不需要十年时间来学习编程的基础知识,但是学习编程的过程肯定没有用。成为一名优秀的程序员和数据科学家需要时间,精力,精力,耐心和承诺。{#9e70}

6. Do I enjoy solving challenging problems?{#65e3}

Data science problems are very challenging. A typical data science project would involve the following stages:{#c3e3}

数据科学问题非常具有挑战性。典型的数据科学项目将涉及以下阶段:{#c3e3}

a. Problem Framing{#8ba4}

一种。问题框架{#8ba4}

b. Data Collection and Analysis{#7ece}

湾数据收集和分析{#7ece}

c. Model Building, Testing, and Evaluation{#5280}

C。模型构建,测试和评估{#5280}

d. Model Application{#d638}

d。模型应用{#d638}

From problem framing to model building and application, the process could take weeks and even months, depending on the scale of the problem. Only individuals that are passionate about solving challenging problems would succeed as data scientists.{#57f3}

从问题框架到模型构建和应用,该过程可能需要数周甚至数月,具体取决于问题的规模。只有那些热衷于解决具有挑战性问题的人才能成为数据科学家。{#57f3}

  1. Am I patient enough to keep on working even when a project seems to have hit a roadblock? {#426e}

Data science projects could be very long and demanding. From problem framing to model building and application, the process could take weeks and even months, depending on the scale of the problem. As a practicing data scientists, hitting a roadblock with a project is something inevitable. Patience, tenacity, and perseverance are key qualities essential for a successful data science career.{#ae93}

数据科学项目可能非常漫长而且要求很高。从问题框架到模型构建和应用,该过程可能需要数周甚至数月,具体取决于问题的规模。作为一名实践数据科学家,与项目达成障碍是不可避免的。耐心,坚韧和毅力是数据科学事业成功的关键。{ae93}

  1. Do I have the business acumen that would enable me to draw out meaningful conclusions from a model that can lead to important data-driven decision making for my organization? {#227d}

Data science is a very practical field. Remember that you may be very good at handling data as well as building good machine learning algorithms, but as a data scientist, the real-world application is all that matters. Every predictive model must produce meaningful and interpretable results of real-life situations. A predictive model must be validated against reality in order for it to be considered meaningful and useful. Your role as a data scientist should be to draw out meaning insights from data that can be used for data-driven decisions that can improve the efficiency of your company or improve the way business is conducted, or help increase profits.{#5dd5}

数据科学是一个非常实用的领域。请记住,您可能非常擅长处理数据以及构建良好的机器学习算法,但作为数据科学家,真实世界的应用程序才是最重要的。每个预测模型都必须产生有意义和可解释的现实生活情境结果。必须根据现实验证预测模型,以使其被认为是有意义和有用的。您作为数据科学家的角色应该是从数据中提取意义洞察力,这些数据可用于数据驱动的决策,可以提高公司效率或改善业务运作方式,或帮助增加利润。{#5dd5}

  1. How long does it take to become a data scientist? {#afc9}

If you have a solid background in an analytical discipline such as

如果你在分析学科中有扎实的背景,比如 physics ,

, mathematics ,

, engineering ,

, computer science ,

, economics , or

, or statistics , you can basically teach yourself the basics of data science. You may start by taking free online courses from platforms like

,你基本上可以自学数据科学的基础知识。您可以从平台等免费在线课程开始 edX ,

, Coursera , or

, or DataCamp . It could take about a year or two of intensive studies to master the fundamentals of data science. Keep in mind that a strong foundation in data science concepts acquired from course work alone will not make you a data scientist. After establishing a strong foundation in data science concepts, you may seek an internship or participate in Kaggle competitions where you get to work on real data science projects. Another way to practice your data science skills is to showcase your projects using platforms such as Github, LinkedIn, or write data science articles on Medium. Here are some suggestions for writing data science articles on medium:

。可能需要一到两年的深入研究来掌握数据科学的基础知识。请记住,仅从课程作业中获得的数据科学概念的坚实基础不会使您成为数据科学家。在建立了数据科学概念的坚实基础之后,您可以寻求实习或参加Kaggle比赛,在那里您可以开展真正的数据科学项目。练习数据科学技能的另一种方法是使用Github,LinkedIn等平台展示您的项目,或者在Medium上编写数据科学文章。以下是在媒体上撰写数据科学文章的一些建议: Beginner's Guide to Writing Data Science Blogs on Medium

.{#99bd}

{#99bd}

  1. What are some resources for learning about data science? {#f939}

There are numerous resources for learning the basics of data science. Here are some:{#e75c}

有许多资源可用于学习数据科学的基础知识。以下是一些:{#e75c}

Data Science 101 --- A Short Course on Medium Platform with R and Python Code Included {#e8f4}

{#e8f4}

Professional Certificate in Data Science (HarvardX, through edX) {#1a3a}

{#1a3a}

Analytics: Essential Tools and Methods (Georgia TechX, through edX) {#0e7f}

{#0e7f}

Applied Data Science with Python Specialization (the University of Michigan, through Coursera) {#8f5a}

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In summary, we've discussed 10 important questions that everyone interested in pursuing a career in data science should consider.{#8f69}

总之,我们已经讨论了10个重要问题,每个人都有兴趣从事数据科学职业。{#8f69}

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