Analyzing several parameters & conditions before jumping into Data Science career

Benjamin Obi Tayo Ph.D.

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}

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}

  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}

  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}

a. Statistics and Probability{#0e1f}

b. Multivariable Calculus{#d10e}

c. Linear Algebra{#6f1c}

d. Optimization Methods{#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}

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}

a. Problem Framing{#8ba4}

b. Data Collection and Analysis{#7ece}

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

d. Model Application{#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}

  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}

  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}

  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}

  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}

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

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

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

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

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