10 Questions to Consider Before Pursuing a Career in Data Science

Analyzing several parameters & conditions before jumping into Data Science career

Benjamin Obi Tayo Ph.D.
Sep 15 · 5 min read

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:

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

1. What does a data scientist do?

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.

2. How much do data scientists make?

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 article discusses more about the salaries of data scientists.

3. What is the job outlook for data scientists?

The job outlook for data scientists is very positive. IBM predicts the demand for data scientists to soar 28% by 2020:

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

A strong background in an analytical discipline is a plus. Data science is heavily math-intensive and requires knowledge in the following:

a. Statistics and Probability

b. Multivariable Calculus

c. Linear Algebra

d. Optimization Methods

5. Do I love working with data and writing programs to analyze the data?

Data science requires a solid programming background. The top 5 programming languages mentioned in most data science job listings (The Most in Demand Skills for Data Scientists — Towards Data Science) are:

a. Python

b. R

c. SQL

d. Hadoop

e. Spark

If you have not read this article:Teach Yourself Programming in Ten Years” by Peter Norvig (Director of Machine Learning at Google), I encourage you to do so. 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.

6. Do I enjoy solving challenging problems?

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

a. Problem Framing

b. Data Collection and Analysis

c. Model Building, Testing, and Evaluation

d. Model Application

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.

7. Am I patient enough to keep on working even when a project seems to have hit a roadblock?

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.

8. 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?

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 to be considered meaningful and useful. Your role as a data scientist is 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.

9. How long does it take to become a data scientist?

If you have a solid background in an analytical discipline such as physics, mathematics, engineering, computer science, economics, or statistics, you can basically teach yourself the basics of data science. You may start by taking free online courses from platforms like edX, Coursera, 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: Beginner’s Guide to Writing Data Science Blogs on Medium.

10. What are some resources for learning about data science?

There are numerous resources for learning the basics of data science. Here are some:

Data Science 101 — A Short Course on Medium Platform with R and Python Code Included

Professional Certificate in Data Science (HarvardX, through edX)

Analytics: Essential Tools and Methods (Georgia TechX, through edX)

Applied Data Science with Python Specialization (the University of Michigan, through Coursera)

In summary, we’ve discussed 10 important questions that everyone interested in pursuing a career in data science should consider.

Towards AI

Towards AI, is the world’s fastest-growing AI community for learning, programming, building and implementing AI.

Benjamin Obi Tayo Ph.D.

Written by

Physicist, Data Scientist, Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Predictive Analytics, Materials Science, Bioinformatics

Towards AI

Towards AI, is the world’s fastest-growing AI community for learning, programming, building and implementing AI.

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