Data Science Preliminaries
Use these questions to determine if data science is the right field for you
Data science skills have become increasingly more important for jobs that once had little to do with statistics, including marketing and business. Adding data science skills to your portfolio will give you an edge in your current role in the market this year. Before getting into data science, use the questions below to determine if data science is the right field for you.
1. What is data science?
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, deep learning, artificial intelligence, etc. There are three levels of data science competency, namely: level 1 (basic level); level 2 (intermediate level); and level 3 (advanced level). Competency increases from level 1 to 3, as shown in Figure 1 below.
2. How long does it take to gain basic skills in data science?
Level 1 competency can be achieved within 6 to 12 months. Level 2 competencies can be achieved within 7 to 18 months. Level 3 competencies can be achieved within 18 to 48 months. We remark here that these are approximate values only. The amount of time required to gain a certain level of competence depends on your background and how much amount of time you are willing to invest in your data science studies. Typically, individuals with a background in an analytic discipline such as physics, mathematics, science, engineering, accounting, or computer science would require less time compared to individuals with backgrounds not complementary to data science.
3. What does a data scientist do?
A data scientist works with data to draw out meaning and insightful conclusions that can drive decision making in an institution or organization. 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, Twitter, Netflix, IBM, etc.
4. 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. A recent study using the LinkedIn job search tool shows that a majority of top tech jobs in the year 2020 are jobs that require skills in data science, business analytics, machine learning, and cloud computing (see Figure 2 below).
5. 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 the salaries of data scientists.
6. How can I prepare for a career in data science?
Most data science or business analytics programs require the following:
a) A high level of quantitative ability
b) A problem-solving mindset
c) Programming proficiency
d) The ability to communicate effectively
e) Ability to work in a team
Hence to prepare for a career in data science, you may start by pursuing a bachelor’s degree in a quantitative discipline such as science, technology, engineering, mathematics, business, or economics.
7. What programming languages should I focus on?
If you are interested in learning the fundamentals of data science, you need to start from somewhere. Do not be overwhelmed by the ridiculous list of programming languages mentioned in data scientist job ads. While it is important to learn as many data science tools as possible, it’s recommended to start from just one or two programming languages for a start. Then once you’ve built a solid background in data science, you can then challenge yourself to learn about different programming languages or different platforms and productivity tools that can enhance your skill set. According to this article, Python and R are still the top two programming languages used in data science. I would recommend starting with Python as more and more academic training programs and industries are using it as the default language for data science.
8. What are some resources for learning about data science?
You may pursue a master’s degree in data science or in business analytics if your circumstances allow you to do that. If you can’t afford a master’s degree program, you may pursue the self-study route for learning about data science. Generally, if you have a solid background in an analytic discipline such as physics, mathematics, economics, engineering, or computer science, and you are interested in exploring the field of data science, the best way is to begin with massive open online courses (MOOCs). Then after establishing a solid foundation, you may then seek other ways to increase your knowledge and expertise such as studying from textbooks, engaging in projects, and networking with other data science aspirants.
Find below are recommended MOOCs and textbooks that can help you master the fundamentals of data science.
Learning from a textbook provides a more refined and in-depth knowledge beyond what you get from online courses. This book provides a great introduction to data science and machine learning, with code included: “Python Machine Learning”, by Sebastian Raschka. https://github.com/rasbt/python-machine-learning-book-3rd-edition
The author explains fundamental concepts in machine learning in a way that is very easy to follow. Also, the code is included, so you can actually use the code provided to practice and build your own models. I have personally found this book to be very useful in my journey as a data scientist. I would recommend this book to any data science aspirant. All that you need is basic linear algebra and programming skills to be able to understand the book. There are also lots of other excellent data science textbooks out there such as “Python for Data Analysis” by Wes McKinney, “Applied Predictive Modeling” by Kuhn & Johnson, “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank & Mark A. Hall, and so on.
Summary and Conclusion
In summary, we’ve discussed 8 important frequently asked questions from data science aspirants. The journey to data science might be different for different individuals based on their backgrounds, but the answers provided in this article can provide some guidance to individuals considering the field of data science.