Use the first-principle method to increase your adds of success through compounding of incremental successful efforts
As a lifelong learner, I am constantly challenging myself to learn something new. In today’s world of information technology, there are unlimited learning resources on virtually any discipline or specialty. For me, the best place where I go to took for great courses to broaden my knowledge and to learn something new is massive open online courses (MOOCs).
MOOCs for the most part, are free online courses available for anyone to enroll. MOOCs provide an affordable and flexible way to learn new skills, advance your career and deliver quality educational experiences at scale. MOOCs cover a broad spectrum of online courses in leadership, analytics, data science, machine learning, professional skills, engineering, business & management, humanities, computer science, and much more. These courses are usually offered by top universities across the world like MIT, Harvard, UC Berkeley, University of Michigan, EPFL, Hong Kong Polytechnic University, The University of Queensland, and much more. Some courses are also offered by big corporations such as IBM, google, and Microsoft. The greatest advantage of MOOCs is the opportunity to learn from leaders and experts, and the privilege of taking courses from the world’s top universities. The most popular providers of MOOCs include the following:
a) edx: https://www.edx.org/
b) Coursera: https://www.coursera.org/
c) DataCamp: https://www.datacamp.com/
d) Udemy: https://www.udemy.com/
e) Udacity: https://www.udacity.com/
f) Lynda: https://www.lynda.com/
With all the tons of courses on these platforms, it may be overwhelming to choose what courses to pursue and how to work hard towards completing these courses. A major hindrance for online learners is procrastination and lack of self-discipline.
In this article, we will discuss how to successfully overcome procrastination and lack of self-discipline.
First, you need to select what fields you would like to explore. Then you have to choose the right courses that would enable you to reach your educational goals. Once you have these courses set out, then you need to plan on how to complete the courses.
This is where the first-principle approach comes in. You need to break your goal into small incremental steps. As you work hard towards achieving each of these incremental steps, you will increase your odds of success. Keep in mind that a journey of a thousand miles begins with a single step.
Let’s consider a case study.
Case Study: Professional Certificate in Data Science (HarvardX, through edX)
Includes the following courses, all taught using R (you can audit courses for free or purchase a verified certificate):
1. Data Science: R Basics
Duration: 8 Weeks
Recommended Effort: 1–2 hours per week
2. Data Science: Visualization
Duration: 8 Weeks
Recommended Effort: 1–2 hours per week
3. Data Science: Probability
Duration: 8 Weeks
Recommended Effort: 1–2 hours per week
4. Data Science: Inference and Modeling
Duration: 8 Weeks
Recommended Effort: 1–2 hours per week
5. Data Science: Productivity Tools
Duration: 8 Weeks
Recommended Effort: 1–2 hours per week
6. Data Science: Wrangling
Duration: 8 Weeks
Recommended Effort: 1–2 hours per week
7. Data Science: Linear Regression
Duration: 8 Weeks
Recommended Effort: 1–2 hours per week
8. Data Science: Machine Learning
Duration: 8 Weeks
Recommended Effort: 2–4 hours per week
9. Data Science: Capstone
Duration: 2 Weeks
Recommended Effort: 15–20 hours per week
Conservative estimate of number of hours required to complete all courses in the certificate program
If we assume a conservative estimate of 2 hours per week effort for courses 1 through 7, this would give a total of (2 x 8 x 7 = 112 hours) since these courses last for 8 weeks.
The machine learning course (course 8 in the sequence) requires 2–4 hours per week effort. If we use 3 hours per week as a conservative value, then we have (3 x 8 = 24 hours).
Finally, for the capstone course (course 9), we can use 20 hours per week effort to obtain (20 x 2 = 40 hours)
The estimated total effort for completing the specialization is 176 hours.
If you are willing to invest 30 minutes everyday in this program, then you would complete the specialization in 352 days, which is approximately 1 year.
If you don’t have 30 minutes, you may decide to invest 15 mins per day, but this would double the completion time to 2 years.
What if you can only afford 10 minutes of time per day for this program, then you’ll complete the specialization in 3 years.
The bottom line is that it doesn’t matter if you invest 1 hour per day, or 30 minutes per day or 10 minutes per day, the point is to choose whatever works for you but you have to be consistent and stay focused to reach your goal.
Summary
The case study presented here is just to illustrate the power of compounding. Even though we used a case study from data science, the technique of breaking a large goal into small incremental goals is applicable to any discipline. If you are willing to invest small amounts of time consistently, you’ll be amazed by what you can accomplish. These small investments of your time will increase your odds of success in accomplishing whatever goal you set for yourself. Even if you can’t complete the entire specialization by the end of the year, why not make it a goal to complete 3 or 4 of the 9 courses this year. This might give you the momentum and energy to work towards completing the remaining courses in the specialization.
Timeline for Data Science Competence
Data Science Curriculum
Essential Maths Skills for Machine Learning
3 Best Data Science MOOC Specializations