In June 2019, I started my journey as a new blogger printed with a top coder platform. It has become a wild trip because I developed a series of posts on data science and how to do the basics with Python. In this post, I want to share some of the lessons I have learned in conducting a series.
Here there are lessons learned:
Blogging about Python and data science sharpen my programming skills. Because topcoder members and contestants rely on blogs to help them win their competition, I am careful not to just make my example code, but provide a broad documentation in the Jupyter notebook that complements the series. I have completed the Python Programming Certificate in Isothermal Community College and requires a way to keep my python skills. So, I made a question to the topcoder and now I’m a blogger on the platform. The best way to apply what you have learned in your Python course is to build the application and post it on GitHub. This is what I did when I developed the blog post in 2019.Data science online training.
Second, in the coding Jupyter notebook for my series, I learned art often debuged my code. The debugging code is the main skill for learning not only for data science, but for general software development. The train code can cause the model to predict the wrong results or not execute at all. The worst error that can be done by a programmer is a logic error, where your code goes correctly but produces wrong results. The wrong prediction model can cost the money of the organization, so for your interest to think about the code and periodically tweak for maximum effectiveness.
Third, I learned Jupyter notebooks sometimes superior to the idea programming to execute the data science code. This notebook, if carefully designed, can mimic real life scientific notebooks. Data programming is usually carried out with Jupyter because it is a practical environment for exploration, coding, and predictive model development. One of the popular Python distributions is Anaconda Python, which includes Jupyter Plus Spyder. By using Anaconda, programmers do not have to regulate their data science programming environment because distribution has most libraries needed for analysis.
Finally, I learned that blogging was hard work and needed an understanding of my audience. Do two blogs in Tensorflow 2.0 buy this house to me as a developer / blogger. To present a way of working with Tensorflow, I have to read the documentation and play with the sample code. Blogging is good and unsuccessful at the same time. It didn’t work because the examples I wanted to include in the posts did not work and I worked against the deadline. I finally post a blog post on steps for tensorflow analysis without being able to enter code examples. However, the blog post was successful, because it introduced a library to the TOPCODER community. Whatever makes the topcoder platform better for members, bloggers, and programmers are always appreciated
The fifth lesson I learned in blogging during 2019 was to participate in the December thrive challenges. While I got a published post, I struggled to work on another post because of the grammar to mark my posts for plagiarism, even though there were no content marked. I really have an award for all members responsible for checking our posts and ensuring we issue the best content on the thrive. The challenge shows me that I can play blog posts that are well developed, no matter the results.
Although not used specifically in data science, object-oriented analysis and design will be served. Object-oriented analysis and design is a way to set up programs around the module. The vocabulary used to describe data will be explained by using the sample class. The following are Wikipedia articles that explain the concept.
The blog series then looks at the following library with a sample program that shows this library: numpy, scipy, scicit-learn, hard, and tensorflow. This library forms the basic data science using Python.
I enjoyed my blogging trip with TopCoder in 2019 and I look forward to developing content in 2020. Because blog information 2020 suggested, I would develop content for general and developing blogs. A new approach for blogging will produce new and interesting content. In my subsequent developing post, I would like to introduce an exploration data analysis using Python, which is a step-by-step approach to analyzing data before applying the data science algorithm to develop the model.