We are living in the industry where data is necessary for everything, thus, related-data jobs are extremely hot now. One of the most famous jobs on the market which is chosen by many students is data science. Since I am also studying Data Science at my university now, I am going to share what you will learn if you choose to study this field.
"What is the good reason for doing this?" - this is the question that I always ask myself before doing or trying something. For me, nothing exists without a purpose. So, I think you should ask yourself this question as well. This is because I see many people work hard on something for a long time, and they forgot the initial purpose for doing that. This leads to unhappiness and regret. Therefore, I am going to use my story of how I learned to code to let you know the importance of asking this question.
I have set my goals in 2020 and I want to share some of them to my social networks. By doing this, I want to know how my social networks impact on my goals. Also, I want to see how I can help them achieve their goals in 2020.
I share my efficient ways on how I learn. Being proactive. Connect everything that I learned. Emrace failure as a learning opportunity. Learn for my knowledge as well as my communication. Achieve a big thing by learning small things.
In this post, I share the books that grew my mind and personality in 2019. Things that I learned from these books are business stories, motivation, leadership, innovation, communication, etc.
This post shows my volunteering experience this year. I learned many things in different aspects. Also, I share my great experience here.
This post shows you my studying progress in the second year at Swinburne Unibersity, Hawthorn, Melbourne, VIC, Australia. Also, I share my studying methods to achieve my current GPA.
Nowadays, we can vectorize everything such as numbers, words, sentences, etc. Why do we need to vectorize everything? We want to make everything countable and measurable so that we can apply many complicated statistical algorithms for it. That is why vectorization is one of the most important things in feature engineering. However, we do not discuss further those concepts in this notebook. Instead of that, this notebook shows how we calculate the distance between two vectors/observations by using different distance techniques.
It is clear that different normalization will be used for different purposes with different datasets. It is possible to use only one normalization technique for a particular dataset. Also, it is possible to use mixed normalization techniques (more than 2) for a different dataset. This notebook shows how we deal with the numeric data with different scale by using normalization techniques.
Last week, I attended the IoT & Industry 4.0 networking event which was held by Swinburne Engineering Student's Society (SESS) club. This is one of the most valuable networking events I have ever attended because I learned many things from the event. Therefore, I can't wait to share with you things that I learned from the event.