NIO announced its 2021Q2 financial report, and here are some of the highlights:
Vehicle sales were RMB7,911.8 million (US$1,225.4 million) in the second quarter of 2021, representing an increase of 127.0% from the second quarter of 2020 and an increase of 6.8% from the first quarter of 2021.
Vehicle marginii was 20.3%, compared with 9.7% in the second quarter of 2020 and 21.2% in the first quarter of 2021.
Total revenues were RMB8,448.0 million (US$1,308.4 million) in the second quarter of 2021, representing an increase of 127.2% from the second quarter of 2020 and an increase of 5.8% …
The content of this blog is just based on my own experience, which means it could be somewhat subjective. The main reason that I take the program is just because I want to consolidate and polish some knowledge in the data engineering field.
The Data Engineering Nanodegree program has five core pillars: Data Modelling, Cloud Data Warehouses, Data Lakes with Spark, Data Pipelines with Airflow, and the Capstone Project. The upcoming content will cover all of these pillars, respectively. In the end, I will express my view about it.
This module mainly introduces the concept of data modeling in…
Followed by its great earnings in Q3, NIO continuously providing a series of amazing results for its Q4 earnings:
Followed by its surprising earnings in Q2, NIO continues providing a series of amazing results for its Q3 earnings:
Reminiscences of a Stock Operator is a roman à clef written by Edwin Lefèvre. The whole book describes the life of the legendary stock speculator Jesse Livermore (Larry Livermore in this novel). I will summarise the book and share some of my reactions in a couple of blogs.
I was most interested in verifying whether I had observed accurately; in other words, whether I was right.
I think this is a very unique motivation for a kid who was only about 14 and started trading — most adults don’t even have such vision — people usually only trade for making…
NIO just released its Q2 earnings report and had the earnings call, and I will list some crucial information about this call below.
In the beginning, William gave the opening remarks below:
This post will share my experience about how to measure the performance of code in R. As an R programmer, you may have heard
apply() functions are usually more efficient than for-loops.
However, you may ask that how can we measure this efficiency and how can we quantify the performance of our functions?
One of the most direct ways to know how long your code runs is using
From the above results, the elapsed time to run function
for_fun is 0.13 seconds. However, to some extent, using