1, big data learning is really difficult. To some extent, being good at math is helpful for learning. In the understanding of many concepts, you can get started faster, but it doesn't mean that people with poor mathematical ability can't learn big data well. The knowledge related to big data learning is indeed related to mathematics, but there is no absolute relationship between mathematics and big data learning. Learning big data, a more important ability requirement is the requirement of logical ability.

2. Logical ability is not born, but is cultivated through the day after tomorrow. I am engaged in IT technology development related work. I have strong logical ability, and I can understand many things faster, so can big data.

3. What does big data need to learn, including Java, Linux, Hadoop, oozie, Web, Python, Hbase, Flink, Kafka, MR, HDFS, Yarn, Hive, spark, etc. Learning these knowledge is the real learning of big data technology, which is not directly related to mathematics, and there are many vocational learning routes for big data, with different positions and different professional skills.

4. At the level of data mining and model, the requirement for mathematical knowledge will be higher, but in practical work, it needs to be able to apply, rather than entangled in theory. The requirement here is to be able to apply, not to study knowledge for exams in high schools or universities. There is a huge difference between the two. Therefore, it is not difficult for scholars who want to develop in this direction to learn well as long as they focus on the required knowledge.