How to learn python data analysis

Python is an object-oriented, literal computer programming language invented by Guido van Rossum at the end of 1989. Because of its simplicity, easy to learn, free and open source, portability, scalability and other characteristics, Python is also called the glue language. The figure below shows the popular trends of major programming languages ​​in recent years. Python’s popularity has skyrocketed.

Recommended courses: Python Tutorial

##Image source Using Python to play with data

Data analysis is an important part of big data and plays an important role in more and more jobs. Python can be used Various Python libraries, such as NumPy, pandas, matplotlib and IPython, can effectively solve various data analysis problems. So how to learn Python data analysis?

Knowledge and skills required for python data analysis:

1. Python introduction, Python environment installation, Python experience

2. Python basics, Syntax, data types, branches, loops, judgments, functions

3. Python oop, multi-threading, io, socket, modules, packages, import control

4. Python regular expressions, Python Crawler implementation

5. Basics of determinants, transposition, matrix definition, matrix operations, inverse matrix, matrix decomposition, matrix transformation, matrix rank

6. Python implementation of common matrix algorithms

7. Principles and uses of commonly used algorithm libraries in Python, numpy, pandas, sklearn

8. Data loading, storage, format processing

9. Data regularization, drawing and visualization

Because Python has a very rich library, it is also widely used in the field of data analysis. Since Python itself has a very wide range of applications, this issue of Python Data Analysis Roadmap mainly describes the Python data analysis roadmap from the perspective of data analysis practitioners. The entire roadmap is planned to be divided into 16 weeks and about 120 days.

The main learning content includes four parts:

1) Understanding the Python working environment and basic grammar knowledge (including learning about regular expressions);

2) Data collection Relevant knowledge (python crawler related knowledge);

3) Data analysis learning;

4) Data visualization learning.

PYTHON learning path plan

Python data analysis is an important part of big data, in addition to In addition, if you want to master more advanced big data skills, you also need to master big data knowledge such as Java, Linux, Hadoop, Hive, Avro and Protobuf, ZooKeeper, HBase, Phoenix, Flume, SSM, Kafka, Scala, Spark, azkaban, etc. Skill!

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