foreword
What is data in data analysis?
Data are different pieces of information, usually formatted in a particular way.
Data is measured, collected, reported, and analyzed, so it is often visualized using graphs, images, or other analytical tools. Raw data ("raw data") may be a collection of numbers or characters before they are "cleaned" and corrected by the researcher. It has to be corrected so we can remove outliers, instrumentation or data entry errors.
Data processing is usually carried out in stages, so "processed data" from one stage can also be considered "raw data" for a subsequent stage. Field data is data collected in an uncontrolled "field" environment. Experimental data are data generated from observations made in scientific investigations.
Data can be generated by:
- Humanity
- machine
- Man-machine combination
It can generally be generated anywhere any information is generated and stored in a structured or unstructured format.
【----Help data analysis and learning, all the following learning materials are free at the end of the article! ----】
Why is data important?
- Data helps to make better decisions.
- Data helps to solve problems by finding the reasons for poor performance.
- Data helps assess performance.
- Data helps improve processes.
- Data helps understand consumers and markets.
type of data?
In general, the data can be divided into two parts:
- Categorical data:
In categorical data, we see data with defined categories such as:
- marital status
- political party
- eye color
- Numeric data:
Numerical data can be further divided into two categories:
- Discrete data:
Discrete data contains data with discrete values, such as the number of children, pageviews per hour, etc. - Continuous Data:
Continuous data contains data with continuous values such as weight, voltage, etc.
At a high level, we can further divide the data into four parts:
- Nominal Scale:
Nominal scales divide data into several distinct categories with no implied ranking criteria. For example, gender, marital status. - Ordinal scales classify data into different categories, during which ranking is implied. For example:
- Teacher Level: Professor, Associate Professor, Assistant Professor
- Student grades: A, B, C, D, E, F
3. Interval scale:
An interval scale may be an ordinal scale during which the difference between measurements is a meaningful quantity, but the measurements do not have a true zero point. For example:
- Fahrenheit and Celsius
- years
4. Scale bar:
A ratio scale may be an ordinal scale during which the difference between measurements is a meaningful quantity, so the measurements have a true zero point. Therefore, we can perform arithmetic operations on data at a real scale. For example: weight, age, salary, etc.
Why use python for data analysis?
Python has been gaining popularity over the past few years, and recent surveys have shown the same, with Python ranking among the top programming languages in both the TIOBE index and the PYPL index. However, in support of this, there are five specific reasons behind it.
- Easy to learn : As an open source platform, Python has a simple and intuitive syntax that is easy to learn and read. This makes it a great language for beginners to learn data science.
- Cross-platform : As a developer, you don't need to worry about data types. The reason is, Python allows developers to run code on Windows, Mac OS X, UNIX, and Linux.
- Portability : Being a simple and beginner-friendly programming language, Python is highly portable in nature, which means that developers can run their code on different machines without making any further changes .
- Extensive Libraries : Python has several powerful libraries that make data analysis and visualization easy. Pandas is a data manipulation and analysis library, NumPy is a numerical computing library, and Matplotlib is a data visualization library.
- Community support : Python has a large and active community that supports and facilitates the development of various data science libraries and tools. This community has created many useful libraries, including Pandas, NumPy, matplotlib, and SciPy, which are widely used in data science.
However, there are many reasons to choose Python for data analysis, such as OOP, expressive language, ability to dynamically allocate memory, etc. That is why the Python programming language is used for data analysis.
1. Learning routes in all directions of Python
Just started learning python, if you don't even plan the complete learning steps, it is basically impossible to learn python. He sorted out all the directions of Python to form a summary of knowledge points in various fields.(The picture is too big, I can’t put it here, if you don’t have a full version, you can get it for free at the end of the article)
Some hard skills needed to engage in data analysis, such as how to use python, SQL and other tools!
2. Getting started with a full set of learning videos
When we watch videos and learn, we can’t just move our eyes and brain without using our hands. A more scientific learning method is to use them after understanding. At this time, the hands-on project is very suitable.
Three, Python operation example
Learning python is the same as learning mathematics. You can’t just read the book without doing the questions. Looking directly at the steps and answers will make people mistakenly think that you have mastered everything, but you will still be at a loss when you encounter a problem.
Therefore, in the process of learning python, you must remember to write more codes by hand. You only need to read the tutorial once or twice.
4. Python employment project actual combat
We must learn Python to find a high-paying job or a high-paying part-time job. The following are some practical projects that companies can use. After learning these, I believe everyone will be able to find a satisfactory job.
11 Django framework
16 WeChat public account
18 Common crawler module usage
…
21 Data Analysis
22 Machine Learning
There are other things, such as my own Python introductory graphic tutorials, you can use your mobile phone to learn knowledge when you don’t have a computer, and after learning the theory, you can type the code to practice verification, and there is also the library information of the Chinese version of Python. , MySQL and HTML tags, etc., these are things that can be given to fans.
Data collection
These are not very valuable things, but they are really good for learners who have no resources or the resources are not very good. If you can use it, you can scan the QR code of CSDN official certification below on WeChat [free access]↓↓↓ .
Good article recommendation
Understand the prospect of python: https://blog.csdn.net/SpringJavaMyBatis/article/details/127194835
Learn about python's part-time sideline: https://blog.csdn.net/SpringJavaMyBatis/article/details/127196603