Is java the best way to do data analysis?

Not really. Data analysis can be done with any computer language, but because of the characteristics of Python and Python's extended ecosystem (there are many expansion packages) more people choose to use Python, especially the panda library.

Supplementary information:

Everyone who does data analysis knows that the first step in starting a project is to establish a project and import data, so how can data analysts advance and learn to use it better? Data sets are very important. To this end, the editor has carefully compiled nine public data science project data sets for everyone to create projects.

What is a data set?

Many friends don’t know what a data set is. A data set is actually a collection of data, also known as a data set, a data set or a data set. For example:

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This is a set of data. It covers certain information about a specific commodity, with each column representing a specific variable. Each row corresponds to a question for a certain member of the data set. Each numerical value is called a datum. Corresponding to the number of rows, the data for this dataset may include one or more members. This specific information will play a key role in the data reporting we need to do.

Using these data sets for analysis is very helpful for data analysts to advance.

What public data sets are available for practice?

1.ImageNet data set:

The ImageNet data set is mainly used in the fields of machine learning and computer vision research. Each record contains a bounding box and corresponding class label. ImageNet provides 1000 images for each synonym set, and you can view the image URL directly in ImageNet.

2.COCO Dataset:

The COCO Dataset is a large-scale object detection, segmentation and subtitles data set, which collects data through extensive use of Amazon Mechanical Turk. This dataset has 1.5 million object instances for 80 object categories.

3. Iris data set:

The iris data set is a data set specially designed for beginners. With this data, novices can build simple projects using machine learning algorithms. It is worth mentioning that all attributes in this dataset are real. The size of the iris data set is small, so novices do not need to preprocess the data.

The so-called preprocessing means organizing and clearing the data before processing it. For example, you are cooking and you want to find pepper and sprinkle it into the pot. But you put all the ingredients together, and if you're not lucky it will take you a long time to find the pepper. After finding it, you are about to pour it into the pot, but you find that the dish has become mushy. Therefore, we need to arrange the ingredients neatly in advance so that it will be more convenient when cooking.

4. Breast Cancer Wisconsin (Diagnosis) Dataset:

The Breast Cancer Wisconsin (Diagnosis) Dataset is one of the most popular datasets in machine learning. This dataset is based on an analysis of breast cancer.

5.Twitter Sentiment Analysis Dataset:

Sentiment analysis is one of the most common applications in natural language processing (NLP). You can use the Twitter sentiment analysis dataset to build sentiment analysis-based models.

As we all know, our Comrade Trump can be said to be Twitter's resident "crosstalk actor". Maybe you can still browse his comments~

6. MNIST data set:

< p>MNIST dataset is built on handwritten data. This dataset is easy to use for beginners and helps to understand techniques and deep learning to identify patterns on real data. You don’t need to spend much time preprocessing the data. For beginners who are keen on deep learning or machine learning, the MINIST dataset is a great choice.

7.Fashion MNIST data set:

The Fashion MNIST data set is built on clothes data and can be used for deep learning image classification problems and machine learning. This dataset is easy to use for beginners and you don’t need to spend much time on data preprocessing. At the same time, the FashionMNIST data set can help you understand and learn the technology on actual data and ML technology and pattern recognition methods in deep learning.

8. Amazon Review Dataset:

The Amazon Review Dataset is also a data set used for NLP (natural language processing). With the help of the Amazon review data set, you can not only understand the substantive problems that will arise in your business, but also understand the sales trends of various products in recent years. Maybe if you study it, you can open an online store.

9. Spam SMS Classifier Dataset:

The Spam SMS Classification Dataset can help you predict spam emails. With the help of spam SMS classification data set, novices can build simple projects using machine learning classification algorithms. Not only that, you can also learn why your mobile phone can automatically identify spam text messages, which is a bit magical when you think about it~