## How to Become Data Science expert ?

When we talk about a data science expert, we mean becoming a person who can go through a lot of data, patterns, etc. Discover the values hidden between them. The discovery of these patterns can increase the added value of a business.

But there are steps you can take to become a specialist in data mining. Naturally, the steps written in this article are not the only solution. However, a data science expert should follow these eight steps.

In the following article, we will explain these eight steps. Then one of the sub-domains of data mining is text mining.

Introduce and explain some of its applications in different areas.

**An example of the application of data science**

Suppose a company like Amazon (or similar examples) is an online retailer and online sales interface. Use their data to predict which products and how much each will sell in three months. Naturally, this forecast can greatly lead to the growth of this business and increase its profits.

The same simple prediction as above. It can help set up sales warehouses in different areas. And significantly reduce warehousing and logistics costs. Amazon, for example, predicts that laptop sales will increase in the Middle East at the beginning of the summer season. The company can ship that type of laptop by different merchant ships before it reaches the peak of demand. Transfer to your warehouse in the Middle East and deliver the product to the customer immediately when ordering. This will increase delivery speed and customer satisfaction and reduce shipping costs.

The people who produce this prediction system are data science experts in the example given. These people, also known as machine learning specialists or data miners, can build predictive and learning systems and help different parts of the business. Note that the terms “data mining” and “machine learning” may be interchangeable.

**1. Learn the basics of statistics and probability**

Statistics and probability are basic sciences require in many engineering activities. Data science is no exception, and in fact, data science owes itself to statistics and probability and scientists in this field. Many of the algorithms proposed in data mining and machine learning are based on statistics and probabilities. This can be a reason to say that statistics and probability science are the mothers of data-related sciences.

Of course, the question that some students ask is how much statistics and probability should be learnable in the field of data science? The answer to this question depends on the students’ interest in the area they need. Some students are more inclined to analyze data. These students naturally need to learn more about statistics and statistical analysis. But if a student is more focused on implementation and data engineering behaviors, the need for statistical topics is less felt. However, it is expectable that all students, regardless of their field of work, will be familiar with the basic topics of statistics and probability and its basic theories in data.

There are many resources for learning statistics and probabilities. For example, the book of statistics and engineering probabilities written by Dr. Nemat Elahi or the book of statistics and its application in management, which has been compiled in two volumes, are some of the good academic books in this field. Of course, these books are mostly academic, but they can be useful in learning statistics and reasonable probabilities because of the very good content. There are also various free online courses that you can use to learn statistics and probabilities.

**۲. Learn a programming language**

There is a lot of talk about the benefits of learning a programming language. Today, learning a programming language is a prerequisite for technological advancement in that discipline in many engineering disciplines. Data science is not separate from these disciplines. And any data science expert should be familiar with programming languages such as Python, R, or Java, which can implement machine learning algorithms and processes on an executable platform. There are also ready-made libraries in these programming languages that speed up the implementation of data mining processes.

**3. Learn the basics of matrices and linear algebra**

Many data mining algorithms are based on Linear Algebra. They make extensive use of matrices and matrix operations in their processes. In this regard, learning the basics of matrices and linear algebra helps understand the functionality of data algorithms. In some textbooks for teaching mining and machine learning, a chapter is usually devote to this topic, or they discuss matrices and linear algebra while teaching topics. But if we want to introduce a book in this field, we can refer to Mr. Avar Nering’s book on linear algebra.

**4. Learn the basics of data mining and machine learning**

Basic data mining and machine learning algorithms can be a solution to this field’s basic and classic problems. These algorithms can teach students a good view of problems and how to solve them. Algorithms and their diversity can contribute to the breadth of student knowledge and learning the basics with these algorithms. In learning data mining, the student should be familiar with different methods and algorithms of classification and clustering and be able to solve various problems in this field with their help. It should also be able to prepare and clean data for these algorithms according to its needs. In this section, the student should also be able to evaluate their models and compare different models and algorithms to find the best algorithm and model for their problem.

**5. Learn a variety of practical examples in the field of data mining**

Learning does not stay in mind except through practice and repetition. If you want to become an expert in this field, you must test various algorithms on various datasets and see the results. Observing different examples and how to solve them can deepen the pattern of data mining problem-solving in the student’s mind. To become an expert in this field, there are various companies and institutions where you can do internships or solve their problems. For example, we can mention the Kaggle site, which has been able to be a good reference for real examples in data mining by holding numerous competitions. By referring to and reading the real-world data on this site, the student’s mind can quickly think data-driven and solve the problem according to the existing structure.

**6. Neural networks and deep learning**

Neural Networks and Deep Learning have enhanced the quality of data mining outputs and attracted the attention of many people and scientists in the field. In data mining, students can solve far more complex problems and improve the quality of different problems by using deep neural networks and various deep learning methods. These algorithms can learn more complex patterns in data and have gradually become one of the mainstays in solving data mining problems.

**7. Learning the specialized subfields of data science**

There are several areas such as Text Mining, Image Mining, Video Mining, Voice Mining, Working on Economic Data, and two other subfields of data mining. After learning algorithms, students can select one or more sub-domains as specialized sub-domains and focus on issues related to that sub-domain. Also, a data science specialist usually finds the necessary expertise in one of these sub-fields and identifies and solves the more complex problems of each sub-field well.

**8. Learning advanced algorithms and methods such as reinforcement learning and applied optimization methods**

Reinforcement Learning, combined with deep learning techniques, can solve more advanced problems. Learning these techniques allows the student to solve more advanced problems in a dynamic environment.

**Text mining**

In the following, to consolidate learning and create a vision of a career future in this field, we will deal with one of the sub-domains of data mining**: text**** mining**. Text mining or natural language processing (NLP) is one of the data science and data mining subfields. Many companies in data mining have focused on text mining and extracting patterns from text. In-text mining, the focus is on textual data: everyday texts composed of different words (such as words in Persian or English).

A large amount of data produced by modern man has been collected in the form of text, and this in itself has created valuable and rich content and, consequently, complex patterns among textual data. But how can these valuable patterns be extracted from data using new tools, such as computers and supercomputers? The answer to this question gave rise to the field of **text mining**, and many scientists began to work on textual data.

Different methods are presented in the field of text, each of which is used for one or more issues in this field. These algorithms are commonly implemented by popular programming languages such as Python or Java, and some of them have been used in large businesses.

**Analyze users’ feelings by text mining**

For example, suppose a business such as Google Play (or its internal counterparts) can use text mining algorithms to evaluate user input in each application and the quality or poor quality of each software (depending on the dynamic analysis of each Which comments) to be informed. This **Sentiment Analysis** can also be much more accurate or advanced. Suppose each of the texts contains comments about a piece of software. For example, someone said that “this application looks good, but its speed is low.” Advanced algorithms and hybrid methods of emotion analysis can detect this separation in particular software. Text-based algorithms and methods can analyze texts like humans.

**Search through a multitude of texts by text mining**

Another problem that text mining seeks to solve is searching through many texts. Building search engines like Google or Yandex are among these issues. Grouping different texts and pages and fetching the right content from many texts can help in a very fast search through a multitude of contents and increase the quality of the search. These algorithms can analyze and understand the texts on a page. For example, if the page is about “mobile games,” these search engines know that on this page, you can find content such as “games,” “mobile,” “software,” “iTee,” and so on, so search engines put the page next to They place pages that work in the same fields and display these pages to the user when searching.

**Set up an automated ticketing response system supported by text mining**

Or, for example, suppose you have a system in which different people, using the support system, send different tickets and requests to different units of a company. These tickets must be sent to the relevant unit. An intelligent system using text mining algorithms can automatically send a support ticket to the relevant unit. Also, in more advanced mode, generate an auto-reply and send it to the user. Many companies have come up with valuable data in their Q&A. Given the relationships they have had with their customers over the years. For example, many customers chat with the operator by text every day. These chats can be valuable data that the algorithm learns from these questions and answers. From now on, the algorithm can automatically give useful and instructive answers to users’ questions.

**Investment risk management by text mining**

Another application of text mining can be considered investment risk management. Large investment companies can analyze news and articles in the official newspapers of companies to gain important and valuable points for investing. For example, the algorithm may be based on news it has learned in the past from news texts, that whenever it sees news about the import of a particular product, after a week, the shares of a particular company increase. According to these patterns and trends that it recognizes, the algorithm gives the ability to offer investment in a particular company so that this particular company will gain a lot of profit for the owners of capital.

**Online crime detection by text mining**

Text mining can also play an important role in detecting online crime. For example, thieves who hunt their prey through cyberspace can have special patterns in chats or comments on social networks. Cyber security policy in any country can identify these patterns and deal with them legally by intelligently monitoring virtual networks.

**Smart online advertising by text mining**

Another area in which text analysis can play an effective role is smart online advertising. By analyzing the pages on which their ads are located, advertising companies can understand the content of a web page and display an ad that is relevant to the topic on that page. For example, one page might contain information about an “electronic kit.” The smart advertising engines that are placed by the site administrator and have access to that page try to display the most relevant ad to the user.

**Conclusion:**

Steps are required to become a data science expert in data mining. This article has explained the eight steps to becoming a data science expert, but the path mentioned above is not the only path available. And each student can go through different paths according to their interests and abilities, but the path and roadmap are stated. The above is one of the ways that seem to have attracted the attention of many scientists in the field of science.

In the continuation of the article, we introduced one of the sub-domains of data mining, namely text mining, and explained some text mining applications. Of course, text mining and natural language processing are not limited to the mentioned cases. We created and corrected texts, created analytical texts, created subtitles, composed texts to create new texts, and automatically sorted documents.

Discovering hidden relationships between articles, creating chatbots (chatbots), and many other things can be other text mining applications. If you have any questions or comments about this article, please share them with SunLearn users in the comments section and us.