登录查看更多内容

You’re looking to start a career in data mining. What hard skills do you need to have?

由人工智能和领英社区提供技术支持

Data mining is the process of extracting valuable insights from large and complex datasets using various techniques and tools. It is a highly sought-after skill in many industries, such as finance, marketing, healthcare, and e-commerce. If you are interested in pursuing a career in data mining, you need to have some hard skills that demonstrate your ability to handle data and solve problems. Here are some of the essential hard skills you need to have as a data miner.

此文章中的业界达人

由社区从 7 条内容中精选。了解更多

TARIQ EL YASSOURI

What’s “Customer Centricity” in Arabic? Redefined here in Saudi Arabia ????. Lost in translation?! Be in touch and find…

1 Programming languages

You need to be proficient in at least one programming language that can handle data manipulation, analysis, and visualization. Python and R are the most popular choices for data mining, as they have many libraries and packages that support data mining tasks. For example, you can use pandas, numpy, and scikit-learn in Python, or dplyr, tidyr, and caret in R, to perform data preprocessing, feature engineering, and machine learning. You should also be familiar with SQL, which is a standard language for querying and managing relational databases.

添加您的观点

TARIQ EL YASSOURI

What’s “Customer Centricity” in Arabic? Redefined here in Saudi Arabia ????. Lost in translation?! Be in touch and find out more.
举报内容
Yes, Python and R are indeed popular choices for data mining due to their extensive libraries and packages tailored for data manipulation, analysis, and visualization. Both languages offer a wide range of tools and frameworks, making them well-suited for various data mining tasks, from preprocessing and exploration to modeling and visualization. Choosing either Python or R depends on factors such as personal preference, specific project requirements, and the existing ecosystem within your organization or community.

已翻译

赞
Wilfredo Alpirez

Senior Data Engineer |Data Scientist|AWS|Azure|Backend Engineer|Python|Snowflake|SQL|LLMS|Data Driven|
举报内容
Para desarrollar habilidades en el manejo, análisis y visualización de datos, es fundamental dominar al menos un lenguaje de programación. Python y R son las opciones más populares para la minería de datos debido a su amplia gama de bibliotecas y paquetes especializados. Por ejemplo, en Python, puede utilizar bibliotecas como pandas, numpy y scikit-learn para tareas de preprocesamiento de datos y aprendizaje automático. En R, las bibliotecas dplyr, tidyr y caret son ampliamente utilizadas para realizar tareas similares. Además, es importante estar familiarizado con SQL, un lenguaje estándar para consultar y administrar bases de datos relacionales.

已翻译

赞
Muhammad Yusuf R.

?? Business Intelligence / Data Analyst at Nanovest | ?? Backend Engineer
举报内容
In the field of data mining, proficiency in certain programming languages is critical due to the nature of the work, which involves data extraction, preprocessing, analysis, and the derivation of insights from large datasets (aside from basic SQL understanding of course). Python or R are indeed the most popular choices for data mining. Based on my experience, if you're just starting to jump into the industry, Python would be your optimal choice, since the community is larger compared with R hence you could acquire help from the community (Kaggle, StackOverflow, and etc.).

已翻译

赞

2 Statistics and mathematics

You need to have a solid foundation in statistics and mathematics, as they are the basis of data mining methods and algorithms. You should be able to apply statistical concepts, such as descriptive statistics, inferential statistics, hypothesis testing, and confidence intervals, to understand and interpret data. You should also be able to use mathematical techniques, such as linear algebra, calculus, and optimization, to perform calculations and operations on data. Additionally, you should be aware of the assumptions, limitations, and trade-offs of different data mining models and methods.

添加您的观点

Muhammad Yusuf R.

?? Business Intelligence / Data Analyst at Nanovest | ?? Backend Engineer
举报内容
Data mining is about uncovering patterns, anomalies, and correlations within large sets of data to predict outcomes and make data-driven decisions. A solid grounding in specific areas of statistics and mathematics is crucial, as these subjects provide the tools necessary for analyzing data effectively. Based on my experience, you would need a strong understanding of statistics since it helps you to do cluster analysis, identify patterns, and make data-driven decisions. Mathematics particularly in areas such as linear algebra and calculus, underpins your understanding of algorithms used in data mining. Imagine you're on a treasure, Statistics is like your map hunt that shows patterns and Mathematics is like your treasure-hunting tools.

已翻译

赞

3 Machine learning and data mining techniques

You need to have a good knowledge of machine learning and data mining techniques, as they are the core of data mining applications. You should be able to select, implement, and evaluate appropriate techniques for different types of data mining problems, such as classification, regression, clustering, association rule mining, anomaly detection, and text mining. You should also be able to use various tools and frameworks, such as TensorFlow, PyTorch, or Weka, to build and deploy data mining solutions. Moreover, you should be able to keep up with the latest trends and developments in the field of data mining.

添加您的观点

4 Data visualization and communication

You need to have a strong skill in data visualization and communication, as they are the key to presenting and explaining your data mining results to stakeholders and clients. You should be able to use various tools and platforms, such as Tableau, Power BI, or Matplotlib, to create clear, attractive, and interactive data visualizations that highlight the main findings and insights from your data mining projects. You should also be able to communicate your data mining process, methods, and outcomes effectively and convincingly, using appropriate language, terminology, and storytelling techniques.

添加您的观点

??Muhammad Talha???

Data Scientist | & | CS | at Stanford | AI, ML Engineer | Entrepreneur | Founder @HalotStore | Making Strategic Choices
举报内容
Data visualization and communication are crucial aspects of conveying insights effectively from data. They involve presenting data in a visual format, such as charts, graphs, and maps, to make complex information more understandable and accessible to a wide audience. Effective data visualization not only enhances understanding but also facilitates decision-making and drives action based on insights derived from the data. It's important to choose the right visualization techniques based on the type of data and the message you want to convey, and to ensure clarity, accuracy, and relevance in your visualizations. Additionally, effective communication of data involves telling a compelling story, providing context

已翻译

赞
Muhammad Yusuf R.

?? Business Intelligence / Data Analyst at Nanovest | ?? Backend Engineer
举报内容
Firstly, proficiency in data visualization tools and libraries, such as Tableau, Microsoft Power BI, and programming libraries like Matplotlib, Seaborn, and ggplot2 for Python and R, is crucial for effectively presenting data in an understandable and visually appealing manner. Based on my experience, developing a strong foundation in both data visualization and communication requires a multifaceted skill set that bridges technical proficiency with storytelling.

已翻译

赞

5 Domain knowledge and business acumen

You need to have a sufficient level of domain knowledge and business acumen, as they are the context and motivation of your data mining projects. You should be able to understand the domain and the business problem that you are trying to solve with data mining, such as customer segmentation, churn prediction, fraud detection, or sentiment analysis. You should also be able to identify the relevant data sources, variables, and metrics that are related to the domain and the business problem. Furthermore, you should be able to translate your data mining results into actionable recommendations and strategies that can add value to the domain and the business.

添加您的观点

Hossein Tazarvi

Marketing Data Analyst at MCI
举报内容
Learning the concepts of data mining and machine learning is essential. Proper communication with the audience and elucidating findings is the end goal, so for hard skills, one should master visualizing data with different tools. With the help of Language models like GPT, although coding loses its importance as the years go by, it will come in handy.

已翻译

赞

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Data Mining

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

You’re looking to start a career in data mining. What hard skills do you need to have?

1

2

3

4

5

6

1 Programming languages

2 Statistics and mathematics

3 Machine learning and data mining techniques

4 Data visualization and communication

5 Domain knowledge and business acumen

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

更多Data Mining相关文章

更多相关阅读内容

You’re looking to start a career in data mining. What hard skills do you need to have?

1

2

3

4

5

6

1 Programming languages

2 Statistics and mathematics

3 Machine learning and data mining techniques

4 Data visualization and communication

5 Domain knowledge and business acumen

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

查看其他技能