登录查看更多内容

What are the most important data science skills for an interview?

由人工智能和领英社区提供技术支持

Data science is a hot and in-demand field that requires a combination of technical, analytical, and communication skills. If you are applying for a data science role, you need to prepare for various types of questions that test your knowledge, experience, and problem-solving abilities. In this article, we will cover some of the most important data science skills for an interview and how to showcase them effectively.

此文章中的业界达人

由社区从 7 条内容中精选。了解更多

1 Programming languages

One of the fundamental skills for data science is the ability to write, read, and debug code in one or more programming languages. Depending on the role and the industry, you may need to be proficient in languages such as Python, R, SQL, Java, or Scala. You should be able to demonstrate your coding skills by writing clear, concise, and well-documented code that can perform data manipulation, analysis, and visualization tasks. You should also be familiar with common data science libraries and frameworks, such as pandas, numpy, scikit-learn, TensorFlow, or PyTorch.

添加您的观点

Rashmi Krishnamohan

Senior Data Analyst | Microsoft Certified Power BI Data Analyst | Driven by data, Fueled by numbers
举报内容
Some of the key skills that aspiring data scientists should master to ace their interviews: 1. Proficient in SQL for data retrieval and manipulation. 2. Clear communication of technical concepts to non-technical stakeholders. 3. Ability to derive and communicate insights from visualized data. 4. Understanding of the industry or domain context.

已翻译

赞
Rouzbeh Beikpour

Founder of Roulium | IT & Digital Marketing Leader | Transforming Businesses with Scalable Solutions
举报内容
Proficiency in programming languages such as Python or R is essential. Python is particularly popular due to its extensive libraries for data analysis (like Pandas, NumPy, and Scikit-learn).

已翻译

赞
Christopher Lazok
举报内容
I look for proof of concept: 1. Do they have a webpage or showcase? 2. Can they build a sample for you on the fly? 3. Can they show utilization of DevOPS? I've been asked to deliver "live" during interviews. And passed the test. In short; Proficiencies; or the [Tools, Frameworks, (Languages)] required will change by the project and SDLC. The Ability to generate Highly Useful Content is the end result for Data Sci. AI will stabalize much of this as well, Its already happening. -Therefore; Understanding of the End Result, and not just the business requirement is something only an Experienced Data Scientist can deliver.

已翻译

赞

2 Data structures and algorithms

Another essential skill for data science is the understanding of data structures and algorithms, which are the building blocks of efficient and scalable code. You should be able to explain the advantages and disadvantages of different data structures, such as arrays, lists, stacks, queues, trees, graphs, and hash tables. You should also be able to implement and compare various algorithms, such as sorting, searching, hashing, recursion, dynamic programming, and machine learning algorithms. You should be prepared to answer questions that test your ability to choose the appropriate data structure and algorithm for a given problem, as well as to analyze their time and space complexity.

添加您的观点

3 Statistics and mathematics

Data science is based on the application of statistics and mathematics to extract insights from data. You should have a solid grasp of the basic concepts and methods of statistics and mathematics, such as probability, distributions, hypothesis testing, confidence intervals, correlation, regression, linear algebra, calculus, and optimization. You should be able to apply these concepts and methods to real-world data problems, such as designing experiments, evaluating models, interpreting results, and making recommendations. You should also be able to communicate your statistical and mathematical reasoning using appropriate terminology and notation.

添加您的观点

4 Machine learning and deep learning

Machine learning and deep learning are the core techniques of data science that enable computers to learn from data and make predictions or decisions. You should have a good understanding of the principles and practices of machine learning and deep learning, such as supervised, unsupervised, and reinforcement learning, classification, regression, clustering, dimensionality reduction, feature engineering, model selection, validation, tuning, evaluation, and deployment. You should also be familiar with the most common machine learning and deep learning models and algorithms, such as linear models, decision trees, random forests, support vector machines, k-means, principal component analysis, neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. You should be able to explain how these models and algorithms work, what are their advantages and disadvantages, and how to use them for different data problems.

添加您的观点

Rouzbeh Beikpour

Founder of Roulium | IT & Digital Marketing Leader | Transforming Businesses with Scalable Solutions
举报内容
It is critical to understand various machine learning algorithms (like linear regression, decision trees, clustering, etc.) and know when to apply them.

已翻译

赞

5 Data visualization and storytelling

Data visualization and storytelling are the skills that enable you to present your data analysis and findings in a clear, compelling, and engaging way. You should be able to create effective and attractive data visualizations using tools such as matplotlib, seaborn, plotly, ggplot2, or Tableau. You should also be able to tell a coherent and convincing story with your data, using techniques such as framing, context, narrative, emotion, and action. You should be able to adapt your data visualization and storytelling style to different audiences and purposes, such as technical, business, or general.

添加您的观点

Rouzbeh Beikpour

Founder of Roulium | IT & Digital Marketing Leader | Transforming Businesses with Scalable Solutions
举报内容
Skills in data visualization tools (like Matplotlib, Seaborn, or even more advanced tools like Tableau) help in effectively presenting data insights.

已翻译

赞

6 Domain knowledge and business acumen

Domain knowledge and business acumen are the skills that enable you to understand the context and the goals of your data science projects. You should be able to demonstrate your knowledge and interest in the domain and the industry that you are applying for, such as finance, healthcare, e-commerce, or education. You should also be able to show your business acumen by identifying the key problems, opportunities, and stakeholders that your data science solutions can address. You should be able to articulate the value and the impact of your data science work, as well as the challenges and the risks involved.

添加您的观点

7 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

Oforma Chineke

Automation-Agnostic Business Analyst turning business problems into smart, simple, winning solutions
举报内容
For an Interview some important data analytics skills would be: - Data visualization for storytelling and interpretation. - Statistics and use of statistical analysis e.g Analysis of data using R - Domain Knowledge of the area the Hiring Company exists in - Demonstration of knowledge using secondary data of the company sourced from publicly available data sources. - Ability to demonstrate how data plays a crucial role in decision making and strategy.

已翻译

赞
Carlos Palou

Ayudo a realizar la transformación digital: seminarios, talleres y coordinando grupos internos de innovación disruptiva
举报内容
Creo que hay que considerar dos apartados muy diferenciados: -Se debe tener un conocimiento del negocio y del sector, como principal argumento. Comprender los problemas, tener identificadas las fuentes fiables, es básico a la hora de plantear cualquier enfoque para buscar soluciones. -Disponer de los conocimientos técnicos necesarios para usar las tecnologías existentes enfocadas a buscar soluciones. Es como un mecánico, debe conocer el coche y su funcionamiento, sus debilidades y fortalezas, como paso previo para decidirse a qué herramientas debe utilizar y cómo utilizarlas. No hay que perder de vista en que las tecnologías son herramientas y que son perecederas, lo que hoy es lo máximo, ma?ana puede ser obsoleto.

已翻译

赞

IT Management

+ 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

What are the most important data science skills for an interview?

1

2

3

4

5

6

7

1 Programming languages

2 Data structures and algorithms

3 Statistics and mathematics

4 Machine learning and deep learning

5 Data visualization and storytelling

6 Domain knowledge and business acumen

7 Here’s what else to consider

IT Management

给文章评分

感谢您的反馈

更多IT Management相关文章

更多相关阅读内容

What are the most important data science skills for an interview?

1

2

3

4

5

6

7

1 Programming languages

2 Data structures and algorithms

3 Statistics and mathematics

4 Machine learning and deep learning

5 Data visualization and storytelling

6 Domain knowledge and business acumen

7 Here’s what else to consider

IT Management

给文章评分

感谢您的反馈

查看其他技能