数据科学提示/入门指南 (DATA SCIENCE TIPS /BEGINNERS GUIDE)

Data Science has improved considerably over the last two years. Nearly 90% of the available data was generated in the previous two years, proving that data scientists have increased tenfold. It is now widely used by big corporate companies and industries across the globe. The data science sector has been flourishing at a much faster pace than other areas.

在过去的两年中，数据科学有了长足的进步。 将近90％的可用数据是在过去两年中生成的 ，证明了数据科学家的数量已增加了十倍。 现在，它已被全球的大型公司和行业广泛使用。 数据科学领域的蓬勃发展比其他领域要快得多。

Do you want to step into the world of data science and gain all the fame associated with it? You are in the right place then! We will broadly explore the methods, tools, and processes involved in data science in this article. We will also provide an in-depth insight into the world of data science and what it takes to become a data scientist. The report will also cover various aspects of this field and how it is extensively used by renowned companies worldwide. Let’s immerse ourselves in the world of the most exponentially growing area of study!

您是否想进入数据科学领域并获得与之相关的所有声誉？ 那您来对地方了！ 我们将在本文中广泛探讨数据科学涉及的方法 ， 工具和过程 。 我们还将提供对数据科学领域以及成为数据科学家所需 的深入了解 。 该报告还将涵盖该领域的各个方面以及全球知名公司如何广泛使用它。 让我们沉浸在学习领域呈指数增长的世界中！

介绍 (Introduction)

· Data Science is the field of study that includes extracting critical information from large quantities of data. It is done through algorithms, processes, and various scientific methods.

·数据科学是一个研究领域，包括从大量数据中提取关键信息。 它是通过算法 ，过程和各种科学方法完成的。

· Important Data Science job roles include data scientist, data engineer, statistician, business analyst, data analyst, etc.

·重要的数据科学工作角色包括数据科学家，数据工程师，统计学家，业务分析师，数据分析师等。

· The gaming world extensively uses it, banking sectors, health industry, and e-commerce sites.

·游戏世界广泛使用它，银行业，医疗保健行业和电子商务站点。

· World-renowned companies like Netflix and Proctor and Gamble use Data Science to get the desired outcomes.

· Netflix和Proctor and Gamble等世界知名公司使用Data Science获得所需的结果。

· It is one of the most highly demanded fields presently.

·它是目前最需要的领域之一。

Let’s Dive into the World of Data Science, Created by Unknown user in Reddit using Giphy.

让我们深入研究Reddit中由未知用户创建的数据科学世界 使用 Giphy 。

什么是数据科学？ (What is Data Science?)

It is one of the most common questions that one has in mind while searching for Data Science.

这是搜索数据科学时想到的最常见的问题之一。

· Data Science is an interdisciplinary field that derives knowledge and simplified information from structured and unstructured data. This simplified information makes it easy to read and retain it.

·数据科学是一个跨学科领域 ，可以从结构化和非结构化数据中获取知识和简化信息。 这种简化的信息使阅读和保存变得容易。

· Data Science exclusively refers to the process of assigning meaning to a group of data.

·数据科学专门指为一组数据分配含义的过程。

· Data Scientists use cloud computing tools to create an environment for virtual development. Mathematical Statistics, Big Data, and Machine Learning are some standard methods used in the process.

·数据科学家使用云计算工具来创建虚拟开发环境。 数学统计，大数据和机器学习是该过程中使用的一些标准方法。

· Large Scale businesses use Data Science strategies in creative ways. It also increases their competitive advantage in the world of business.

·大型企业以创造性的方式使用数据科学策略。 这也增加了他们在商业领域的竞争优势。

· Data Science processes include Business Analytics, Business Intelligence, Data Mining, Predictive Analytics, Data Analytics, and Data Visualisation.

·数据科学流程包括业务分析，商业智能，数据挖掘，预测分析，数据分析和数据可视化。

为什么数据科学如此受欢迎？ (Why is Data Science becoming so popular?)

· Data Science helps in transforming a problem into research. It similarly comes up with a practical solution.

·数据科学有助于将问题转化为研究。 类似地，它提出了一个实用的解决方案。

· You can identify fraudulent activities with the help of Data Science. It saves your business from falling into fake and virtual traps.

·您可以在数据科学的帮助下识别欺诈活动 。 它可以避免您的业务陷入虚假和虚拟陷阱。

· You can increase your customer brand loyalty. Data Science makes that possible as it performs sentiment analysis. It also helps you to recommend the exact product that your customer demands.

·您可以提高客户品牌忠诚度。 数据科学通过执行情感分析使之成为可能。 它还可以帮助您推荐客户所需的确切产品。

· It prevents any kind of monetary loss.

·防止任何形式的金钱损失。

· It boosts the process of decision making, and it makes it quicker and faster.

·它加快了决策过程，使其变得越来越快。

· One of the coolest features of data science is that it lets you develop your machines’ intelligence ability!

·数据科学最酷的功能之一是，它可以让您开发机器的智能能力 ！

数据科学类似于商业智能(BI)吗？ (Is Data Science similar to Business Intelligence (BI)?)

Although they are often interchangeably used, they are not similar!

尽管它们经常可互换使用，但它们并不相似！

· Data Science is a vast field that makes use of Business Intelligence as one of its strategies. Hence, BI falls under the parent category of Data Science.

·数据科学是一个广阔的领域，利用商业智能作为其战略之一。 因此，BI属于数据科学的父类别。

· BI focuses on visualization and statistics, and Data Science focuses on statistics, Graph, and Machine Learning.

·BI专注于可视化和统计，而Data Science专注于统计，图和机器学习。

· BI uses tools like Microsoft Bl, Pentaho, and QlikView. Data Science uses tools like TensorFlow and R.

·BI使用Microsoft Bl，Pentaho和 QlikView之类的工具 。 数据科学使用TensorFlow和R之类的工具。

· Business Intelligence analyses the history, experiences, and related data. Data Science uses these to analyze and predict what lies in the future. Where BI identifies the problem, Data Science provides a solution to it through Neuro-linguistic programming and analysis.

·商业智能分析历史 ，经验和相关数据。 数据科学使用这些工具来分析和预测未来情况。 在BI发现问题的地方，数据科学通过神经语言编程和分析为其提供了解决方案。

数据科学的组成部分是什么？ (What are the components of Data Science?)

· Statistics: It is the most important unit. Statistics refers to the scientific method of collecting and analyzing vast quantities of numerical data. It provides useful insights.

· 统计：这是最重要的单位。 统计是指收集和分析大量数值数据的科学方法。 它提供了有用的见解。

· Visualization: It helps in accessing a large amount of data through digestible and straightforward visuals. It makes data easy to decipher.

· 可视化：它可帮助您通过易于理解的直观图像访问大量数据。 它使数据易于解密。

· Machine Learning: It accentuates the study of algorithms. It also helps in building the same. It is done to make predictions about future data.

· 机器学习：强调算法的研究。 它还有助于构建相同的对象。 可以对未来的数据做出预测 。

· Deep Learning: It is a comparatively new research field of machine learning. Here, the algorithm particularly chooses the analysis model that will be followed.

深度学习：这是一个相对较新的机器学习研究领域。 在此，算法特别选择将要遵循的分析模型。

数据科学使用哪些工具？ (What are the tools used by Data Science?)

· Data Sets: Data is acquired from a lot of researches which were conducted in the past. The data is then analyzed through analytical tools and algorithms. Without Data Sets, Data Science research is impossible as there will be no data to analyze.

· 数据集：数据是从过去进行的许多研究中获得的。 然后通过分析工具和算法对数据进行分析。 没有数据集，就不可能进行数据科学研究，因为将没有数据可分析。

· Big Data: It is the collection of a very complicated and massive amount of data. It is difficult to process using traditional data processing applications or on-hand database management tools. Traditional software, at no cost, can manage Big Data. Hence, Data Scientists came up with another device.

· 大数据：这是非常复杂和大量数据的集合。 使用传统的数据处理应用程序或现有的数据库管理工具很难进行处理。 传统软件可以免费管理大数据。 因此，数据科学家想出了另一种设备。

· Hadoop: Hadoop was initially developed to handle Big Data that no traditional software could manage. It stores and processes these large datasets. HDFS or Hadoop Distributed File System manages the storage in Hadoop. It further improves the availability of data by distributing them evenly across the ecosystem. It first breaks the information into segments and then spreads them to various nodes in a cluster.

· Hadoop： Hadoop最初是为处理传统软件无法管理的大数据而开发的。 它存储和处理这些大型数据集。 HDFS或Hadoop分布式文件系统管理Hadoop中的存储。 通过在整个生态系统中平均分配数据，可以进一步提高数据的可用性。 它首先将信息分成多个部分，然后将其传播到集群中的各个节点。

MapReduce is the most crucial element of Hadoop. The algorithms function by mapping and reducing data. The mappers break the more significant tasks into smaller ones. These smaller tasks are distributed evenly. Once the mapping is done, the results are aggregated. The effects are reduced to comparatively more uncomplicated values through the Reduce process.

MapReduce是Hadoop最关键的元素。 该算法通过映射和减少数据来发挥作用。 映射器将较重要的任务分解为较小的任务。 这些较小的任务平均分配。 映射完成后，将汇总结果。 通过“减少”过程，可以将效果减小为相对简单的值。

· R Studio: It is an open-source programming language and software environment. It deals with graphics and statistical computing under the R Foundation. It can also be used for analytical purposes as a programming language. It can be used for Data Visualisation. It is simple and easy to read, write, and learn. Since it is an open-source, people can distribute its copies, read and modify its source code, etc. R studio, however, cannot manage Big Data.

· R Studio：这是一种开放源代码编程语言和软件环境。 它在R Foundation下处理图形和统计计算 。 它也可以作为一种编程语言用于分析目的。 它可以用于数据可视化。 它很容易阅读，编写和学习。 由于它是开源的，因此人们可以分发其副本，阅读和修改其源代码等。但是，R studio无法管理大数据。

· Spark R: Using Hadoop, processing input with R Studio is quite tricky since it cannot function in a distributed ecosystem. Hence, we use Spark R. Spark R is an R Package. It provides a simple way of using R with Apache Spark. It provides distributed data frames. These data frames can be implemented to filter, select, and aggregate large data sets.

· Spark R：使用Hadoop，使用R Studio处理输入非常棘手，因为它无法在分布式生态系统中运行。 因此，我们使用SparkR。SparkR是一个R包。 它提供了将R与Apache Spark结合使用的简单方法。 它提供分布式数据帧 。 可以实现这些数据帧以过滤，选择和聚合大型数据集。

数据科学中使用了哪些过程？ (What are the processes used in Data Science?)

· Exploring Data: It generally deals with collecting data from the external as well as internal sources. It is done to answer or provide a solution to a particular business question. The data that it deals with is collected through streaming from online sources using APIs, from census datasets, social media, and as logs from web servers.

· 探索数据：它通常处理从外部和内部源收集数据。 可以回答特定的业务问题或提供解决方案。 它处理的数据是通过使用API从在线资源流式传输，普查数据集，社交媒体以及Web服务器的日志收集而来的。

· Preparation of Data: It cleans inconsistencies like blank columns, missing value, and incorrect data format. Before modeling, the data needs to be explored, processed, and conditioned. With clean data, you can achieve better prediction.

· 数据准备：它清除不一致的地方，例如空白列，缺失值和错误的数据格式。 在建模之前，需要对数据进行探索，处理和调整。 使用干净的数据，您可以实现更好的预测。

· Model Planning: You need to identify the technique and method to draw a relation between input variables. Model planning is done by using various statistical formulae and visualization tools. R, SQL analysis services are some of the tools used for model planning.

· 模型计划：您需要确定在输入变量之间绘制关系的技术和方法。 通过使用各种统计公式和可视化工具来完成模型规划。 R，SQL分析服务是用于模型规划的一些工具。

· The building of Model: Data sets are evenly distributed for testing and training. Techniques of clustering, classification, and association are applied to training data sets. When the model is prepared, it is tested against the testing dataset.

· 建立模型：将数据集均匀分布以进行测试和培训 。 聚类，分类和关联技术应用于训练数据集。 准备好模型后，将根据测试数据集对其进行测试。

· Operationalization: Final model is delivered with technical documents, reports, and codes. The model is thoroughly tested. If it passes the test, it is used as a real-time production environment.

· 操作性：最终模型随附技术文档，报告和代码。 该模型已经过全面测试。 如果通过测试，它将用作实时生产环境。

· Results: The results are communicated to all the stakeholders. It genuinely decides if the results are successful and or not. The decision is made based on the inputs from the model.

· 结果：将结果传达给所有利益相关者。 它真正决定结果是否成功。 根据模型的输入做出决策。

与数据科学相关的角色是什么？ (Which job are roles associated with Data Science?)

· Data Scientist: A data scientist handles large quantities of data to produce compelling visions for the particular business. They make use of various algorithms, tools, methods, and processes. A Data Scientist deals with programming languages like R, Python, SAS, SQL, Matlab, Spark, Hive and Pig.

· 数据科学家：数据科学家处理大量数据，以针对特定业务产生令人信服的愿景。 它们利用了各种算法，工具，方法和过程。 数据科学家处理诸如R，Python，SAS，SQL，Matlab，Spark，Hive和Pig等编程语言。

· Data Analyst: They mine vast quantities of data. They look for trends, patterns, and relationships in data. They do so to deliver compelling visualization and reporting. These are further used to analyze data. Business decisions are made only after this. They deal with programming languages like R, Python, SQL, C++, C, HTML, and JS.

· 数据分析师：他们挖掘大量数据。 他们在数据中寻找趋势，模式和关系 。 他们这样做是为了提供引人注目的可视化和报告。 这些进一步用于分析数据。 此后才做出业务决策。 他们处理R，Python，SQL，C ++，C，HTML和JS等编程语言。

· Data Engineer: A Data Engineer works with large quantities of data. They maintain, build, develop, and test architectures such as large scale databases and processing systems. They deal with programming languages like Java, C++, R, Python, Hive, SQL, SAS, Perl, and Ruby.

· 数据工程师：数据工程师处理大量数据。 他们维护，构建，开发和测试架构，例如大型数据库和处理系统。 他们处理Java，C ++，R，Python，Hive，SQL，SAS，Perl和Ruby等编程语言。

· Statistician: They use statistical methods and theories to collect and analyze data. They also use these to understand quantitative as well as qualitative data. They deal with programming languages like Spark, Perl, R, SQL, Python, Tableau, and Hive.

· 统计员：他们使用统计方法和理论来收集和分析数据。 他们还使用它们来了解定量和定性数据 。 他们处理诸如Spark，Perl，R，SQL，Python，Tableau和Hive的编程语言。

· Business Analyst: They are responsible for improving business processes. They act as the bridge between the IT department and the business executive. They deal with programming languages like SQL, Python, Tableau, and Power BI.

· 业务分析师：他们负责改善业务流程。 它们充当了IT部门和业务主管之间的桥梁。 他们处理SQL，Python，Tableau和Power BI等编程语言。

· Data Administrator: They ensure the accessibility of the database to all the users. They look after its correct and safe performance to prevent it from getting hacked. They deal with programming languages like SQL, Java, Ruby on Rails, Python, and C#.

· 数据管理员：他们确保所有用户都可以访问数据库。 他们会照顾它的正确和安全性能，以防止其被黑客入侵。 它们处理SQL，Java，Ruby on Rails，Python和C＃等编程语言。

数据科学有哪些应用？ (What are the applications of Data Science?)

· Google Search: It uses Data Science to search for a particular result within a few microseconds.

· Google搜索 ：它使用数据科学在几微秒内搜索特定结果。

· Speech and Image Recognition: Speech deals with many systems like Siri, Alexa, and Google Assistant. All of this has been possible due to the application of Data Science. An example of image recognition is when you upload a photo with your friend on social media; it recognizes your friend and shows suggestion tags.

语音和图像识别：语音处理许多系统，例如Siri，Alexa和Google Assistant 。 由于数据科学的应用，所有这些都是可能的。 图像识别的一个例子是当您与朋友在社交媒体上上传照片时； 它会识别您的朋友并显示建议标签。

· Recommendation System: Data Science is used in creating a recommendation system. Suggested friends in social media, suggested videos on YouTube, and suggested purchases on e-commerce sites are examples.

· 推荐系统：数据科学用于创建推荐系统。 例如 ，社交媒体中的推荐朋友 ，YouTube上的推荐视频以及电子商务网站上的推荐购买。

· Price Comparison: Shopzilla, Junglee, and PriceRunner make use of Data Science. Using APIs, data is fetched from particular websites.

· 价格比较： Shopzilla，Junglee和PriceRunner使用数据科学。 使用API​​，可从特定网站获取数据。

· Games: Nintendo, Sony, and EA Sports use Data Science. A machine learning technique is used to develop games. When you move to higher and more complicated levels, it updates itself to face more complications henceforth. You also get to unlock various prizes. All of it has been possible due to Data Science.

· 游戏：任天堂，索尼和EA Sports使用数据科学。 机器学习技术用于开发游戏。 当您移动到更高和更复杂的级别时，它会随之更新以面对更多的复杂性。 您还可以解锁各种奖品。 由于数据科学，所有这些都是可能的。

哪些部门使用数据科学？ (Which sectors use Data Science?)

· E-Commerce: Online retailers make use of Data Science in 4 ways. It is done to achieve business value. The four methods include identifying the target customers, exploring potential customers, increasing sales with product recommendations, and extracting useful feedback from reviews.

· 电子商务 ：在线零售商通过4种方式使用数据科学。 这样做是为了实现商业价值 。 这四种方法包括：确定目标客户，探索潜在客户，通过产品推荐增加销量以及从评论中提取有用的反馈。

· Manufacturing Industry: It uses Data Science in 8 ways to analyze its productivity, minimize risks, and increase profit. These eight ways include tracking performance and defects, predictive maintenance, forecasting demand, supply chain relations, global market pricing, automation, new product development techniques, and increased efficiency of sustainability.

· 制造业：它以8种方式使用数据科学来分析其生产率，最小化风险并增加利润。 这八种方式包括跟踪性能和缺陷，预测性维护，预测需求，供应链关系，全球市场定价，自动化，新产品开发技术以及提高的可持续性效率。

· Banking: Banking sectors use Data Science in fraud detection, risk modeling, customer value, customer segmentation, and real-time predictive analysis.

· 银行业：银行业在欺诈检测，风险建模，客户价值，客户细分和实时预测分析中使用数据科学。

· Healthcare Industry: It uses Data Science for patient prediction and patient tracking. It also uses it for electronic health records, significant data imaging, and predictive analytics.

· 医疗保健行业：它将数据科学用于患者预测和患者跟踪 。 它还将其用于电子健康记录， 重要数据成像和预测分析。

· Transport: Transport sectors use Data Science to go to ensure a safer driving environment for drivers. It optimizes vehicle performance. It also adds autonomy to the drivers. Data Science has also given rise to self-driving cars.

· 运输：运输部门使用数据科学来确保为驾驶员提供更安全的驾驶环境。 它优化了车辆性能。 它还为驱动程序增加了自主性。 数据科学还兴起了自动驾驶汽车 。

哪些知名组织利用数据科学？ (Which well-known organizations make use of Data Science?)

· Netflix: Yes, you read that right. It uses Data Science to understand what accentuates the interests of the users. Depending on the information collected, it premiers the next production series.

· Netflix ：是的，您没有看错。 它使用数据科学来了解什么可以强调用户的兴趣。 根据所收集的信息，它将首播下一个产品系列。

· Proctor and Gamble: It uses Data Science’s time series models. Through these models, it understands the future demands and plans for production levels accordingly.

· Proctor and Gamble：它使用Data Science的时间序列模型 。 通过这些模型，它可以了解未来的需求并相应地计划生产水平。

· Target: It uses Data Science to identify the major customer segments and their shopping behavior. Through this, they guide different audiences.

· 目标：它使用数据科学来识别主要客户群及其购物行为。 通过这种方式，他们可以指导不同的受众。

如何成为数据科学家？ (How to Become a Data Scientist?)

· Educational Qualifications: You should have a bachelor’s degree in any of these fields — Computer Science, Physics, Social Science, and Statistics. The most common areas include Statistics and Mathematics, followed by Computer Science and Engineering.

· 教育资格：您应该在以下任何一个领域拥有学士学位-计算机科学，物理，社会科学和统计。 最常见的领域包括统计学和数学，其次是计算机科学和工程。

· Learn Statistics and Mathematics: An individual needs to have a stable ground of Mathematics and a basic understanding of Statistics to become a Data Scientist. You must be familiar with causation, correlation, and hypothesis testing. Linear algebra and calculus are essential.

· 学习统计学和数学：个人需要拥有稳定的数学基础和对统计学的基本了解才能成为数据科学家。 您必须熟悉因果关系，相关性和假设检验 。 线性代数和微积分是必不可少的。

· Practice Programming: You must be familiar with the programming language of Python. Database interaction is equally important. If you develop a good knowledge of Python, move forward to learning other programming languages like Java and R.

· 练习编程：您必须熟悉Python的编程语言。 数据库交互也同样重要。 如果您精通Python，请继续学习其他编程语言，例如Java和R。

· Focus on Machine Learning: It is advisable to learn the standard algorithms which are also popular. Learning complicated problems do not always help. Start with the simpler ones that matter us your problem solving and optimization capacities.

· 专注于机器学习：建议学习同样流行的标准算法 。 学习复杂的问题并不总是有帮助。 从与我们息息相关的简单问题开始，解决问题和优化能力。

· Create Machine Learning Projects: Start implementing the knowledge that you have developed on machine learning. Big firms always look for the ones who know how it works behind the screen.

· 创建机器学习项目：开始实施您在机器学习中获得的知识。 大公司总是寻找那些知道其幕后运作方式的人。

· Keep up with the trend: Upskilling is very important. Presently, companies are looking for people who are skilled in Robotics, Cybersecurity, RPA, Artificial Intelligence, Automation, Data Analytics, and FinTech.

· 紧跟潮流：提高技能非常重要。 目前，公司正在寻找熟练掌握机器人技术，网络安全 ，RPA， 人工智能 ，自动化，数据分析和金融科技的人员。

· Create a Portfolio: Your resume must mention your coding and software skills. Candidate username, e-mail address, locations, and current employers must be specified. What enhances your portfolio is a large number of followers, improving on stars, contribution graphs, writing targeted code, contribution graph, and so on.

· 创建投资组合： 您的简历必须提及您的编码和软件技能。 必须指定候选人的用户名，电子邮件地址，位置和当前雇主 。 增加您的投资组合的是大量的关注者，他们在星级，贡献图，编写目标代码，贡献图等方面有所改进。

数据科学面临哪些挑战？ (What are the challenges faced by Data Science?)

High-quality data is necessary for accurate analysis. A small organization cannot have a Data Science department. Adequate Data Scientists are not available even though it is a highly demanded field. There might be privacy issues. A company’s management fails to provide the financial support needed to build a Data Science team. It is challenging to explain Data Science to people who do not possess any knowledge in this field. Access to data is either unavailable or difficult. Business decision-makers fail to use Data Science results effectively.

高质量的数据对于准确的分析是必要的。 小型组织不能有数据科学部门。 没有足够的数据科学家，即使这是一个要求很高的领域。 可能存在隐私问题。 公司的管理层未能提供建立数据科学团队所需的财务支持。 向在该领域没有任何知识的人们解释数据科学具有挑战性。 数据访问不可用或困难。 商业决策者无法有效利用数据科学结果。

结论 (Conclusion)

There are plenty of career opportunities if you are dealing with Data Science. Multi-national companies are always filtering data and optimizing it for better customer experience. Essential sectors like banks, healthcare industries, transportation, e-commerce sites use Data Science to get the best results. The world is continuously upgrading itself into a better version. It generally paves the way for data science necessities in dealing with humongous amounts of data and satisfying the customers!

如果您与数据科学打交道，将会有很多职业机会 。 跨国公司始终在过滤数据并对其进行优化，以提供更好的客户体验。 诸如银行，医疗保健行业，运输，电子商务站点之类的重要部门使用Data Science获得最佳结果。 世界正在不断将自己升级为更好的版本。 通常，它为处理大量数据并满足客户需求的数据科学必需品铺平了道路！

In the coming years, the world will need more than 140,000 data scientists. It has been reported that the income of data scientists in the US is about $144,000 per year. Hence, it is high time people should consider Data Science as a compelling career choice. The companies should also invest in it and provide the financial support that it needs.

在未来几年中，世界将需要超过14万名数据科学家 。 据报道，美国数据科学家的收入约为每年144,000美元 。 因此，现在应该将数据科学视为一种引人注目的职业选择。 公司还应对此进行投资并提供所需的财务支持。

I`ve always taken life as a journey from one experience to another. So far it has been a road full of interesting events and people. Join me on my Journey through LinkedIn, Instagram & Youtube

我一直把生活视为从一种经历到另一种经历的旅程。 到目前为止，这条路充满了有趣的事件和人们。 通过LinkedIn ， Instagram和Youtube加入我的旅程

With all the information at hand, you are hopefully prepared to become a successful Data Scientist in the future. Hope this helps and all the best for your future endeavors! Thanks for reading this article! Leave a comment below if you have any questions.

掌握了所有信息，您有望将来成为一名成功的数据科学家。 希望这对您的未来有所帮助，并祝一切顺利！ 感谢您阅读本文！ 如有任何疑问，请在下面发表评论。

Best of Luck! Cheers!

祝你好运！ 干杯!