Big Data Analytics 简明教程

Big Data Analytics - Data Analyst

数据分析师负责收集、分析和解释数据以解决特定问题。数据分析师花费大量时间检查数据，并通过图形化报告和仪表板来了解洞察。因此，数据分析师具有面向报告的简介，并且具有通过 SQL 从传统数据仓库中提取和分析数据的经验。

A Data Analyst is a person who collects, analyses and interprets data to solve a particular problem. A data analyst devotes a lot of time to examining the data and finds insights in terms of graphical reports and dashboards. Hence, a data analyst has a reporting-oriented profile and has experience in extracting and analyzing data from traditional data warehouses using SQL.

在大数据分析中担任数据分析师听起来是一个很灵动的角色。大数据分析包括分析海量且多样的数据集，以发现隐藏的模式、未知的关系、市场趋势、客户需求以及有价值的相关商业洞察。

Working as a data analyst in big data analytics sounds like a dynamic role. Big data analytics includes analysing large-size and varied datasets to discover hidden patterns, unknown relationships, market trends, customer needs, and related valuable business insights.

在当今情况下，不同的组织机构都在努力寻找市场上的合格数据科学家。然而，选择有前途的数据分析师并对其进行培训以掌握相关数据科学技能是一个好主意。合格的数据分析师具有业务理解、SQL 编程、报告设计和仪表板创建等技能。

In today’s scenario, different organizations struggle hard to find competent data scientists in the market. It is however a good idea to select prospective data analysts and train them to the relevant skills to become a data scientist. A competent data analyst has skills like business understanding, SQL programming, report design and Dashboard creation.

Role and Responsibilities of Data Analyst

下面提到的图像包含了数据分析师的主要角色和职责

Below mentioned image incorporate all the major roles and responsibilities of a data analyst −

big data analytics role

Data Collection

它指的是从数据库、数据仓库、API 和物联网设备等不同来源收集数据的过程。这可能包括执行调查、追踪访问者在公司网站上的行为或从数据收集专家处购买相关数据集。

It refers to a process of collecting data from different sources like databases, data warehouses, APIs, and IoT devices. This could include conducting surveys, tracking visitor behaviour on a company’s website, or buying relevant data sets from data collection specialists.

Data Cleaning and Pre-processing

原始数据中可能存在重复项、错误或离群值。清理原始数据会消除错误、不一致性和重复项。预处理是将数据转换为分析实用格式的过程。数据清理包括在电子表格中维护数据质量或使用编程语言来确保您的解释正确无偏见。

There may be duplicates, errors or outliers in the raw data. Cleaning raw data eliminates errors, inconsistencies, and duplicates. Pre-processing is the process of converting data into an analytically useful format. Cleaning data entails maintaining data quality in a spreadsheet or using a programming language to ensure that your interpretations are correct and unbiased.

Exploratory Data Analysis (EDA)

使用统计方法和可视化工具来分析数据，以识别趋势、模式或关系。

Using statistical methods and visualization tools, analysis of data is carried out to identify trends, patterns or relationships.

Model Data

包括创建和设计数据库结构。选择要存储和收集的数据类型。它确保如何关联数据类别以及如何显示数据。

It includes creating and designing database structures. Selection of type of data is going to be stored and collected. It ensures that how data categories are related and data appears.

Statistical Analysis

应用统计技术来解释数据、验证假设和进行预测。

Applying statistical techniques to interpret data, validate hypotheses, and make predictions.

Machine Learning

通过使用机器学习算法构建预测模型，为了预测未来趋势、分类数据或检测异常情况。

To predict future trends, classify data or detect anomalies by building predictive models using machine learning algorithms.

Data Visualization

为了有效地向利益相关者传达数据见解，有必要创建诸如图表、图形和仪表板等可视化表示。

To communicate data insights effectively to stakeholders, it is necessary to create visual representations such as charts, graphs and dashboards.

Data Interpretation and Reporting

通过解释分析结果以及准备报告或演示文稿，向决策者传达发现和建议。

To communicate findings and recommendations to decision-makers through the interpretation of analysis results, and preparation of reports or presentations.

Continuous Learning

包括及时了解数据分析、大数据技术和业务趋势方面的最新发展。

It includes keeping up to date with the latest developments in data analysis, big data technologies and business trends.

数据分析师在统计学、Python 或 R 等编程语言、数据库基础知识、SQL 以及 Hadoop、Spark 和 NoSQL 数据库等大数据技术方面奠定了其熟练程度的基础。

A Data analyst makes their proficiency foundation in statistics, programming languages like Python or R, database fundamentals, SQL, and big data technologies such as Hadoop, Spark, and NoSQL databases.

What Tools Does a Data Analyst Use?

数据分析师经常使用以下工具，在数据分析过程中更准确、更高效地处理分配的工作。一些数据分析师常用的工具如下面图像所示：

A data analyst often uses the following tools to process assigned work more accurately and efficiently during data analysis. Some common tools used by data analysts are mentioned in below image −

bda data analyst

Types of Data Analysts

随着技术的快速发展，能够收集、分类和分析数据的数据的类型和数量已成为几乎所有企业的基本技能。在当前的情况下，每个领域都有数据分析专家，例如刑事司法、时尚、食品、技术、商业、环境和公共部门等领域的数据分析师。从事数据分析的人员可能会被称为：

As technology has rapidly increasing; so, the types and amounts of data that can be collected, classified, and analyse data has become an essential skill in almost every business. In the current scenario; every domain has data analysts experts like data analysts in the criminal justice, fashion, food, technology, business, environment, and public sectors amongst many others. People who perform data analysis might be known as −

  1. Medical and health care analyst

  2. Market research analyst

  3. Business analyst

  4. Business intelligence analyst

  5. Operations research analyst

Data Analyst Skills

一般来说，数据分析师的技能被分为两大类，即 Technical Skills 和 Behavioural Skills 。

Generally, the skills of data analysts are divided into two major groups' i.e. Technical Skills and Behavioural Skills.

Data Analyst Technical Skills

  1. Data Cleaning − A data analyst has proficiency in identifying and handling missing data, outliers, and errors in datasets.

  2. Database Tools − Microsoft Excel and SQL are essential tools for any data analyst. Excel is most widely used in industries; while SQL is capable of handling larger datasets using SQL queries to manipulate and manage data as per user’s needs.

  3. Programming Languages − Data Analysts are proficient in languages such as Python, R, SQL, or others used for data manipulation, analysis, and visualization. Learning Python or R makes me proficient in working on large-size data sets and complex equations. Python and R are popular to work on data analysis.

  4. Data Visualisation − A competent data analyst must clearly and compellingly present their findings. Knowing how to show data in charts and graphs will help coworkers, employers, and stakeholders comprehend your job. Some popular data visualization tools are Tableau, Jupyter Notebook, and Excel.

  5. Data Storytelling − Data Analysts can find and communicate insights effectively through storytelling using data visualization and narrative techniques.

  6. Statistics and Maths − Statistical methods and tools are used to analyse data distributions, correlations, and trends. Knowledge of statistics and maths can guide us to determine which tools are best to use to solve a particular problem, identify errors in data, and better understand the results.

  7. Big Data Tools − Data Analysts are familiar with big data processing tools and frameworks like Hadoop, Spark, or Apache Kafka.

  8. Data Warehousing − Data Analysts also have an understanding of data warehousing concepts and work with tools such as Amazon Redshift, Google BigQuery, or Snowflake.

  9. Data Governance and Compliance − Data Analysts are aware of data governance principles, data privacy laws, and regulations (Like GDPR, and HIPAA).

  10. APIs and Web Scraping − Data Analysts have expertise in pulling data from web APIs and scraping from websites using libraries like requests (Python) or BeautifulSoup.

Behavioural Skills

  1. Problem-solving − A data analyst can understand the problem that needs to be solved. They identify patterns or trends that might reveal data. Critical thinking abilities enable analysts to focus on the types of data, identify the most illuminating methods of analysis, and detect gaps in their work.

  2. Analytical Thinking − The ability to evaluate complex problems, divide them into smaller components, and devise logical solutions.

  3. Communication − As a data analyst, communicating ideas is essential. Data analysts need solid writing and speaking abilities to communicate with colleagues and stakeholders.

  4. Industry Knowledge − Knowing your industry like health care, business, finance, etc. can help you to communicate with one another.

  5. Collaboration − Working well with team members, exchanging expertise, and contributing to a collaborative environment in which ideas are openly exchanged.

  6. Time Management − Prioritizing work, meeting deadlines, and devoting time to various areas of data analysis projects.

  7. Resilience − Dealing effectively with setbacks or failures in data analysis initiatives while remaining determined to find solutions.

Role of Data Analysts in Today’s Data-Driven World

在当今的数据驱动世界中，数据分析师至关重要，他们在许多层面上都扮演着至关重要的角色；其中一些原因如下：

Data analysts are essential to today’s data-driven world, they play a vital role on many levels; some of the reasons are as follows −

  1. Strategic Decision-Making − Knowing your industry like health Data analysts lays the framework for strategic decision-making by identifying trends and insights that can inform corporate plans and improve outcomes.

  2. Improving Efficiency − Data analysts assist firms in streamlining processes, lowering costs, and increasing productivity by discovering operational inefficiencies.

  3. Enhancing Customer Experiences − Analyzing customer data enables organizations to better understand customer habits and preferences, resulting in better products and services.

  4. Risk Management − Data analysis assists firms in identifying potential risks and obstacles, allowing them to develop mitigation solutions.

  5. Business Intelligence − Analysing raw data into relevant information and visualizations helps stakeholders to understand complex data. They produce dashboards, reports, and presentations for data-driven decision-making across a business.

  6. Predictive Analytics − Based on historical data, data analysts predict future patterns and outcomes using statistical modelling and machine learning. This helps firms anticipate customer wants, optimize resource allocation, and establish proactive initiatives.

  7. Continuous Improvement − Data analysts assess and monitor data analysis processes and methods to improve accuracy, efficiency, and relevance. They keep up with new technology and best practices to better data analysis.