该计划将帮助有志进入数据科学领域的人了解项目管理方法的概念。这将是处理数据科学项目的结构化方法。将学习理解业务问题以及理解目标、约束和定义成功标准的重要性。成功标准将包括商业、机器学习以及经济方面。了解在任何项目上创建的第一个文档，即项目章程。各种数据类型和四种数据度量将与数据收集机制一起进行解释，以便获得适当的数据以进行进一步分析。将详细解释包括调查和实验在内的主要数据收集技术。探索性数据分析或描述性分析将侧重于商业时刻的所有“4”时刻以及图形表示，其中还包括单变量、双变量和多变量图。将解释箱线图、直方图、散点图和 QQ 图。主要关注点是理解使用 Python 的数据预处理技术。这将确保提供适当的数据作为模型构建的输入。将使用面向实用的数据集讨论数据预处理技术，包括异常值分析、插补技术、缩放技术等。
你会学到什么
- 了解以结构化方式处理数据相关项目的项目管理方法。
- 了解业务问题定义、设定目标和 约束。
- 了解数据类型以及数据收集机制。
- 了解探索性数据分析 (EDA) / 描述性统计以及图形表示
- 使用 Python 了解各种数据清理/预处理任务。
此课程面向哪些人：
- 初学者、中级和高级学习者
- 刚接触数据科学并希望进入数据科学领域的应届生
- 在不同行业工作的专业人士
- 主要职责是教授学生数据相关概念的讲师、教授和教师
Instructors: 360DigiTMG Elearning
3 sections • 20 lectures • 2h 27m total length
Video: MP4 1280×720 44 KHz | English + Sub
Updated 9/2022 | Size: 1.3 GB
Data Science – Data Pre-processing Using Python
What you’ll learn
- Understand Project Management Methodology to Handle Data Related Projects in Structured Manner.
- Understand Business Problem Definition, Setting Objectives & Constraints.
- Understand Data Types as well as Data Collection Mechanisms.
- Understand Exploratory Data Analytics (EDA) / Descriptive Statistics as well as Graphical Representation
- Understand the various Data Cleansing /Pre-Processing Tasks using Python.
Requirements
No Programming and No Statistics knowledge is needed because everything is taught right from scratch.Basic Computer Knowledge and Primary School Mathematics Knowledge is sufficient.
Description
This program will help aspirants getting into the field of data science understand the concepts of project management methodology. This will be a structured approach in handling data science projects. Importance of understanding business problem alongside understanding the objectives, constraints and defining success criteria will be learnt. Success criteria will include Business, ML as well as Economic aspects. Learn about the first document which gets created on any project which is Project Charter. The various data types and the four measures of data will be explained alongside data collection mechanisms so that appropriate data is obtained for further analysis. Primary data collection techniques including surveys as well as experiments will be explained in detail. Exploratory Data Analysis or Descriptive Analytics will be explained with focus on all the ‘4’ moments of business moments as well as graphical representations, which also includes univariate, bivariate and multivariate plots. Box plots, Histograms, Scatter plots and Q-Q plots will be explained. Prime focus will be in understanding the data preprocessing techniques using Python. This will ensure that appropriate data is given as input for model building. Data preprocessing techniques including outlier analysis, imputation techniques, scaling techniques, etc., will be discussed using practical oriented datasets. /blogs/tomorrowland2
Who this course is for: Beginners, Intermediate as well as Advanced learnersFreshers who are new of data science and want to embark into the field of data scienceWorking professionals who are working in different industriesLecturers & Professors & Teachers whose primary role is to teach students on data related concepts
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