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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-with-python-certification-training/
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课程名称：用 Python 进行数据科学（初学者到专家） 课程概述： 欢迎参加由 Uplatz 提供的《用 Python 进行数据科学》课程。本课程不仅涵盖了如何使用 Python 语言进行数据清理、分析和可视化，还探讨了如何将 Python 编程技能应用于预测和识别有助于决策的趋势。随着数据革命的兴起，数据成为组织的新油，现今的决策依赖于数据、数学模型、统计学和图形等多学科方法来满足各种商业需求，例如天气预报、客户细分、生物学中的蛋白质结构研究、市场营销活动的设计以及新店开设等。现代的数据驱动技术系统依赖于识别、整合、存储和分析数据以支持商业决策。 本课程将引导学员从 Python 的基础知识，逐步探索简单和复杂的数据集，最终进行预测分析与模型开发。课程将使你能够准备数据进行分析，执行复杂的统计分析，创建有意义的数据可视化，预测未来的趋势，开发机器学习和深度学习模型等。Python 编程部分将从零基础逐步提升至高级编程，使学员能够编写自己的 Python 脚本并进行基础的数据分析。 课程目标是为学员提供一个全面的学习框架，以使用 Python 进行数据科学。你将深入理解数据，学习应用数据科学的方法与技术，并掌握分析技能。本课程还将涵盖统计、机器学习、信息可视化、文本分析和社交网络分析等概念，使用 pandas、NumPy、matplotlib 和 scikit-learn 等热门 Python 工具包。 学习内容包括： 1. 数据科学基础及其重要性 2. Python 编程基础，包括变量、数据类型、控制结构 3. 使用 Python 数据科学库（如 Pandas 和 Numpy）进行数据处理和建模 4. 数据可视化，通过 Matplotlib 和 Seaborn 创建多种图表 5. 统计分析和机器学习基础，涵盖回归分析、分类、聚类和关联规则学习 适合希望成为数据科学家的学员，课程将帮助你理解数据科学概念并开始数据科学之旅，提升你的 Python 技能和分析能力。
A warm welcome to the Data Science with Python course by Uplatz.Data Science with Python involves not only using Python language to clean, analyze and visualize data, but also applying Python programming skills to predict and identify trends useful for decision-making.Why Python for Data Science?Since data revolution has made data as the new oil for organizations, today's decisions are driven by multidisciplinary approach of using data, mathematical models, statistics, graphs, databases for various business needs such as forecasting weather, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. The modern data-powered technology systems are driven by identifying, integrating, storing and analyzing data for useful business decisions. Scientific logic backed with data provides solid understanding of the business and its analysis. Hence there is a need for a programming language that can cater to all these diverse needs of data science, machine learning, data analysis & visualization, and that can be applied to practical scenarios with efficiency. Python is a programming language that perfectly fits the bill here and shines bright as one such language due to its immense power, rich libraries and built in features that make it easy to tackle the various facets of Data Science.This Data Science with Python course by Uplatz will take your journey from the fundamentals of Python to exploring simple and complex datasets and finally to predictive analysis & models development. In this Data Science using Python course, you will learn how to prepare data for analysis, perform complex statistical analyses, create meaningful data visualizations, predict future trends from data, develop machine learning & deep learning models, and more.The Python programming part of the course will gradually take you from scratch to advanced programming in Python. You'll be able to write your own Python scripts and perform basic hands-on data analysis. If you aspire to become a data scientist and want to expand your horizons, then this is the perfect course for you. The primary goal of this course is to provide you a comprehensive learning framework to use Python for data science. In the Data Science with Python training you will gain new insights into your data and will learn to apply data science methods and techniques, along with acquiring analytics skills. With understanding of the basic python taught in the initial part of this course, you will move on to understand the data science concepts, and eventually will gain skills to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular Python toolkits such as pandas, NumPy, matplotlib, scikit-learn, and so on.The Data Science with Python training will help you learn and appreciate the fact that how this versatile language (Python) allows you to perform rich operations starting from import, cleansing, manipulation of data, to form a data lake or structured data sets, to finally visualize data - thus combining all integral skills for any aspiring data scientist, analyst, consultant, or researcher. In this Data Science using Python training, you will also work with real-world datasets and learn the statistical & machine learning techniques you need to train the decision trees and/or use natural language processing (NLP). Simply grow your Python skills, understand the concepts of data science, and begin your journey to becoming a top data scientist.Data Science with Python Programming - Course Syllabus1. Introduction to Data ScienceIntroduction to Data SciencePython in Data ScienceWhy is Data Science so Important?Application of Data ScienceWhat will you learn in this course?2. Introduction to Python ProgrammingWhat is Python Programming?History of Python ProgrammingFeatures of Python ProgrammingApplication of Python ProgrammingSetup of Python ProgrammingGetting started with the first Python program3. Variables and Data TypesWhat is a variable?Declaration of variableVariable assignmentData types in PythonChecking Data typeData types ConversionPython programs for Variables and Data types4. Python Identifiers, Keywords, Reading Input, Output FormattingWhat is an Identifier?KeywordsReading InputTaking multiple inputs from userOutput FormattingPython end parameter5. Operators in PythonOperators and types of operators - Arithmetic Operators - Relational Operators - Assignment Operators - Logical Operators - Membership Operators - Identity Operators - Bitwise OperatorsPython programs for all types of operators6. Decision MakingIntroduction to Decision makingTypes of decision making statementsIntroduction, syntax, flowchart and programs for - if statement - if…else statement - nested ifelif statement7. LoopsIntroduction to LoopsTypes of loops - for loop - while loop - nested loopLoop Control StatementsBreak, continue and pass statementPython programs for all types of loops8. ListsPython ListsAccessing Values in ListsUpdating ListsDeleting List ElementsBasic List OperationsBuilt-in List Functions and Methods for list9. Tuples and DictionaryPython TupleAccessing, Deleting Tuple ElementsBasic Tuples OperationsBuilt-in Tuple Functions & methodsDifference between List and TuplePython DictionaryAccessing, Updating, Deleting Dictionary ElementsBuilt-in Functions and Methods for Dictionary10. Functions and ModulesWhat is a Function?Defining a Function and Calling a FunctionWays to write a functionTypes of functionsAnonymous FunctionsRecursive functionWhat is a module?Creating a moduleimport StatementLocating modules11. Working with FilesOpening and Closing FilesThe open FunctionThe file Object AttributesThe close() MethodReading and Writing FilesMore Operations on Files12. Regular ExpressionWhat is a Regular Expression?Metacharactersmatch() functionsearch() functionre match() vs re search()findall() functionsplit() functionsub() function13. Introduction to Python Data Science LibrariesData Science LibrariesLibraries for Data Processing and Modeling - Pandas - Numpy - SciPy - Scikit-learnLibraries for Data Visualization - Matplotlib - Seaborn - Plotly14. Components of Python EcosystemComponents of Python EcosystemUsing Pre-packaged Python Distribution: AnacondaJupyter Notebook15. Analysing Data using Numpy and PandasAnalysing Data using Numpy & PandasWhat is numpy? Why use numpy?Installation of numpyExamples of numpyWhat is ‘pandas'?Key features of pandasPython Pandas - Environment SetupPandas - Data Structure with exampleData Analysis using Pandas16. Data Visualisation with MatplotlibData Visualisation with Matplotlib - What is Data Visualisation? - Introduction to Matplotlib - Installation of MatplotlibTypes of data visualization charts/plots - Line chart, Scatter plot - Bar chart, Histogram - Area Plot, Pie chart - Boxplot, Contour plot17. Three-Dimensional Plotting with MatplotlibThree-Dimensional Plotting with Matplotlib - 3D Line Plot - 3D Scatter Plot - 3D Contour Plot - 3D Surface Plot18. Data Visualisation with SeabornIntroduction to seabornSeaborn FunctionalitiesInstalling seabornDifferent categories of plot in SeabornExploring Seaborn Plots19. Introduction to Statistical AnalysisWhat is Statistical Analysis?Introduction to Math and Statistics for Data ScienceTerminologies in Statistics - Statistics for Data ScienceCategories in StatisticsCorrelationMean, Median, and ModeQuartile20. Data Science Methodology (Part-1)Module 1: From Problem to ApproachBusiness UnderstandingAnalytic ApproachModule 2: From Requirements to CollectionData RequirementsData CollectionModule 3: From Understanding to PreparationData UnderstandingData Preparation21. Data Science Methodology (Part-2)Module 4: From Modeling to EvaluationModelingEvaluationModule 5: From Deployment to FeedbackDeploymentFeedbackSummary22. Introduction to Machine Learning and its TypesWhat is a Machine Learning?Need for Machine LearningApplication of Machine LearningTypes of Machine Learning - Supervised learning - Unsupervised learning - Reinforcement learning23. Regression AnalysisRegression AnalysisLinear RegressionImplementing Linear RegressionMultiple Linear RegressionImplementing Multiple Linear RegressionPolynomial RegressionImplementing Polynomial Regression24. ClassificationWhat is Classification?Classification algorithmsLogistic RegressionImplementing Logistic RegressionDecision TreeImplementing Decision TreeSupport Vector Machine (SVM)Implementing SVM25. ClusteringWhat is Clustering?Clustering AlgorithmsK-Means ClusteringHow does K-Means Clustering work?Implementing K-Means ClusteringHierarchical ClusteringAgglomerative Hierarchical clusteringHow does Agglomerative Hierarchical clustering Work?Divisive Hierarchical ClusteringImplementation of Agglomerative Hierarchical Clustering26. Association Rule LearningAssociation Rule LearningApriori algorithmWorking of Apriori algorithmImplementation of Apriori algorithm
