Data Science with Python Certification Training and Project | Data Science Online Course by Udemy

Development Online Course by Udemy, On Sale Here

Start your career as Data Scientist from scratch. Learn Data Science with Python. Predict trends with advanced analytics

An excellent training about Data Science

Data Science with Python Certification Training and Project

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 – ifelse 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

Udemy is the leading global marketplace for learning and instruction

By connecting students all over the world to the best instructors, Udemy is helping individuals reach their goals and pursue their dreams.

Study anytime, anywhere.

START HERE