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Python for Data Science · LearnSpace
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Python for Data Science

Курс от Fractal Analytics
Начальный≈ 40.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Master Python for data science with hands‑on projects. Learn pandas, statistics, and visualization to solve real‑world business problems. Build job‑ready skills in data wrangling, exploratory data analysis (EDA), and charting with matplotlib/seaborn—no prior experience required. This beginner‑friendly course guides you through cleaning messy data, applying descriptive and inferential statistics, and preparing datasets for machine learning. You’ll design analyses that answer business questions, communicate insights with compelling visuals, and complete challenging assessments aligned to workplace scenarios. By the end, you’ll confidently manipulate data in pandas, automate workflows, and build dashboards that stakeholders understand. Start your data‑driven journey and turn raw data into decisions.

Навыки, которые вы освоите

Data TransformationFeature EngineeringData PreprocessingExploratory Data AnalysisData ManipulationPandas (Python Package)Plot (Graphics)Statistical AnalysisDescriptive StatisticsCorrelation AnalysisData ProcessingData AnalysisData ScienceData CleansingStatistical MethodsData WranglingData Visualization SoftwareSeabornMatplotlibStatistical Hypothesis Testing

Программа курса

5 модулей · 166 учебных материалов

01Introduction to Python for Data Science22 материалов

Welcome to the Course

Welcome to python for data scienceВидеоCourse syllabusЧтениеExpert Talk - A data scientist's experience with PythonВидео

Kickstarting with Python and Jupyter

What is python?Видео

Учитесь у экспертов

Fractal Analytics Academy

Преподаватель курса

Python for Data Science
В каталоге вашей программы

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Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 40.5 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Working with Jupyter notebooksВидео
Installation guide Чтение
Python fundamentalsЗадание
Working effectively with Jupyter notebooksЧтение

How Python Solved a Business Problem

Important note!ЧтениеIntroduction to the problemВидеоSolution approach - Preparing tables and chartsВидеоSolution approach - Gaining InsightsВидеоSolution Approach - Airline traffic analysisВидеоSolution summaryВидеоConnecting Python to Real Business SolutionsDIALOGUE

The Business Problem for the Course

The Global Problem StatementЧтениеExpert Talk - Why Python is the language of choice for data science professionalsВидеоIntroduction to the ProblemВидеоExploring the ProblemВидеоStakeholder Meeting: Uncovering the Real Business ProblemDIALOGUEData Analysis ЗаданиеTell us what you think!Чтение
02Data wrangling with Python49 материалов

Getting started with a data set

IntroductionВидеоDiving into CSV DataВидеоData inspectionВидеоFinding missing data in the POS dataВидеоDeleting missing data and saving the cleaned data setВидеоData cleaning with pythonЧтениеLab data and problemВидеоA note on assessmentsВидеоData cleaning & manipulation ЛабораторнаяDataFame essentialsЗаданиеResources - Datasets and Jupyter notebooks Чтение

DataFrame Fundamentals and Indexing

Basic data structures - lists and dictionariesВидеоBasic data structures - seriesВидеоCreating a data frame using lists, dictionaries and seriesВидеоPython statistics fundamentals ЧтениеSlicing with precisionВидеоChanging the indices and saving the new DataFrameВидео

Exploring and Querying Columns

Navigating data insightsВидеоSelecting data that match certain criteriaВидеоSelecting data that match multiple criteriaВидеоExpert Talk - Understanding your dataВидеоData selection & filteringЗаданиеData slicing & manipulationsЛабораторная

Manipulation and Grouping of Data

What are the unique products in the POS data set?ВидеоFinding specific values in the dataВидеоHow much did we sell per category? ВидеоFinding totals and averages by brand and by categoryВидеоGrouping by multiple attributesВидеоDisplaying aggregated data in a pivot tableВидео

Date Time Operations in Python

Expert talk - How insights and data analysis guide each otherВидеоWorking with datesВидеоHow much did we sell each month?ВидеоWhat is the monthly average of sales?ВидеоWere there specific dates when sales were high?ВидеоWorking with dates Чтение

Putting Together Data from Multiple Sources

What if we have more than one dataset?ВидеоMerging some simple data setsВидеоMerging POS data with the online dataВидеоMerging & joining dataframesЗаданиеMerging the dataЛабораторнаяNew Programming AssignmentПрограммирование
03Exploratory data analysis45 материалов

Descriptive Statistics

IntroductionВидеоExpert Talk - Why EDA is a superpowerВидеоFinding the average of the dataВидеоUnderstanding the spread of the dataВидеоQuantiles - how to understand and visualize themВидеоExploring variability in the POS dataВидеоWhat shape is my data? ВидеоUnderstanding the distributions of features in the POS dataВидеоUnderstanding data distributions ЛабораторнаяStatistics fundamentalsЗаданиеResources - Datasets and Jupyter notebooksЧтение

Understanding Data Distributions

Understanding Data DistributionsВидеоSome other common shapes of data - Part IВидеоSome other common shapes of data - Part IВидеоSome other common shapes of data - Part IIВидеоSome other common shapes of data - Part IIIВидеоWhat chance of revenue falls in a given rangeВидео

Relationships Between Features

How are the features related to each other? - Part IВидеоHow are the features related to each other? - Part IВидеоHow are the features related to each other? - Part IIВидеоHow are the features related to each other? - Part IIВидеоVisualizing categorical featuresВидеоVisualizing proportionsВидео

Enhancing Data Visualizations

Expert Talk - Power of visualization & its importance in storytelling ВидеоUsing boxplots to compare revenues across segments in the POS dataВидеоMaking better visuals - Part IIIВидеоCommunicating insights better by creating multiple subplots within the same plotВидеоComparing the distribution of revenue for each sector by overlaying their KDE plots ВидеоPractice QuizЗадание

Comparing Two Groups: Hypothesis Testing

Sampling our data - Part I ВидеоSampling our data - Part IIВидеоIntroduction to hypothesis testing - Part IВидеоIntroduction to hypothesis testing - Part IIВидеоHypothesis testing using Z - Test - Part IВидеоHypothesis testing using Z - Test - Part IIВидео
04Data pre-processing34 материалов

Treating Missing Data

IntroductionВидеоExpert Talk - Handling missing dataВидеоWhat to do with missing values?ВидеоMissing values in the POS dataВидеоMissing values within a hierarchyВидеоMissing values within a hierarchy (contd.)ВидеоWhat if parts of the hierarchy are also missing?ВидеоFinishing up missing value treatment in the POS dataВидеоMissing values - another simpler exampleВидеоMissing valuesЗаданиеHandling missing valuesЛабораторнаяResources - Datasets and Jupyter notebooksЧтение

Data Transformation

Working with categoric featuresВидеоTransforming features - binning and discretizationВидеоTransforming features - binning and discretization (contd.)ВидеоEncoding categoric features - one-hot and label encodingВидеоEncoding features in the POS dataВидеоFinishing up the encoding and saving the encoded dataВидео

Understanding Data Normalization and Outlier Detection

What is data normalization and why do we need it?ВидеоData normalization using min-max scalingВидеоData normalization using z-score scalingВидеоOther types of data transformationВидеоApplying log transformation to the online dataВидеоFinding outlying dataВидео
05Feature Engineering16 материалов

Creating Derived Features in Python

IntroductionВидеоReducing the dimensionality of data setsВидеоExploring the features of the obesity data setВидеоWhat is Principal Component Analysis(PCA)?ВидеоApplying PCA to the obesity dataВидеоCreating a transformed version of the data through feature engineeringВидеоComplete guide to Feature EngineeringЧтениеFeature engineering & PCAЗаданиеResources - Datasets and Jupyter notebooksЧтениеDimensionality reduction, PCAЛабораторная

Gen AI in Python

Expert Talk - Gen AI in PythonВидеоIntroduction to Gen AI in Python for Data scienceВидеоSome quick data analysis using PandasAIВидеоSome quick data visualization using PandasAIВидеоGraded AssignmentПрограммированиеSummaryВидео
DataFrame operationsЗадание
Data manipulation & aggregationЗадание
Data aggregationsЛабораторная
Date time operationsЗадание
Practice Programming AssignmentЛабораторная
Graded AssignmentПрограммирование
Walkthrough - How to approach a graded assignmentВидео
SummaryВидео
Data distributionsЗадание
Practice Programming AssignmentЛабораторная
Understanding relationships between featuresЗадание
Practice Programming AssignmentЛабораторная
Hypothesis testing using t - TestВидео
Hypothesis testing using Chi-square testВидео
Practice quizЗадание
Practice Programming AssignmentЛабораторная
Graded AssignmentПрограммирование
SummaryВидео
Dealing with categorical dataЗадание
Handling categorical featuresЛабораторная
Removing outliers by dropping themВидео
How to deal with outliers - imputationВидео
How to deal with outliers - cappingВидео
Data normalizationЗадание
Data pre-processing Чтение
Data normalization & treating outliersЛабораторная
Graded AssignmentПрограммирование
SummaryВидео