Python for Data Analysis

Python for Data Analysis Courses

Learn how to explore, clean, and analyze data using Python — the most widely used language in data science. This category covers everything from working with Pandas and NumPy to visualizing insights with Matplotlib and Seaborn. Whether you're a beginner or upskilling for a data-driven role, you'll gain hands-on experience analyzing real datasets and making data-driven decisions.

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Opnåede færdigheder:

A/B Test DesignAnalytics-Driven Decision MakingAssignment & Transportation ProblemsAvancerede NumPy-teknikkerBatch Data Pipeline DesignBusiness Data VisualizationChi-Square AnalysisCohort AnalysisCorrelation AnalysisCustomer SegmentationData Extraction TechniquesData StorytellingData Transformation with pandasData Visualization with matplotlib and seabornDataaggregering med pandasDatabase Loading with sqlite3Dataindeksering og -udvælgelse i pandasDatatransformationDatavaskDatavisualiseringDescriptive StatisticsETL and ELT ConceptsExperimental Data PreparationExploratory Data AnalysisFeature engineeringFeature-kodningFeature-selektionFeature-skaleringFunnel AnalyticsHypothesis TestingImputation af manglende værdierIncremental Loading StrategiesInteger ProgrammingLinear Programming in PythonLogging and MonitoringMetric Framework DesignMonte Carlo SimulationMultivariate AnalysisNumPy-funktionerNumPy-grundlæggendeOptimization ModelingOrchestration PatternsPandas datamanipulationPipeline ModularizationPipeline-opbygningPortfolio SimulationPython Data ManipulationPython-programmeringPython-visualiseringPython-visualisering med MatplotlibRandom Sampling in PythonResource Allocation ModelingRevenue SegmentationRisk EstimationSeaborn-visualiseringSensitivity AnalysisStatistical InterpretationStatistik med PythonTeknikker til datavisualiseringUdregning af outliersUnit Economics CalculationWeb scraping med Pythont-test and z-test Application

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Niveau

Lektionstype

Teknologier

Data Wrangling with pandas

4 timer

33 kapitler

Mellemniveau

17 STUDERER NU

Opnåede færdigheder: Data Indexing and Selection in pandas, Data Aggregation with pandas, Pandas Data Manipulation

NumPy Basics

3 timer

27 kapitler

Mellemniveau

32 STUDERER NU

Opnåede færdigheder: NumPy Basics, NumPy Advanced Techniques, NumPy Functions

Ultimate Visualization with Python

3 timer

26 kapitler

Mellemniveau

15 STUDERER NU

Opnåede færdigheder: Data Visualization, Data Visualization Techniques, Python Visualization, Python Visualization with Matplotlib, Seaborn Visualization

Web Scraping with Python

3 timer

23 kapitler

Mellemniveau

3 STUDERER NU

Opnåede færdigheder: Python Programming, Web Scraping with Python

Statistics with Python

6 timer

38 kapitler

Avanceret

4 STUDERER NU

Opnåede færdigheder: Statistics with Python

Data Preprocessing and Feature Engineering with Python

1 time

12 kapitler

Begynder

4 STUDERER NU

Opnåede færdigheder: Data Cleaning , Missing Value Imputation , Outlier Detection , Feature Encoding , Feature Scaling , Data Transformation , Feature Engineering , Feature Selection , Pipeline Building

Business Analytics and Decision Making with Python

2 timer

18 kapitler

Mellemniveau

2 STUDERER NU

Opnåede færdigheder: Cohort Analysis, Funnel Analytics, Unit Economics Calculation, Revenue Segmentation, Customer Segmentation, Metric Framework Design, Python Data Manipulation, Business Data Visualization

Applied Hypothesis Testing & A/B Testing

3 timer

31 kapitler

Begynder

1 STUDERER NU

Opnåede færdigheder: Hypothesis Testing, t-test and z-test Application, Chi-Square Analysis, A/B Test Design, Experimental Data Preparation, Statistical Interpretation

Data Pipelines with Python

2 timer

15 kapitler

Mellemniveau

Opnåede færdigheder: ETL and ELT Concepts, Batch Data Pipeline Design, Data Extraction Techniques, Data Transformation with pandas, Database Loading with sqlite3, Incremental Loading Strategies, Pipeline Modularization, Logging and Monitoring, Orchestration Patterns

Exploratory Data Analysis with Python

2 timer

18 kapitler

Begynder

Opnåede færdigheder: Exploratory Data Analysis, Descriptive Statistics, Data Visualization with matplotlib and seaborn, Correlation Analysis, Multivariate Analysis, Data Storytelling

Optimization Methods for Data Analytics

1 time

12 kapitler

Begynder

Opnåede færdigheder: Optimization Modeling, Linear Programming in Python, Integer Programming, Resource Allocation Modeling, Assignment & Transportation Problems, Sensitivity Analysis, Analytics-Driven Decision Making

Simulation and Monte Carlo Modeling with Python

2 timer

14 kapitler

Mellemniveau

1 STUDERER NU

Opnåede færdigheder: Random Sampling in Python, Monte Carlo Simulation, Risk Estimation, Sensitivity Analysis, Portfolio Simulation

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Python for Data Analysis Courses: Nøgleinfo og spørgsmål

What is Python for Data Analysis?

Python for Data Analysis focuses on using Python to collect, manipulate, and interpret data. It includes working with libraries like Pandas for dataframes, NumPy for numerical operations, and tools for data visualization, making it essential for aspiring data analysts and scientists.

Do I need to know Python before taking these courses?

Basic Python knowledge is helpful but not always required. Many courses start with quick refreshers or beginner-friendly intros before moving into data-focused topics like cleaning, filtering, and analyzing datasets.

What skills will I gain from Python for Data Analysis courses?

You’ll learn how to work with CSV and Excel files, manipulate large datasets, handle missing data, calculate statistics, create visualizations, and extract meaningful insights. You'll also build the ability to ask the right questions and support decisions with data.

What libraries and tools are used in these courses?

Courses typically focus on Pandas, NumPy, Matplotlib, and Seaborn. Some may also include Jupyter Notebooks, Plotly, or integrations with tools like Excel, SQL, or Google Sheets.

Are the courses hands-on?

Yes — most lessons include live code editors, exercises with real-world datasets, and small projects like analyzing sales, customer behavior, or public data. This practical approach ensures you learn by doing, not just reading theory.

What careers can Python for Data Analysis help with?

This skillset is crucial for roles such as data analyst, business analyst, product analyst, marketing analyst, and junior data scientist. It also supports skills needed in operations, finance, and any job involving reports or decision-making based on data.

Will I receive a certificate?

Yes. After completing the course, you’ll get a certificate that verifies your ability to perform data analysis using Python — a strong asset for resumes, portfolios, or job applications.

Can I use AI tools alongside Python for Data Analysis?

Absolutely. Many courses now include guidance on how to use AI (like ChatGPT or Copilot) to assist with writing Pandas queries, cleaning data, or generating visualizations — making your workflow faster and more efficient.

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Topkurser i Python for Data Analysis kategori

1.	Data Wrangling med Pandas	4 timer	33 kapitler
2.	Numpy-Grundlæggende	3 timer	27 kapitler
3.	Ultimativ Visualisering med Python	3 timer	26 kapitler
4.	Web Scraping med Python	3 timer	23 kapitler
5.	Statistik med Python	6 timer	38 kapitler