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Probability & Statistics courses

Probability and statistics courses explore mathematical concepts for analyzing random events and interpreting data through models and inference. Use tools such as Python, R, Excel and Google Sheets to apply your theoretical knowledge in statistics.

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Recommended for Probability & Statistics beginners

Build your Probability & Statistics skills with interactive courses, curated by real-world experts

course

Introduction to Statistics in R

MellanliggandeFärdighetsnivå
4.4+
4.5K
4 hours
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

track

Statistician in R

919
52 hours
A statistician collects and analyzes data and helps companies make sense of quantitative data, including spotting trends and making predictions.

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course

ChIP-seq with Bioconductor in R

MellanliggandeFärdighetsnivå
4.5+
105
4 hours
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

course

Analyzing Survey Data in Python

MellanliggandeFärdighetsnivå
4.6+
104
4 hours
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

course

Intermediate Network Analysis in Python

AvanceradFärdighetsnivå
4.4+
104
4 hours
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

course

Bayesian Modeling with RJAGS

AvanceradFärdighetsnivå
4.8+
82
4 hours
In this course, youll learn how to implement more advanced Bayesian models using RJAGS.

course

Case Studies: Network Analysis in R

GrundläggandeFärdighetsnivå
4.8+
78
4 hours
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

course

Building Response Models in R

MellanliggandeFärdighetsnivå
4.7+
73
4 hours
Learn to build simple models of market response to increase the effectiveness of your marketing plans.

course

Introduction to Anomaly Detection in R

MellanliggandeFärdighetsnivå
4.6+
58
4 hours
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

course

Forecasting Product Demand in R

MellanliggandeFärdighetsnivå
4.7+
57
4 hours
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

course

Probability Puzzles in R

GrundläggandeFärdighetsnivå
4.4+
51
4 hours
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

course

Mixture Models in R

MellanliggandeFärdighetsnivå
4.7+
47
4 hours
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Relaterade resurser på Probability & Statistics

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How to Become a Statistician in 2026

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Discover the powerful fusion of statistics and machine learning. Explore how statistical techniques underpin machine learning models, enabling data-driven decision-making.
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Ready to apply your skills?

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    Frequently asked questions

    How does probability and statistics related to data science?

    Probability and statistics are foundational to data science, offering the tools and frameworks necessary for analyzing data, making predictions, and deriving meaningful insights. They enable data scientists to understand patterns, assess uncertainties, and make informed decisions based on data analysis.

    Why is it important to develop knowledge in probability and statistics?

    Developing knowledge in probability and statistics is crucial for effectively interpreting data and making reliable predictions. This understanding forms the basis for designing experiments, analyzing results, and validating conclusions in various fields, ensuring decisions are data-driven and evidence-based.

    What careers can I pursue with knowledge in probability and statistics?

    With knowledge in probability and statistics, you can pursue a wide array of careers such as data scientist, market researcher, machine learning engineer, statistical analyst, and risk manager. These roles span various industries including finance, healthcare, technology, and government, where interpreting data and making evidence-based decisions are key.

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