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Probability and Statistics for Data Science
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This 5-day course provides a comprehensive introduction to probability and statistics, essential tools for data science and machine learning. Participants will explore fundamental concepts such as probability distributions, descriptive statistics, hypothesis testing, and regression analysis.
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Course Duration
5 Days
Course Details
This 5-day course provides a comprehensive introduction to probability and statistics, essential tools for data science and machine learning. Participants will explore fundamental concepts such as probability distributions, descriptive statistics, hypothesis testing, and regression analysis. The course emphasizes practical applications and data analysis techniques, using real-world examples to illustrate the importance of probability and statistics in data-driven decision-making. Through interactive sessions and hands-on exercises using statistical software (like R or Python with statistical libraries), learners will develop a strong understanding of statistical principles and be able to apply them to analyze data and draw meaningful conclusions.
This course focuses on building a solid foundation in statistical thinking and the ability to interpret statistical results. It covers both descriptive statistics (summarizing and visualizing data) and inferential statistics (making inferences about populations based on samples). The course also introduces Bayesian statistics, a powerful approach for updating beliefs based on evidence. By the end of this course, participants will be able to perform statistical analyses, interpret the results, and communicate findings effectively.
By the end of this course, learners will be able to:
- Understand basic probability concepts and distributions.
- Calculate descriptive statistics and visualize data.
- Perform hypothesis testing.
- Apply regression analysis techniques.
- Understand the principles of Bayesian statistics.
- Use statistical software for data analysis.
- Data scientists, analysts, and machine learning engineers.
- Individuals who want to gain a strong understanding of probability and statistics.
- Anyone who needs to analyze data and make data-driven decisions.
Course Outline
5 days Course
- Introduction to Probability:
- Basic probability concepts: Events, sample space, probability axioms.
- Conditional probability and Bayes’ theorem.
- Discrete probability distributions: Bernoulli, binomial, Poisson.
- Practical exercise: Solving probability problems.
- Continuous Probability Distributions and Descriptive Statistics:
- Continuous probability distributions: Normal, exponential, uniform.
- Descriptive statistics: Mean, median, mode, variance, standard deviation.
- Data visualization: Histograms, box plots, scatter plots.
- Practical exercise: Calculating descriptive statistics and creating visualizations using statistical software.
- Hypothesis Testing:
- Hypothesis testing: Null and alternative hypotheses, p-values.
- t-tests, chi-square tests, ANOVA.
- Practical exercise: Performing hypothesis tests using statistical software.
- Hypothesis Testing:
- Regression Analysis:
- Linear regression: Simple and multiple regression.
- Model evaluation and interpretation.
- Practical exercise: Building and interpreting regression models using statistical software.
- Regression Analysis:
- Bayesian Statistics:
- Bayesian statistics: Prior and posterior probabilities.
- Bayes’ theorem and its applications.
- Introduction to Markov Chain Monte Carlo (MCMC) methods.
- Practical exercise: Applying Bayesian methods to real-world problems.
- Bayesian Statistics:
