This is a one-day course for covering inferential and descriptive statistical concepts and their implementation using Python. This is useful for anyone needing to do hypothesis testing or who who deals with data on a regular basis and need to analyze it to derive insights and solve problems.
This course builds on existing Python knowledge to explore the tools Python has to conduct statistical analyses and operations.
Learners will conduct descriptive and inferential calculations on data sets using Python. Analyzing statistics using Python offers a rich ecosystem of libraries tailored for various statistical tasks, making it accessible to users with diverse backgrounds. Python's intuitive syntax and extensive community support contribute to its ease of use, while its flexibility enables seamless integration with other tools and platforms for comprehensive data analysis pipelines. With powerful visualization libraries, Python facilitates interactive exploration and effective communication of statistical findings, enhancing the efficiency and depth of statistical analysis workflows.
Delivery Method | Duration | ||
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1 days | Get a Quote | ||
1 days | Get a Quote |
This course is for Data Scientists, Data Analysts, Business Analysts, Researchers and students, or anyone who deals with data regularly and needs to analyze it to derive insights and solve problems.
Core mathematics to matric level
Python programming knowledge covered in our Python Programming 5-day course
Basic statistics knowledge covered in our Introduction to Statistics course
Data analysis concepts covered in our Python Pandas and Data Visualisation with Python
Introduction to Python statistics
Probability
Discrete probability
Monte Carlo simulations for categorical data
Independence
Conditional probabilities
Addition and multiplication rules
Combinations and permutations
Infinity in practice
Continuous probability
Theoretical continuous distributions
Monte Carlo simulations for continuous variables
Continuous distributions
Random variables
Random variables
Sampling models
The probability distribution of a random variable
Distributions versus probability distributions
Notation for random variables
The expected value and standard error
Central Limit Theorem
Statistical properties of averages
Law of large numbers
There are currently no scheduled dates.