Final Exam: Statistics and Probability

Math    |    Beginner
  • 1 Video | 32s
  • Includes Assessment
  • Earns a Badge
Final Exam: Statistics and Probability will test your knowledge and application of the topics presented throughout the Statistics and Probability track of the Skillsoft Aspire Essential Math for Data Science Journey.


  • describe what statistics, populations, and samples are
    recognize how metrics such as mean, median and mode describe data
    summarize the workings a number of probability sampling techniques
    load data from a CSV file into a pandas DataFrame and perform some initial analysis
    calculate the mean and median of a distribution using your own function and compare it with the built-in pandas function
    use Seaborn and Matplotlib to visualize a distribution and where the mean, median, and mode fit in
    calculate the mean and median of a distribution using your own function and compare it with the built-in pandas function
    create a balanced sample using random undersampling and oversampling
    define terms such as event, outcome, and experiment
    import python libraries needed to work with probabilities
    simulate the flipping of a coin in Python
    define joint, marginal, and conditional probability
    simulate the rolling of two die to test joint probability
    calculate joint probabilities associated with the rolling of a die
    calculate the joint probability of dependent variables
    define the formula of the expected value of a random variable
    compute conditional probabilities
    define and understand the Bayes theorem
    define a Bayesian model in Python
    explore the probability tables of nodes in a Bayesian network
    predict values with Bayesian models
    explore probabilities associated with a Bayesian model
    create naive Bayes models in Python
    define descriptive and inferential statistics
    describe different types of probability distributions and where they occur
    analyze and visualize data using box plots
    recognize how data is distributed using histograms and violin plots
    calculate and visualize confidence intervals using Python
    estimate a population's mean with confidence intervals
    describe binomial distributions and generate one using SciPy
  • recount binomial distributions and generate one using SciPy
    analyze a uniform distribution by using cumulative distribution and probability density functions
    apply Poisson distributions to make estimates in real-life situations
    use Poisson distributions to make estimates in real-life situations
    describe normal distributions and their characteristics
    explain the law of large numbers programmatically
    recall the symmetrical features of normal distributions
    describe the fundamentals of hypothesis testing
    set up null and alternative hypotheses for statistical tests
    interpret p-values using alpha levels
    compare and contrast type I and type II errors in hypothesis testing
    explore one-sided and two-sided T-tests
    create a function to manually perform a T-test
    perform the Wilcoxon signed-rank test to compare medians
    test medians using the Wilcoxon signed-rank test
    perform T-tests on real-world data
    recall the assumptions of the two-sample T-test
    use the two-sample T-test to compare means
    use Levene’s test to check for equal variances
    recognize when the Welch’s T-test should be used
    describe type I and type II errors
    perform the paired T-test on paired samples
    use the Welch’s T-test to compare means
    recognize the use of the Mann-Whitney U-test
    use the Mann-Whitney U-test
    outline the use of one-way ANOVA analysis
    use Tukey’s HSD to know which categories differ significantly
    use the non-parametric Kruskal-Wallis test
    outline the use of the two-way ANOVA analysis
    use two-way ANOVA with interaction between the independent variables


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    Statistics and Probability


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