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Person Analyzing Statistics

Introduction to Python Data Analytics

Unlock the Power of Data: Master AP Statistics and Excel in Analysis!

Introducing you to the field of Data and Statistic and build your understanding in this exciting journey of exploring and understanding the power of data. 

20 hours of 1:1 lessons

Course Overview 

AP Statistics is a college-level course that introduces students to the principles and methods of statistical analysis. The course covers a wide range of topics, including data collection and organization, probability theory, hypothesis testing, and data interpretation. Students will develop the skills necessary to analyze and interpret data, make informed decisions, and communicate statistical information effectively.

22 High Quality 



19 practical Task

20 hours of video

Certificate of Achievement

Lesson You 'II Learn 

Topic Covered

AP Statistics

Data Collection

Data Analysis


  1. A solid foundation in statistical concepts and techniques.

  2. Develop ability to collect, organize, and analyze data using appropriate statistical methods.

  3. Enhance understanding of probability theory and its applications in real-world scenarios.

  4. Skills to conduct hypothesis tests, make inferences, and draw conclusions based on sample data.

  5. To foster critical thinking and problem-solving skills through hands-on data analysis and interpretation.

  6. To prepare students for the AP Statistics exam, including the ability to apply statistical concepts to solve complex problems.

Course Curriculum :
  1. Introduction to Statistics

    • Descriptive statistics

    • Inferential statistics

    • Data types and sources

  2. Organizing Data

    • Frequency tables

    • Histograms and bar graphs

    • Stem-and-leaf plots

    • Box plots

  3. Descriptive Statistics

    • Measures of central tendency                                                                (mean, median, mode)

    • Measures of variability (range, interquartile                                     range, variance, standard deviation)

    • Measures of position (percentiles, z-scores)

  4. Probability

    • Fundamental counting principle

    • Probability rules

    • Conditional probability

    • Independent and dependent events

  5. Random Variables and Probability Distributions

    • Discrete and continuous random variables

    • Probability mass function (PMF) and probability                              density function (PDF)

    • Binomial distribution

    • Normal distribution

  6. Sampling and Sampling Distributions

    • Simple random sampling

    • Stratified and cluster sampling

    • Sampling distributions of means and proportions

    • Central Limit Theorem

  7. Confidence Intervals

    • Confidence interval estimation

    • Margin of error

    • Confidence intervals for means and proportions

    • Determining sample size for estimating proportions

  1. Hypothesis Testing

    • Null and alternative hypotheses

    • Type I and Type II errors

    • One-sample t-tests

    • Chi-square tests

  2. Inference for Categorical Data

    • Tests of independence

    • Goodness-of-fit tests

    • Contingency tables

  3. Inference for Regression

    • Simple linear regression

    • Correlation coefficient

    • Coefficient of determination (R-squared)

    • Inference for regression parameters

  4. Analysis of Variance (ANOVA)

    • One-way ANOVA

    • F-test

    • Post hoc tests

  5. Experimental Design

    • Randomized controlled experiments

    • Matched pairs design

    • Blocking and factorial designs

  6. Probability Simulation

    • Monte Carlo simulation

    • Random number generation

    • Simulating experiments

  7. Data Analysis and Interpretation

    • Data visualization techniques

    • Exploratory data analysis

    • Interpretation of statistical results


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