Basic Statistics Mini-Course for absolute beginners

Basic Statistics Mini-Course for absolute beginners is designed for learners without a mathematics or statistics background or training. It is especially useful for those looking to gain a solid grasp of the foundations of statistics and data analysis before deciding how deep they would like to dive into the world of statistics.

Basic Statistics explains without jargon key statistical concepts and their applications in quantitative data analysis for decision making. You will learn through intuitive examples foundational mathematical skills to help you launch a career in data analysis and data science. Topics covered include data collection, data preparation, measures of central tendency, spread analysis, the normal distribution, the normal approximation to the binomial distribution, and two variable statistics.

You may also be interested in Google Data Analytics Professional Certificate Course 1: Foundations – Cliffs Notes.

Basic Statistics (Mini-Course Modules)

Statistics can be understood as a branch of mathematics concerned with collecting, organizing, analyzing, and presenting data for decision making.

1. Data collection in statistics

Data collection in statistics is a process of gathering information from the relevant sources to find a solution to a research problem.

Key concepts covered: data sources, random samples, table of random digits, simulations, tally charts, data collection methods, types of data.

2. Data presentation in statistics

Visual data presentation helps data analysists identify and understand relationships between and within data sets to inform science-based decision making.

Key concepts covered: frequency distributions and histograms, graphical representations of data, comparing graphs, stem and leaf plots.

3. Measures of central tendency

Measures of central tendency help us locate the centre of the data, which allows us to make predictions about what is likely to happen. The most common measures of central tendency are the mean, the median, and the mode.

Key concepts covered: mean, median, mode, effect of distribution on measures of central tendency.

4. Analyzing data spread

Data spread or data variability of a distribution is the extent to which the scores vary around their central tendency. Common methods to investigate the variability in data sets are the range, the interquartile range, and the standard deviation.

Key concepts covered: range, interquartile range, box and whisker plots, mean deviation, standard deviation, measuring variability in grouped data, Chebyshev’s theorem.

5. Normal distribution or Gaussian distribution

Normal distribution or Gaussian distribution or Gauss distribution or Laplace–Gauss distribution, in probability theory, is a type of continuous probability distribution for a real-valued random variable.

Key concepts covered: the normal curve (the normal probability curve), area probability graphs, using tables of areas under the normal curve, using N(0, 1) to investigate N(,2), standard deviation and the normal curve.

6. Normal approximation to the binomial distribution

The binomial distribution is a representation of the results of repeated experiments which are independent and identical and in which there are two possible outcomes each time.

Key concepts covered: the binomial distribution, comparing the binomial distribution to the normal distribution, using the normal distribution to approximate the binomial distribution.

7. Bivariate statistics or two variable statistics

Bivariate statistics or two variable statistics is a type of inferential statistics that deals with the relationship between two variables (e.g., price and demand). Bivariate statistics examines how one variable compares with or influences another variable (e.g., how price can drive demand).

Key concepts covered: scatterplots, correlation, line of best fit, curve of best fit.

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Basic Statistics (Mini-Course Modules)

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