In this lesson, students will learn what data science is, what a data scientist does, and the different types of questions that can be asked about data. Students will learn that statistical questions include computations or finding a relationship or pattern.
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In this lesson, students will learn about the data cycle and apply the first two steps of asking questions and considering data. Students will start a mini-project that spans through the rest of the module by asking a statistical question about a field of interest and gathering and structuring the data. They will also learn about and consider both quantitative and qualitative data.
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In this lesson, students will learn the basics of Python programming in the context of data science. This includes how to define and use variables and lists, how to use comparison and logical operators, and the importance of knowing the different data types used in Python.
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In this lesson, students will learn about Python modules and libraries and how to implement and use them within the editor.
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In this lesson, students will learn how to create a use a Pandas Series. They will also learn and explore measures of central tendency including the mean, median, and mode.
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In this lesson, students will expand their statistical knowledge to include the spread of a dataset. They will learn about and apply measures of spread including standard deviation, variance, range, and interquartile range.
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In this lesson, students will learn how to create a data frame using the Pandas library. They will also learn and use functions to explore a data frame further including which data types are included, the shape of the data frame, the descriptive statistics of the data in each column, and more.
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In this lesson, students will learn how to filter a data frame by selecting and displaying only specific columns. They will also learn how to filter rows displayed by using conditionals. Lastly, students will learn how to change the index used in a data frame and set it to a column of their choice.
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In this lesson, students will define and use functions, along with values in a dataset, to calculate and create new columns of data.
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In this lesson, students will practice collecting, explaining, and presenting the important data and details of their mini-project.
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In this lesson, students review content with a 15 question end-of-module quiz.
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In this lesson, students will explore how data is used in the social sector. They will use this information to help formulate at least three problem statements each with two statistical questions.
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In this lesson, students will learn about big data and cognitive biases. They will reflect on their own potential biases and work forward on their project by finding and considering datasets and further decomposing their problem statement.
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In this lesson, students will learn how to import large datasets. They will also learn how to filter a dataset using index-based selection (iloc) and label-based selection (loc).
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iloc and locIn this lesson, students will learn how to conditionally filter a dataset using label-based selection (loc) and comparison operators.
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locIn this lesson, students will learn the importance of data cleaning and how to do it. Data cleaning deals with fixing or removing incorrect or missing values.
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In this lesson, students will explore datasets using visualizations such as pie charts, boxplots, histograms, and scatterplots.
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In this lesson, students will work on analyzing, explaining, and presenting conclusions found in their data exploration.
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In this lesson, students review content with a 10 question end-of-module quiz.
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In this lesson, students will learn how to use data to support and add to a story. The data story will combine visuals with a compelling narrative to help audiences understand the importance of the data being explained. This story will be told through the lens of promoting change, convincing people to take action, or compelling the readers or consumers of the data story to start a movement.
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In this lesson, students work on their module project by finding and cleaning a dataset that will help them tell their data story.
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In this lesson, students will learn about the importance of data visualization when telling a data story. Students will be using a variety of charts, graphs, images, and other common data visualizations to help to bring meaning and understanding to otherwise complex data.
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In this lesson, students will learn about univariate data and how to visualize and compare datasets using line and bar charts.
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In this lesson, students will learn and use the normal distribution curve to predict the likelihood of certain events.
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In this lesson, students will apply what they have learned about univariate data visualizations to explore how these may help tell their data story for the module project.
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In this lesson, students will learn about correlations and causations. They will reflect on whether causation follows a correlation or if a moderating or mediating variable is responsible for the correlation.
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In this lesson, students will learn and apply aspects of linear regression such as finding the line of best fit and using a model to predict the outcome to different values.
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In this lesson, students will apply what they have learned about bivariate data visualizations to explore how these may help tell their data story for the module project.
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In this lesson, students will work on their module projects by applying what they have learned to create a data story.
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In this lesson, students review content with a 10 question end-of-module quiz.
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In this lesson, students will be introduced to the module project. They will take a look at how the data is used in the business world to improve aspects of the business as well as predict future outcomes.
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In this lesson, students will learn how to determine the quality of a dataset. They will explore a few raw datasets to access their quality and completeness.
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In this lesson, students will practice aggregating data by using different sort and group functions and parameters.
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In this lesson, students will practice combining data by using different concatenation and merging techniques.
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In this lesson, students will work on their module projects by gathering and combining data from multiple sources. They will check the quality of the datasets as well as clean, combine and sort them.
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In this lesson, students will learn different types of bias that can be present and affect data analytics. They will also take time to analyze and interpret their project datasets.
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In this lesson, students will work on their module projects by creating a business report.
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In this lesson, students review content with a 10 question end-of-module quiz.
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