diff --git a/_sources/DataEthics/introduction.rst b/_sources/DataEthics/introduction.rst index b15538e..82bfa9b 100644 --- a/_sources/DataEthics/introduction.rst +++ b/_sources/DataEthics/introduction.rst @@ -24,12 +24,14 @@ merging data from multiple sources. They should ask questions such as: - Will the ethics of the project stay the same after the share and/or merge? - Does the share and/or merger reveal information that could be harmful to the privacy of people involved in the data? -Learning Goals --------------- +Learning Goals and Objectives +----------------------------- +Learning goals: + - Learn about ethical practices when using data. -Learning Objectives -------------------- +Learning objectives: + - Learn to identify data that are free to use. - Learn to check if screen scraping is legal on different websites. - Understand the ethical wrongs of misrepresenting data. diff --git a/_sources/Instacart/introduction.rst b/_sources/Instacart/introduction.rst index 52252f5..e91a7e0 100644 --- a/_sources/Instacart/introduction.rst +++ b/_sources/Instacart/introduction.rst @@ -8,15 +8,16 @@ to purchase. We will also explore tools such as recommender systems, which are a that predict consumer’s preferences. We will learn how to use Python libraries to find common purchasing combinations and consumer’s purchasing preferences in a large data set. -Learning Goals ---------------- +Learning Goals and Objectives +----------------------------- + +Learning goals: - Look for common patterns in a large data set - Analyze data and determine if it is sparse - Visualize associations between different items in a large data set -Learning Objectives ---------------------- +Learning objectives: - Find relationships between a particular group of items in an extensive data set using the Market basket analysis technique - Construct, analyze, and retrieve information from an item-item matrix diff --git a/_sources/Instacart/market_basket.rst b/_sources/Instacart/market_basket.rst index 6fb5369..62954eb 100644 --- a/_sources/Instacart/market_basket.rst +++ b/_sources/Instacart/market_basket.rst @@ -1200,7 +1200,7 @@ used in industry. From 0 to 200 (on the x-axis) there is one bar that goes to 5,000 and from 200 to 400 the bar goes up to less than 200. -Experimenting with Item-Item Recommendations +Experimenting With Item-Item Recommendations -------------------------------------------- - The histogram above shows that the vast majority of the items are in the diff --git a/_sources/Introduction/introduction.rst b/_sources/Introduction/introduction.rst index c621fce..7a207f2 100644 --- a/_sources/Introduction/introduction.rst +++ b/_sources/Introduction/introduction.rst @@ -11,8 +11,23 @@ It will explore the history and current state of the discipline, explaining how data science began and where it will be going in the future. We will also explore how data science leverages data analysis and data visualization. -Learning Goals --------------- +Learning Goals and Objectives +----------------------------- + +Goals and objectives refer to the desired outcomes that an entity, person or business, +wants to achieve. However, there is a significant difference between them. + +A **goal** is a broad, often non-measurable, and intangible statement focusing on the desired +outcome. A goal seeks to achieve the larger, conceptual mission of the individual or the +business, and it does not define methods for attaining that intended outcome. Generally, +goals span over a long time frame. + +An **objective**, on the other hand, is a specific, actionable, and tangible target that can be +achieved within a shorter specified time frame. While goals target the larger mission, +objectives are involved in achieving the goals. + +Learning goals: + - Understand the importance of data collection and its implementation. - Gain awareness of how broad data collection is in all subjects. - Gain an introduction to what a Data Scientist does. @@ -20,8 +35,7 @@ Learning Goals - Understand what it takes to gain and better skills: **Learning Zone** vs **Performance Zone**. - Learn the difference between Data Science and Data Analytics. -Learning Objectives ---------------------- +Learning objectives: - Be able to identify different steps along different data science pipelines, recognizing all the previous and future steps. - Be able to identify if you are in a performance zone or learning zone and transition between them as necessary. @@ -48,7 +62,7 @@ you: what you buy, what you read, where you eat, where you stay, how and when you travel, and so much more. By 2025, it is estimated that 463 exabytes of data will be created each day globally, and the entire digital universe is expected to reach 44 zettabytes by 2020. `This would mean there would be 40 times more bytes than there are stars in the observable universe. `_ -What does it all mean? +What Does It All Mean? ---------------------- Often, this data is collected and stored with little idea about how to use it, @@ -79,7 +93,7 @@ efficient and comfortable footwear `understanding this data `_. -What does a data scientist do? +What Does a Data Scientist Do? ------------------------------ .. youtube:: 0tuEEnL61HM @@ -284,7 +298,7 @@ ability to join these solutions together to solve increasingly challenging problems with real-world applications. -Datasets in this Book +Datasets in This Book --------------------- Every chapter in this book uses data. The data that we use is real world data diff --git a/_sources/MovieData/indexing.rst b/_sources/MovieData/indexing.rst index 6208f5f..74874f4 100644 --- a/_sources/MovieData/indexing.rst +++ b/_sources/MovieData/indexing.rst @@ -4,7 +4,7 @@ http://creativecommons.org/licenses/by-sa/4.0/. -Numbers as Indices +Numbers As Indices ================== Enough about movie budgets, it's time to budget my time instead. Because I @@ -167,4 +167,4 @@ But what is the 155th shortest movie in this collection? :option_2: Within reach if I try my hardest :option_3: Out of reach no matter how hard I try - For me to master the things taught in this lesson feels... \ No newline at end of file + For me to master the things taught in this lesson feels... diff --git a/_sources/MovieData/introduction.rst b/_sources/MovieData/introduction.rst index 67b7c6d..1c30fc3 100644 --- a/_sources/MovieData/introduction.rst +++ b/_sources/MovieData/introduction.rst @@ -11,13 +11,16 @@ and Excel are great ways to visualize and manipulate data, they are not as versa as we might want. That is where **data frames** come in. In this chapter, you will be introduced to data frames and learn to use them to obtain information from data. -Learning Goals ---------------- +Learning Goals and Objectives +----------------------------- + +Learning goals: + - Learn how to create and manipulate a ``DataFrame``. - Learn how to use data from multiple ``DataFrames``. -Learning Objectives --------------------- +Learning objectives: + - Be able to use Jupyter Notebooks and Pandas. - Be able to import data into a ``DataFrame``. - Be able to manipulate ``DataFrames`` to gain specific information. diff --git a/_sources/MovieData/multiple_df.rst b/_sources/MovieData/multiple_df.rst index 3b418f8..4cfa79a 100644 --- a/_sources/MovieData/multiple_df.rst +++ b/_sources/MovieData/multiple_df.rst @@ -4,7 +4,7 @@ http://creativecommons.org/licenses/by-sa/4.0/. -Dealing with Multiple DataFrames +Dealing With Multiple DataFrames ================================ Forget about budget or runtimes as criteria for selecting a movie, let's take a @@ -76,7 +76,7 @@ difference between the ``vote_average`` and ``my_vote`` and divide it by .. fillintheblank:: mov_star_wars_difference - What's the percentage difference between the popular rating for Star Wars and my vote + What's the percentage difference between the popular rating for Star Wars and my vote for it? |blank| - :-10: Is the correct answer diff --git a/_sources/MovieData/toctree.rst b/_sources/MovieData/toctree.rst index 274cfe2..479f3d2 100644 --- a/_sources/MovieData/toctree.rst +++ b/_sources/MovieData/toctree.rst @@ -4,7 +4,7 @@ http://creativecommons.org/licenses/by-sa/4.0/. -Learning Pandas with Movie Data +Learning Pandas With Movie Data =============================== .. toctree:: diff --git a/_sources/PredictiveAnalytics/bike_data_starter.rst b/_sources/PredictiveAnalytics/bike_data_starter.rst index ef2c971..a6b2804 100644 --- a/_sources/PredictiveAnalytics/bike_data_starter.rst +++ b/_sources/PredictiveAnalytics/bike_data_starter.rst @@ -1,4 +1,4 @@ -Getting Started with the Bike Data +Getting Started With the Bike Data ================================== In this Lesson, we will be hands on and try out SQL with the Capital @@ -7,7 +7,7 @@ bike sharing dataset, hosted on a SQLLite database. You don't have to do anythin -Verify access to the dataset +Verify Access to The Dataset ---------------------------- Let’s verify that you have access to the dataset by running a simple SQL diff --git a/_sources/PredictiveAnalytics/introduction.rst b/_sources/PredictiveAnalytics/introduction.rst index 7c69a29..f269aa0 100644 --- a/_sources/PredictiveAnalytics/introduction.rst +++ b/_sources/PredictiveAnalytics/introduction.rst @@ -12,14 +12,17 @@ common ways of storing data is in a database. In this chapter, we will use SQLli send queries to different databases, and import data from those databases into a pandas DataFrame. Then we will use the data to model different situations and predict outcomes. -Learning Goals --------------- +Learning Goals and Objectives +----------------------------- + +Learning goals: + - Manipulate data from a database using Structured Query Language. - Use linear regression to model the relationship between predicted data and actual data. - Create models that we can use to predict outcomes. -Learning Objectives -------------------- +Learning objectives: + - Import a SQL database into a Pandas DataFrame. - Retrieve, sort, and aggregate data from a database. - Join and extract data from multiple databases. diff --git a/_sources/PredictiveAnalytics/introduction_to_SQL.rst b/_sources/PredictiveAnalytics/introduction_to_SQL.rst index c890cdc..77bdb15 100644 --- a/_sources/PredictiveAnalytics/introduction_to_SQL.rst +++ b/_sources/PredictiveAnalytics/introduction_to_SQL.rst @@ -3,7 +3,7 @@ International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/. -Exploring Bike Rental Data with SQL +Exploring Bike Rental Data With SQL =================================== .. figure:: https://imgs.xkcd.com/comics/exploits_of_a_mom.png diff --git a/_sources/PredictiveAnalytics/predicting_rentals.rst b/_sources/PredictiveAnalytics/predicting_rentals.rst index ea3952c..4d5a09e 100644 --- a/_sources/PredictiveAnalytics/predicting_rentals.rst +++ b/_sources/PredictiveAnalytics/predicting_rentals.rst @@ -208,7 +208,7 @@ Compare your graph to this one after you have made it. :modaltitle: Predicted Versus Actual Daily Rentals V1 .. image:: Figures/regression_compare_1.png - :alt: Linear Regression model with ride_count as the y axis and daynum as the x axis. + :alt: Linear Regression model with ride_count as the y axis and daynum as the x axis. What do you think of the model so far? You are probably a bit disappointed, both @@ -237,7 +237,7 @@ look at the time series of daily rentals. .. figure:: Figures/year_one_ts.png - :alt: Line graph of bike rentals with duration (0 to 6,000) as the y axis and start_date (by months of first year) as the x axis. + :alt: Line graph of bike rentals with duration (0 to 6,000) as the y axis and start_date (by months of first year) as the x axis. The representation of the date we chose is simple, but you know from @@ -357,7 +357,7 @@ matches this one. .. reveal:: modelv25_viz .. image:: Figures/modelv25_compare.png - :alt: Scatter plot with y-axis set as actual (shown in blue) and preds (shown in red), and x-axis as the number of days. + :alt: Scatter plot with y-axis set as actual (shown in blue) and preds (shown in red), and x-axis as the number of days. Version 3.0 @@ -509,7 +509,7 @@ One really common method for transforming the data is to use min-max scaling. This will ensure that all of your values are between 0 and 1. -Where to go from here? +Where To Go From Here? ---------------------- In the introduction to this textbook, we showed you this diagram. Take a look at diff --git a/_sources/PredictiveAnalytics/time_series_bikes.rst b/_sources/PredictiveAnalytics/time_series_bikes.rst index 0c2ac4d..3b02eb2 100644 --- a/_sources/PredictiveAnalytics/time_series_bikes.rst +++ b/_sources/PredictiveAnalytics/time_series_bikes.rst @@ -218,7 +218,7 @@ of the week with just the business days. d+b -Indexing with a DatetimeIndex +Indexing With a DatetimeIndex ----------------------------- Using a timestamp as an index gives you some additional power. For example, you @@ -260,7 +260,7 @@ common models to start with for making predictions is "linear regression". But first, let's take a break for some pizza. -Working with ZIP Files (Optional) +Working With ZIP Files (Optional) --------------------------------- In many cases, large data files are available in compressed format. Usually, this diff --git a/_sources/PythonReview/introduction.rst b/_sources/PythonReview/introduction.rst index 0009fc2..50a87fb 100644 --- a/_sources/PythonReview/introduction.rst +++ b/_sources/PythonReview/introduction.rst @@ -17,13 +17,16 @@ languages in the data science field. You will also set up the tools you will be using throughout the entire course including a type of scientific notebook that allows for a mix of text and code. -Learning Goals --------------- +Learning Goals and Objectives +----------------------------- + +Learning goals: + - Review the fundamental constructs of programming in Python. - Learn to use a type of programming notebook that mixes text and code. -Learning Objectives -------------------- +Learning objectives: + - Recall the fundamentals of programming in Python. - Learn to use the Markdown language. - Learn to set up a Jupyter Notebook or a Google Colaboratory Notebook. diff --git a/_sources/Solver/introduction.rst b/_sources/Solver/introduction.rst index 577e785..07027f3 100644 --- a/_sources/Solver/introduction.rst +++ b/_sources/Solver/introduction.rst @@ -26,13 +26,15 @@ We will use a tool for linear programming called Solver in Google Sheets. Solver can also be used with Microsoft Excel and many other spreadsheet programs. -Learning Goals --------------- +Learning Goals and Objectives +----------------------------- + +Learning goals: + - Explore the ideas and techniques of **optimization**. - Learn how to optimize an **objective function** under specific **constraints**. -Learning Objectives -------------------- +Learning objectives: - Be able to recognize an **objective function** and any **constraints** in a specific problem. - Learn to apply optimization concepts to maximize or minimize an **objective function** or to set it to a specified value. diff --git a/_sources/Statistics/cs1_more_happiness.rst b/_sources/Statistics/cs1_more_happiness.rst index 620f24b..05b3cfe 100644 --- a/_sources/Statistics/cs1_more_happiness.rst +++ b/_sources/Statistics/cs1_more_happiness.rst @@ -131,8 +131,8 @@ mean by dividing our two columns. - :Taiwan.*: Is the correct answer :x: Keep checking - -Joining Data from Other Sources +S +Joining Data From Other Sources ------------------------------- So far, we have limited our analysis to the data provided for us in the original diff --git a/_sources/Statistics/cs1_yearly_happiness.rst b/_sources/Statistics/cs1_yearly_happiness.rst index ea7b478..21c494c 100644 --- a/_sources/Statistics/cs1_yearly_happiness.rst +++ b/_sources/Statistics/cs1_yearly_happiness.rst @@ -6,7 +6,7 @@ .. _CSHappinessComparingYears: -Case Study 1: Comparing Happiness Data across Years +Case Study 1: Comparing Happiness Data Across Years =================================================== We have two files of happiness data, one for 2017 which you have been using, and diff --git a/_sources/Statistics/introduction.rst b/_sources/Statistics/introduction.rst index ef58769..817c5a7 100644 --- a/_sources/Statistics/introduction.rst +++ b/_sources/Statistics/introduction.rst @@ -22,8 +22,10 @@ So, if you are familiar with Microsoft Excel, you will find Google Sheets very e Lastly, we will discuss the difference between **correlation** and **causation** and explore why correlation does not imply causation. Understanding this concept is crucially important for making correct assumptions and decisions when analyzing data. -Learning Goals --------------- +Learning Goals and Objectives +----------------------------- +Learning goals: + - Explore the concepts of descriptive statistics and data visualization. - Distinguish between descriptive and inferential statistics. - Learn to apply the various measures of central tendency and the measures of variability. @@ -31,8 +33,8 @@ Learning Goals - Addressing cells: relative versus absolute, on the same sheet versus across sheets. - Use a spreadsheet to explore data. -Learning Objectives -------------------- +Learning objectives: + - Become adept at importing, organizing, and visualizing data using Google Sheets. - Extrapolate measures of central tendencies and measures of variability of a given data set. - Combine different datasets and use them to extract new data. diff --git a/_sources/TextualAnalysis/graph_relations.rst b/_sources/TextualAnalysis/graph_relations.rst index 14d01b3..5a8f62e 100644 --- a/_sources/TextualAnalysis/graph_relations.rst +++ b/_sources/TextualAnalysis/graph_relations.rst @@ -397,7 +397,7 @@ code. :x: This is the same as before, but with more years -Visualizing the Relationships with a Heatmap +Visualizing the Relationships With a Heatmap -------------------------------------------- We will now look at a way to get a better visual representation of the table we @@ -499,7 +499,7 @@ very satisfying part of programming and data analysis! You have to enjoy your victories while you can. -Visualizing the Relationships with a Graph +Visualizing the Relationships With a Graph ------------------------------------------ The good news is that we have already done most of the hard work in the last diff --git a/_sources/TextualAnalysis/introduction.rst b/_sources/TextualAnalysis/introduction.rst index 6f392d6..8cf64dc 100644 --- a/_sources/TextualAnalysis/introduction.rst +++ b/_sources/TextualAnalysis/introduction.rst @@ -11,16 +11,19 @@ between multiple variables in a data frame. Finally, we will focus on evaluating text to determine whether the text conveys a positive, negative, or neutral sentiment. -Learning Goals ---------------- +Learning Goals and Objectives +----------------------------- + +Learning goals: + - Analyze and measure text complexity - Find relationships in, and make a visual representation, from the text. - Tidy up data to create a proper format for analysis - Graph the relationship between different variables in a data set -Learning Objectives --------------------- +Learning objectives: + - Reshape and merge one data frame to another to create a more precise and consistent data frame - Apply the basic principles of tidying up data - Measure text complexity using the Python package Textatistic diff --git a/_sources/TextualAnalysis/research_questions.rst b/_sources/TextualAnalysis/research_questions.rst index eac1305..30d8dc6 100644 --- a/_sources/TextualAnalysis/research_questions.rst +++ b/_sources/TextualAnalysis/research_questions.rst @@ -4,7 +4,7 @@ http://creativecommons.org/licenses/by-sa/4.0/. -Working with Text +Working With Text ================= Since we are working with data frames, sometimes when extracting text, blank spaces or diff --git a/_sources/TextualAnalysis/text_mining.rst b/_sources/TextualAnalysis/text_mining.rst index 515b1a5..5e8ffa1 100644 --- a/_sources/TextualAnalysis/text_mining.rst +++ b/_sources/TextualAnalysis/text_mining.rst @@ -5,7 +5,7 @@ Text Mining -============== +=========== In this section, we will learn how to read and explore data on a deeper level. We will learn how to use various tools to group and visualize the diverse text in a data set. Keep in mind that you might encounter some errors along the way. diff --git a/_sources/WorldFacts/cs1_exploratory_data_analysis.rst b/_sources/WorldFacts/cs1_exploratory_data_analysis.rst index d494f90..643e1e5 100644 --- a/_sources/WorldFacts/cs1_exploratory_data_analysis.rst +++ b/_sources/WorldFacts/cs1_exploratory_data_analysis.rst @@ -7,7 +7,7 @@ Case Study 1: Exploratory Data Analysis ========================================== -Loading Data into a DataFrame from a CSV File +Loading Data Into a DataFrame From a CSV File --------------------------------------------- The **CSV file** is one of the most common ways you will find data. CSV stands for @@ -495,7 +495,7 @@ mixed between 1 and 2, and 2.5 mixed between 2 and 3. -Visualizing Distribution with Histograms +Visualizing Distribution With Histograms ---------------------------------------- @@ -565,7 +565,7 @@ Practice :x: Try again, the number is less than 15 -Scatter Plots for Discovering Relationships +Scatter Plots For Discovering Relationships ------------------------------------------- Now, let's make a simple **scatter plot** of area versus population of the diff --git a/_sources/WorldFacts/cs1_graphing_infant_mortality.rst b/_sources/WorldFacts/cs1_graphing_infant_mortality.rst index f5ff015..a2069cd 100644 --- a/_sources/WorldFacts/cs1_graphing_infant_mortality.rst +++ b/_sources/WorldFacts/cs1_graphing_infant_mortality.rst @@ -145,7 +145,7 @@ coincidence that they have the same name in this example. :alt: Heat map of the U.S. counties based on relative Unemployment. Colors range from yellow for high unemployment to blue for low employment. -Using a Web API to get Country Codes +Using a Web API to Get Country Codes ------------------------------------ Can you make use of the provided example and the Altair documentation to produce diff --git a/_sources/WorldFacts/cs2_data_analysis.rst b/_sources/WorldFacts/cs2_data_analysis.rst index 7ebad31..d718420 100644 --- a/_sources/WorldFacts/cs2_data_analysis.rst +++ b/_sources/WorldFacts/cs2_data_analysis.rst @@ -13,7 +13,7 @@ For this case study, we will use the World Bank Data on Protecting Minority Inve provided to you in the introduction. -Loading Minority Investors Data into a DataFrame +Loading Minority Investors Data Into a DataFrame ------------------------------------------------- To get our first glimpse at **Pandas** and its capabilities, we will be using the data about the countries we used in the spreadsheet module. @@ -321,7 +321,7 @@ Let's use our handy method that Pandas provides us with, ``describe``, to get so -Visualizing Minority Investors with a Histogram +Visualizing Minority Investors With a Histogram ------------------------------------------------ .. code:: python3 @@ -355,7 +355,7 @@ Let's write all of the above calls into a single line. -Discovering Relationships with Scatter Plots +Discovering Relationships With Scatter Plots -------------------------------------------- We can visualize and show the relationship between data using **scatter plots**. diff --git a/_sources/WorldFacts/cs2_graphing_business_data.rst b/_sources/WorldFacts/cs2_graphing_business_data.rst index bb7f11c..205adba 100644 --- a/_sources/WorldFacts/cs2_graphing_business_data.rst +++ b/_sources/WorldFacts/cs2_graphing_business_data.rst @@ -10,7 +10,7 @@ Case Study 2: Graphing Business Data on a Map In this section, we will explore visualization techniques that use data to display information in a more abstract and helpful format so that the data analysis results are better understood. For this case study, we will focus on graphing business data on a map using Altair. -Getting Country Codes from a Web API +Getting Country Codes From a Web API ------------------------------------ Now that you are familiar with graphing data on a map using Altair from the previous case study. We can diff --git a/_sources/WorldFacts/introduction.rst b/_sources/WorldFacts/introduction.rst index 099d67e..3283ad9 100644 --- a/_sources/WorldFacts/introduction.rst +++ b/_sources/WorldFacts/introduction.rst @@ -16,16 +16,17 @@ we need to find other means to obtain the data we need or to reformat the data i using web scraping methods. Finally, you will be able to use the Pandas pivot table to summarize the data. -Learning Goals ----------------- +Learning Goals and Objectives +----------------------------- + +Learning goals: * Visualize, analyze, and describe data in various formats * Extract data from different sources * Summarizes the data of a large data set -Learning Objectives --------------------- +Learning objectives: * Use Pandas to analyze and describe data * Visualize data with histograms and scatter plots