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Analyzing and Visualizing Real-World Data
course content

Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

1. Preprocessing Data: Part I
Loading the DataData TypesColumns OverviewRemoving Characters: Method 1Removing Characters: Method 2Checking ColumnsReplacing Characters
2. Preprocessing Data: Part II
Analyzing ColumnsRemoving and ReplacingProcessing DatesWrong Convertation?Proper ConvertingThat's All?
3. Analyzing Data
Superficial AnalysisThe Most Profitable StoreDate RangeHow Periodic the Data is?The Most Profitable PeriodsThe Best CombinationHolidays EffectWhat Holidays are we Considering?The Most Profitable Holiday
4. Visualizing Data
First Look at the DataComparing DynamicsFinding RelationDoes Temperature Affect?Or is it Holiday Effect?Comparing ShopsAdding Holidays to Comparison

bookThe Most Profitable Periods

Good, now we know that the given data are weekly. Let's find out what weeks were the most profitable by comparing the total for each week.

Task
test

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  1. Group the observations in the df dataframe by the 'Date' column values.
  2. Select the 'Weekly_Sales' column.
  3. Calculate the total (sum) for each group.
  4. Sort values in descending order (not ascending).

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Section 3. Chapter 5
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bookThe Most Profitable Periods

Good, now we know that the given data are weekly. Let's find out what weeks were the most profitable by comparing the total for each week.

Task
test

Swipe to show code editor

  1. Group the observations in the df dataframe by the 'Date' column values.
  2. Select the 'Weekly_Sales' column.
  3. Calculate the total (sum) for each group.
  4. Sort values in descending order (not ascending).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 5
toggle bottom row

bookThe Most Profitable Periods

Good, now we know that the given data are weekly. Let's find out what weeks were the most profitable by comparing the total for each week.

Task
test

Swipe to show code editor

  1. Group the observations in the df dataframe by the 'Date' column values.
  2. Select the 'Weekly_Sales' column.
  3. Calculate the total (sum) for each group.
  4. Sort values in descending order (not ascending).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Good, now we know that the given data are weekly. Let's find out what weeks were the most profitable by comparing the total for each week.

Task
test

Swipe to show code editor

  1. Group the observations in the df dataframe by the 'Date' column values.
  2. Select the 'Weekly_Sales' column.
  3. Calculate the total (sum) for each group.
  4. Sort values in descending order (not ascending).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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