Alpha & Ωmega

1

In order to investigate a possible story, upload data and specify what you want to predict. A & Ω will try to identify patterns. Ask A & Ω questions and it will respond back with it's predictions!

Example:

A journalist wants to explore an alternate election prediction model, using various economic and political indicators instead of polling data-- and also deal with the challenges of model building when there is very little training data.
let's suppose that the journalist uses data sources and creates a csv from:

Historical presidential approval ratings (highest and lowest for each president) from Wikipedia
GDP growth in election year from World Bank

*Numbers on the above table are given as an example of how the **A & Ω** works, do not correspond to the real percentage result, election forecast of Year GDP growth.
Year	Name of president	Name of political party	Number of terms	% result in last election	Election forecast (%)	Year GDP growth	Winning party
1972	Nixon	Party 2	1	35%	41%	2.6	Party 1
1976	Nixon	Party 2	2	38%	31%	5.8	Party 1
1968	Johnson	Party 1	2	35%	32%	4.8	Party 2
...	...	Party 2	1	39%	35%	5.3	Party 2
...	...	Party 2	2	25%	24%	5.4	Party 1
...	Obama	Party 1	2	...	...	...	Party 1
...	Trump	Party 2	1	...	...	...	Party 2

2

The app informs the user to select
a column of interest. For example, "Winning party".<br>The questions the app will answer is:
Can we predict the value of this column, by taking into account all other data?

The app informs the user to select a column of interest. For example, "Winning party".
The questions the app will answer is: Can we predict the value of this column, by taking into account all other data?

3

User selects the "Winning Party" column. The app will try to predict the winning party based on the other columns

4

User selects to run the analysis, and waits for the magic to happen. Alpha & omega will try to make sense of your data, identify patterns and create a prediction model.

5

Alpha & Omega is now ready to predict the "Winner". You are asked to provide new data.

Number of terms	% result in last election	Election forecast (%)	Year GDP growth
To be completed by journalist	To be completed by journalist	To be completed by journalist	To be completed by journalist

6

And click an option <b>"Make a prediction for "Winner""</b>

And click an option "Make a prediction for "Winner""

1

In order to investigate a possible story, explore if A relates to B. For example, height and weight are related; taller people tend to be heavier than shorter people. Upload your data and A & Ω will find these for you.

Example:

Let's say a journalist wants to explore if demographic and economic factors affect the University Success rating of teenagers. She has collected the following data:

*Numbers on the above table are given as an example of how the **A & Ω** works, do not correspond to the real percentage result, election forecast of Year GDP growth.
Family income per year	Neighborhood	Average number of children per family	University success rating
20.000€	Neighborhood 1	27	5%
25.000€	Neighborhood 2	17	17%
...
30.000€	Neighborhood 3	11	25%

2

The app informs the user that it will try to identify interesting correlations between any of the selected columns

3

User selects to run the analysis. This may take up to 10 minutes.

4

The following can happen when the analysis completes:

The application displays a message that
We cannot find interesting correlations with this dataset.
The application displays a message
We have identified 2 interesting correlations

If the user opens the project to view it, they should be able to view the data and the correlation results

It seems like "Family income per year" is correlated with "University success rating" The more "Family income per year" increases, the more "University success rating" increases.
It seems like "Average number of children per family" is correlated with "University success rating" The more "Average number of children per family" increases, the more "University success rating" decreases.

1

In order to investigate a possible story, upload your data to see if there is an outlier

Example:

A journalist has collected some information regarding the perception of government corruption across various countries. They would like to understand if extreme observations/cases exist in this data that would be interesting to focus on.

Country	Percentage of population that has a BSc degree	GDP	Percentage of people who believe that the government is corrupted
Country 1	20%	28.000€	50%
Country 2	20%	32.000€	20%
Country 3	22%	29.000€	25%
...
Country 200	21%	30.000€	22%

2

The app informs the user that it will try to identify extreme observations between any of the selected columns

3

User selects to run the analysis. This may take up to 10 minutes.

4

The following can happen when the analysis completes:

The application displays a message that
We cannot find extreme observations with this dataset.
The application displays a message
We have identified 1 outlier

Country 1 seems to be an outlier (based on the "percentage of people who believe that the government is corrupted") The application will highlight the outliers for the Journalist.

Country	Percentage of population that has a BSc degree	GDP	Percentage of people who believe that the government is corrupted
Country 1	20%	28.000€	50%

How does it work?

Identify stories, causations and correlations in date, without the need of statistical or machine learning expertise.

Step 1 | Upload your data

Create a data journalism project

Upload your spreadsheet and navigate through a broad range of pre-configured, ready to use recipes.

Step 2 | Employ A.I.

Let the system do the rest

Find correlations, outliers, build prediction models without having any knowledge of statistics, machine learning or programming.

Step 3 | Tell the story

View the results

Structure and observe data correlations, taking heed of common pitfalls. Identify what to research. Investigate and narrate about a casual model of the world.

How it works

Recipe 1 | Prediction

Recipe 2 | Correlation

Recipe 3 | Outlier Detection

Create a data journalism project

Let the system do the rest

View the results