Adaptive analytics user interfaces

1. A method comprising:

receiving, by a computing device via an analytics user interface, a query to be applied to a dataset, wherein the query comprises at least one keyword;

determining, based on the query and a machine learning module of a recommendation engine, a plurality of recommended result elements and associated visualization elements, wherein the plurality of recommended result elements comprises a plurality of filters to be applied to the dataset, and wherein the machine learning module is trained based on previous queries that comprise the at least one keyword and are associated with the plurality of filters;

providing, via the analytics user interface, the plurality of recommended result elements and associated visualization elements;

receiving, via the analytics user interface, a selection of at least one recommended result element of the plurality of recommended result elements, wherein the at least one recommended result element comprises at least one filter of the plurality of filters;

determining, based on: the query, the at least one recommended result element, and a selection of at least one recommended visualization element, a query result, wherein the query result is associated with: the query, the at least one recommended result element, and the at least one recommended visualization element, and wherein the query result comprises a portion of the dataset resulting from the at least one filter applied to the dataset; and

generating, based on the at least one recommended visualization element, a visualization of the query result.

2. The method of claim 1 , further comprising:

determining, based on the previous queries, a frequency of occurrence for each of a plurality of words associated with the previous queries;

determining, based on the frequency of occurrence for each of the plurality of words, a ranking for each of the plurality of words;

generating, based on the ranking for each of the plurality of words, a training dataset; and

training, based on the training dataset, the machine learning module.

3. The method of claim 1 , wherein the plurality of recommended result elements and associated visualization elements are provided based on an experience level associated with a user of the analytics user interface.

4. The method of claim 1 , wherein the at least one filter is associated with at least one classification variable of the dataset, and wherein the at least one classification variable defines the portion of the dataset based on the at least one filter being applied to the dataset.

5. The method of claim 1 , further comprising:

determining, by the recommendation engine and based on the query, one or more of an analysis intent associated with the query or a context associated with the query, wherein the plurality of recommended result elements is based on one or more of the analysis intent or the context.

6. The method of claim 1 , further comprising:

providing, via the analytics user interface, a plurality of visualization elements associated with the at least one recommended result element, wherein the plurality of visualization elements comprises the at least one recommended visualization element; and

receiving, via the analytics user interface, a selection of the at least one recommended visualization element.

7. A method comprising:

(a) receiving, by a computing device via an analytics user interface, a query, comprising at least one keyword, and a result element, wherein the result element is one of a plurality of result elements and comprises at least one filter to be applied to a dataset;

(b) determining, based on the query and the result element, a query result comprising a portion of the dataset resulting from application of the at least one filter on the dataset;

(c) receiving a selection of a visualization element from a plurality of visualization elements to apply to the query result, wherein the plurality of visualization elements are provided at the analytics user interface based on the result element selected at the analytics user interface;

(d) generating a training dataset based on: the at least one keyword of the query, the at least one filter associated with the result element, and the visualization element; and

(e) training a machine learning module of a recommendation engine using the training dataset, wherein the machine learning module is trained to provide the at least one filter as a recommended result element for a further query that comprises the at least one keyword.

8. The method of claim 7 , further comprising:

(f) repeating steps (a)-(e) for at least one of: each query of a plurality of queries, each result element of the plurality of result elements, or each visualization element of the plurality of visualization elements.

9. The method of claim 8 , wherein each query of the plurality of queries is associated with an experience level of a user of the analytics user interface.

10. The method of claim 9 , wherein step (a) further comprises:

determining, by the recommendation engine and based on the query, one or more of an analysis intent associated with the query or a context associated with the query.

11. The method of claim 10 , wherein the result element is associated with one or more of the analysis intent or the context.

12. The method of claim 7 , wherein the at least one filter is associated with at least one classification variable of the dataset, and wherein the at least one classification variable defines the portion of the dataset based on the at least one filter being applied to the dataset.

13. The method of claim 7 , wherein the visualization element is associated with the result element.

14. A non-transitory computer readable medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to:

receive, via an analytics user interface, a query to be applied to a dataset, wherein the query comprises at least one keyword;

determine, based on the query and a machine learning module of a recommendation engine, a plurality of recommended result elements and associated visualization elements, wherein the plurality of recommended result elements comprises a plurality of filters to be applied to the dataset, and wherein the machine learning module is trained based on previous queries that comprise the at least one keyword and are associated with the plurality of filters;

provide, via the analytics user interface, the plurality of recommended result elements and associated visualization elements;

receive, via the analytics user interface, a selection of at least one recommended result element of the plurality of recommended result elements, wherein the at least one recommended result element comprises at least one filter of the plurality of filters;

determine, based on: the query, the at least one recommended result element, and a selection of at least one recommended visualization element, a query result, wherein the query result is associated with: the query, the at least one recommended result element, and the at least one recommended visualization element, and wherein the query result comprises a portion of the dataset resulting from the at least one filter applied to the dataset; and

generate, based on the at least one recommended visualization element, a visualization of the query result.

15. The non-transitory computer readable medium of claim 14 , wherein the processor executable instructions further cause the at least one processor to:

determine, based on the previous queries, a frequency of occurrence for each of a plurality of words associated with the previous queries;

determine, based on the frequency of occurrence for each of the plurality of words, a ranking for each of the plurality of words;

generate, based on the ranking for each of the plurality of words, a training dataset; and

train, based on the training dataset, the machine learning module.

16. The non-transitory computer readable medium of claim 14 , wherein the plurality of recommended result elements and associated visualization elements are provided based on an experience level associated with a user of the analytics user interface.

17. The non-transitory computer readable medium of claim 14 , wherein the at least one filter is associated with at least one classification variable of the dataset, and wherein the at least one classification variable defines the portion of the dataset based on the at least one filter being applied to the dataset.

18. The non-transitory computer readable medium of claim 14 , wherein the processor executable instructions further cause the at least one processor to:

provide, via the analytics user interface, a plurality of visualization elements associated with the at least one recommended result element, wherein the plurality of visualization elements comprises the at least one recommended visualization element; and

receive, via the analytics user interface, a selection of the at least one recommended visualization element.