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The High Cadence Transient Survey (HiTS) aims to discover and study transientobjects with characteristic timescales between hours and days, such aspulsating, eclipsing and exploding stars. This survey represents a uniquelaboratory to explore large etendue observations from cadences of about 0.1days and to test new computational tools for the analysis of large data. Thiswork follows a fully \textit{Data Science} approach: from the raw data to theanalysis and classification of variable sources. We compile a catalog of${\sim}15$ million object detections and a catalog of ${\sim}2.5$ millionlight-curves classified by variability. The typical depth of the survey is$24.2$, $24.3$, $24.1$ and $23.8$ in $u$, $g$, $r$ and $i$ bands, respectively.We classified all point-like non-moving sources by first extracting featuresfrom their light-curves and then applying a Random Forest classifier. For theclassification, we used a training set constructed using a combination ofcross-matched catalogs, visual inspection, transfer/active learning and dataaugmentation. The classification model consists of several Random Forestclassifiers organized in a hierarchical scheme. The classifier accuracyestimated on a test set is approximately $97\%$. In the unlabeled data,$3\,485$ sources were classified as variables, of which $1\,321$ wereclassified as periodic. Among the periodic classes we discovered with highconfidence, 1 $\delta$-scutti, 39 eclipsing binaries, 48 rotational variablesand 90 RR-Lyrae and for the non-periodic classes we discovered 1 cataclysmicvariables, 630 QSO, and 1 supernova candidates. The first data release can beaccessed in the project archive of HiTS.

The High Cadence Transient Survey (HITS): Compilation and characterization of light-curve catalogs