PM01-0215. Técnicas para resolver problemas de datos ausentes

Limpieza y validación de datos

La presencia de datos ausentes en tus libros de trabajo, puede distorsionar los resultados de tus análisis, llevándote a conclusiones erróneas.

[{"selector":"#anim-db9daab8-9e56-4f3d-afee-9915f3d6a0f5","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-d2685420-3865-4747-a102-27fdb784f1d9","keyframes":{"transform":["translate3d(118.95425%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

La toma de decisiones se basa en la información disponible.

[{"selector":"#anim-39e4b275-2868-4bb0-a7ec-a07f854b65c7","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-e51d8717-677d-4589-8e52-aaefe5c5b46b","keyframes":{"transform":["translate3d(118.95425%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-37d5ab11-b4a3-4a2e-a7aa-889f5d03f34e","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-28e2288a-9e77-4cc3-85f7-82f65d667be4","keyframes":{"transform":["translate3d(118.95425%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

Por ello, al solucionar este problema, garantizas que tus resultados sean precisos y confiables.

[{"selector":"#anim-8dda90dc-71fb-49e5-8906-09398f9aacbf","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-75e3e545-0cfa-4c27-86dd-deba44ed1684","keyframes":{"transform":["translate3d(118.95425%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

Aprende cómo hacerlo con la siguiente técnica:

[{"selector":"#anim-ff584015-a825-45c7-a7d2-a12d2369dba9","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-769f2363-c471-4c99-bd0b-0c943ff72fb4","keyframes":{"transform":["translate3d(-114.8265%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-e8f35d9f-f072-4486-ac69-f46a6c24be26","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-b69e66c8-aeab-4c1c-83c8-1312944d2e82","keyframes":{"transform":["translate3d(-386.51686%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-ccce6bfc-0322-408d-8ce6-dbd4dddde7f9","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.4, 0.4, 0.0, 1)","fill":"both"}] [{"selector":"#anim-d401c5ae-0d1f-4933-8a82-9262bfcef6e7","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-c32b88fb-6c29-4625-9373-15abfe7f5503","keyframes":{"transform":["translate3d(100.00000%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] Aplica Burst with Arrow

[{"selector":"#anim-0fba3000-2906-4c7f-988c-f4ad78064be7","keyframes":[{"offset":0,"transform":"translate3d(0, -166.2263%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.29,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.45,"transform":"translate3d(0, -46.74283556%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.61,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.71,"transform":"translate3d(0, -15.89123428%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.8,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.85,"transform":"translate3d(0, -5.967524170000001%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.92,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.96,"transform":"translate3d(0, -2.59313028%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":1,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"}],"delay":0,"duration":1400,"fill":"both"}]

Limpieza y validación de datos

Técnica para resolver problemas de datos ausentes

La presencia de datos ausentes en tus libros de trabajo, puede distorsionar los resultados de tus análisis, llevándote a conclusiones erróneas.

La toma de decisiones se basa en la información disponible.

Si hay lagunas en tus datos, las decisiones pueden ser incompletas o sesgadas.

Por ello, al solucionar este problema, garantizas que tus resultados sean precisos y confiables.

Aprende cómo hacerlo con la siguiente técnica: