diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index 9785c3e5517..b0e33e8b9ce 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -1592,10 +1592,8 @@ def build_column(
             children=children,
         )
     elif dtype.type is np.datetime64:
-        if data is None:
-            raise TypeError("Must specify data buffer")
         return cudf.core.column.DatetimeColumn(
-            data=data,
+            data=data,  # type: ignore[arg-type]
             dtype=dtype,
             mask=mask,
             size=size,
@@ -1603,10 +1601,8 @@ def build_column(
             null_count=null_count,
         )
     elif isinstance(dtype, pd.DatetimeTZDtype):
-        if data is None:
-            raise TypeError("Must specify data buffer")
         return cudf.core.column.datetime.DatetimeTZColumn(
-            data=data,
+            data=data,  # type: ignore[arg-type]
             dtype=dtype,
             mask=mask,
             size=size,
@@ -1614,10 +1610,8 @@ def build_column(
             null_count=null_count,
         )
     elif dtype.type is np.timedelta64:
-        if data is None:
-            raise TypeError("Must specify data buffer")
         return cudf.core.column.TimeDeltaColumn(
-            data=data,
+            data=data,  # type: ignore[arg-type]
             dtype=dtype,
             mask=mask,
             size=size,
diff --git a/python/cudf/cudf/core/column/datetime.py b/python/cudf/cudf/core/column/datetime.py
index 1dbc94384d3..d0ea4612a1b 100644
--- a/python/cudf/cudf/core/column/datetime.py
+++ b/python/cudf/cudf/core/column/datetime.py
@@ -24,6 +24,7 @@
     get_compatible_timezone,
     get_tz_data,
 )
+from cudf.core.buffer import Buffer
 from cudf.core.column import ColumnBase, as_column, column, string
 from cudf.core.column.timedelta import _unit_to_nanoseconds_conversion
 from cudf.utils.dtypes import _get_base_dtype
@@ -34,10 +35,8 @@
         ColumnBinaryOperand,
         DatetimeLikeScalar,
         Dtype,
-        DtypeObj,
         ScalarLike,
     )
-    from cudf.core.buffer import Buffer
     from cudf.core.column.numerical import NumericalColumn
 
 if PANDAS_GE_220:
@@ -207,30 +206,39 @@ class DatetimeColumn(column.ColumnBase):
     def __init__(
         self,
         data: Buffer,
-        dtype: DtypeObj,
+        size: int | None,
+        dtype: np.dtype | pd.DatetimeTZDtype,
         mask: Buffer | None = None,
-        size: int | None = None,  # TODO: make non-optional
         offset: int = 0,
         null_count: int | None = None,
+        children: tuple = (),
     ):
-        dtype = cudf.dtype(dtype)
-        if dtype.kind != "M":
-            raise TypeError(f"{self.dtype} is not a supported datetime type")
-
+        if not isinstance(data, Buffer):
+            raise ValueError("data must be a Buffer.")
+        dtype = self._validate_dtype_instance(dtype)
         if data.size % dtype.itemsize:
             raise ValueError("Buffer size must be divisible by element size")
         if size is None:
             size = data.size // dtype.itemsize
             size = size - offset
+        if len(children) != 0:
+            raise ValueError(f"{type(self).__name__} must have no children.")
         super().__init__(
-            data,
+            data=data,
             size=size,
             dtype=dtype,
             mask=mask,
             offset=offset,
             null_count=null_count,
+            children=children,
         )
 
+    @staticmethod
+    def _validate_dtype_instance(dtype: np.dtype) -> np.dtype:
+        if not (isinstance(dtype, np.dtype) and dtype.kind == "M"):
+            raise ValueError("dtype must be a datetime, numpy dtype")
+        return dtype
+
     def __contains__(self, item: ScalarLike) -> bool:
         try:
             ts = pd.Timestamp(item).as_unit(self.time_unit)
@@ -858,21 +866,30 @@ class DatetimeTZColumn(DatetimeColumn):
     def __init__(
         self,
         data: Buffer,
+        size: int | None,
         dtype: pd.DatetimeTZDtype,
         mask: Buffer | None = None,
-        size: int | None = None,
         offset: int = 0,
         null_count: int | None = None,
+        children: tuple = (),
     ):
         super().__init__(
             data=data,
-            dtype=_get_base_dtype(dtype),
-            mask=mask,
             size=size,
+            dtype=dtype,
+            mask=mask,
             offset=offset,
             null_count=null_count,
+            children=children,
         )
-        self._dtype = get_compatible_timezone(dtype)
+
+    @staticmethod
+    def _validate_dtype_instance(
+        dtype: pd.DatetimeTZDtype,
+    ) -> pd.DatetimeTZDtype:
+        if not isinstance(dtype, pd.DatetimeTZDtype):
+            raise ValueError("dtype must be a pandas.DatetimeTZDtype")
+        return get_compatible_timezone(dtype)
 
     def to_pandas(
         self,
diff --git a/python/cudf/cudf/core/column/timedelta.py b/python/cudf/cudf/core/column/timedelta.py
index ba0dc4779bb..6b6f3e517a8 100644
--- a/python/cudf/cudf/core/column/timedelta.py
+++ b/python/cudf/cudf/core/column/timedelta.py
@@ -75,28 +75,33 @@ class TimeDeltaColumn(ColumnBase):
     def __init__(
         self,
         data: Buffer,
-        dtype: Dtype,
-        size: int | None = None,  # TODO: make non-optional
+        size: int | None,
+        dtype: np.dtype,
         mask: Buffer | None = None,
         offset: int = 0,
         null_count: int | None = None,
+        children: tuple = (),
     ):
-        dtype = cudf.dtype(dtype)
-        if dtype.kind != "m":
-            raise TypeError(f"{self.dtype} is not a supported duration type")
+        if not isinstance(data, Buffer):
+            raise ValueError("data must be a Buffer.")
+        if not (isinstance(dtype, np.dtype) and dtype.kind == "m"):
+            raise ValueError("dtype must be a timedelta numpy dtype.")
 
         if data.size % dtype.itemsize:
             raise ValueError("Buffer size must be divisible by element size")
         if size is None:
             size = data.size // dtype.itemsize
             size = size - offset
+        if len(children) != 0:
+            raise ValueError("TimedeltaColumn must have no children.")
         super().__init__(
-            data,
+            data=data,
             size=size,
             dtype=dtype,
             mask=mask,
             offset=offset,
             null_count=null_count,
+            children=children,
         )
 
     def __contains__(self, item: DatetimeLikeScalar) -> bool: