diff --git a/source/elements/oneMKL/source/domains/stats/onemkl_stats_dataset.rst b/source/elements/oneMKL/source/domains/stats/onemkl_stats_dataset.rst
index e595031c83..56b2353935 100755
--- a/source/elements/oneMKL/source/domains/stats/onemkl_stats_dataset.rst
+++ b/source/elements/oneMKL/source/domains/stats/onemkl_stats_dataset.rst
@@ -30,10 +30,7 @@ structure dataset (Buffer version)
 
     	explicit dataset(std::int64_t n_dims_, std::int64_t n_observations_,
                     sycl::buffer<Type, 1> observations_, sycl::buffer<Type, 1> weights_ = {0},
-                    sycl::buffer<std::int64_t, 1> indices_ = {0}) :
-                    n_dims(n_dims_), n_observations(n_observations_),
-                    observations(observations_),
-                    weights(weights_), indices(indices_) {};
+                    sycl::buffer<std::int64_t, 1> indices_ = {0});
 
     	std::int64_t n_dims;
     	std::int64_t n_observations;
@@ -91,7 +88,7 @@ structure dataset (Buffer version)
             explicit dataset::dataset(std::int64_t n_dims_, std::int64_t n_observations_,
                 sycl::buffer<Type, 1> observations_,
                 sycl::buffer<Type, 1> weights_ = {0},
-                sycl::buffer<std::int64_t, 1> indices_ = {0})
+                sycl::buffer<std::int64_t, 1> indices_ = {0});
 
         .. container:: section
 
@@ -102,8 +99,8 @@ structure dataset (Buffer version)
             	* `n_dims_` is the number of dimensions
             	* `n_observations_` is the number of observations
             	* `observations_` is the matrix of observations
-            	* `weights_` is an optional parameter, represents array of weights for observations (of size `n_observations`). If the parameter is not specified, each observation is assigned a weight equal 1.
-            	* `indices_` is an optional parameter, represents array of dimensions that are processed (of size `n_dims`). If the parameter is not specified, all dimensions are processed.
+            	* `weights_` is an optional parameter, represents an array of weights for observations (of size `n_observations`). If the parameter is not specified, each observation is assigned a weight equal 1.
+            	* `indices_` is an optional parameter, represents an array of dimensions that are processed (of size `n_dims`). If the parameter is not specified, all dimensions are processed.
 
         .. container:: section
 
@@ -125,10 +122,7 @@ structure dataset (USM version)
     template<layout ObservationsLayout, typename Type>
 	struct dataset<Type*, ObservationsLayout> {
     	explicit dataset(std::int64_t n_dims_, std::int64_t n_observations_, Type* observations_,
-                    Type* weights_ = nullptr, std::int64_t* indices_ = nullptr) :
-                    n_dims(n_dims_), n_observations(n_observations_),
-                    observations(observations_),
-                    weights(weights_), indices(indices_) {};
+                    Type* weights_ = nullptr, std::int64_t* indices_ = nullptr);
 
     	std::int64_t n_dims;
     	std::int64_t n_observations;
@@ -186,7 +180,7 @@ structure dataset (USM version)
             explicit dataset::dataset(std::int64_t n_dims_, std::int64_t n_observations_,
                 Type* observations_,
                 Type* weights_ = nullptr,
-                std::int64_t* indices_ = nullptr)
+                std::int64_t* indices_ = nullptr);
 
         .. container:: section
 
@@ -197,8 +191,8 @@ structure dataset (USM version)
             	* `n_dims_` is the number of dimensions
             	* `n_observations_` is the number of observations
             	* `observations_` is the matrix of observations
-            	* `weights_` is an optional parameter, represents array of weights for observations (of size `n_observations`). If the parameter is not specified, each observation is assigned a weight equal 1.
-            	* `indices_` is an optional parameter, represents array of dimensions that are processed (of size `n_dims`). If the parameter is not specified, all dimensions are processed.
+            	* `weights_` is an optional parameter, represents an array of weights for observations (of size `n_observations`). If the parameter is not specified, each observation is assigned a weight equal 1.
+            	* `indices_` is an optional parameter, represents an array of dimensions that are processed (of size `n_dims`). If the parameter is not specified, all dimensions are processed.
 
         .. container:: section
 
diff --git a/source/elements/oneMKL/source/domains/stats/onemkl_stats_usage_model.rst b/source/elements/oneMKL/source/domains/stats/onemkl_stats_usage_model.rst
index eee2dc16de..bb6d634ab3 100755
--- a/source/elements/oneMKL/source/domains/stats/onemkl_stats_usage_model.rst
+++ b/source/elements/oneMKL/source/domains/stats/onemkl_stats_usage_model.rst
@@ -20,92 +20,32 @@ A typical algorithm for summary statistics is as follows:
 The following example demonstrates how to calculate mean values for a 3-dimensional dataset filled with random numbers. For dataset creation, the :ref:`onemkl_stats_make_dataset` helper function is used.
 
 
-Buffer-based example
---------------------
-
-.. code-block:: cpp
-
-    #include <iostream>
-    #include <vector>
-
-    #include "CL/sycl.hpp"
-    #include "oneapi/mkl/stats.hpp"
-
-    int main() {
-        sycl::queue queue;
-
-        const size_t n_observations = 1000;
-        const size_t n_dims = 3;
-        std::vector<float> x(n_observations * n_dims);
-        // fill x storage with random numbers
-        for(int i = 0; i < n_dims, i++) {
-          for(int j = 0; j < n_observations; j++) {
-                x[j + i * n_observations] = float(std::rand()) / float(RAND_MAX);
-            }
-        }
-        //create buffer for dataset
-        sycl::buffer<float, 1> x_buf(x.data(), x.size());
-        // create buffer for mean values
-        sycl::buffer<float, 1> mean_buf(n_dims);
-        // create oneapi::mkl::stats::dataset
-        auto dataset = oneapi::mkl::stats::make_dataset<oneapi::mkl::stats::layout::row_major>(n_dims, n_observations, x_buf);
-
-
-        oneapi::mkl::stats::mean(queue, dataset, mean_buf);
-
-
-        // create host accessor for mean_buf to print results
-        auto acc = mean_buf.template get_access<sycl::access::mode::read>();
-
-
-        for(int i = 0; i < n_dims; i++) {
-          std::cout << "Mean value for dimension " << i << ": " << acc[i] << std::endl;
-        }
-        return 0;
-    }
-
-
 USM-based example
 -----------------
 
 .. code-block:: cpp
 
-    #include <iostream>
-    #include <vector>
-
-    #include "CL/sycl.hpp"
     #include "oneapi/mkl/stats.hpp"
 
     int main() {
         sycl::queue queue;
 
-        const size_t n_observations = 1000;
-        const size_t n_dims = 3;
+        constexpr std::size_t n_observations = 1000;
+        constexpr std::size_t n_dims = 3;
 
-        sycl::usm_allocator<float, sycl::usm::alloc::shared> allocator(queue);
+        // allocate Unified Shared Memory for the dataset of the size n_observations * n_dims and fill it with any data
+        // allocate Unified Shared Memory for the mean output of the size n_dims
 
-        std::vector<float, decltype(allocator)> x(n_observations * n_dims, allocator);
-        // fill x storage with random numbers
-        for(int i = 0; i < n_dims, i++) {
-          for(int j = 0; j < n_observations; j++) {
-                x[j + i * n_observations] = float(std::rand()) / float(RAND_MAX);
-            }
-        }
-        std::vector<float, decltype(allocator)> mean_buf(n_dims, allocator);
         // create oneapi::mkl::stats::dataset
-        auto dataset = oneapi::mkl::stats::make_dataset<oneapi::mkl::stats::layout::row_major>(n_dims,  n_observations, x);
-
-       sycl::event event = oneapi::mkl::stats::mean(queue, dataset, mean);
-       event.wait();
-       for(int i = 0; i < n_dims; i++) {
-         std::cout << "Mean value for dimension " << i << ": " << mean[i] << std::endl;
-       }
-       return 0;
-    }
+        auto dataset = oneapi::mkl::stats::make_dataset<oneapi::mkl::stats::layout::row_major>(n_dims, n_observations, dataset_ptr);
 
+        // call statistics computation routine
+        auto event = oneapi::mkl::stats::mean(queue, dataset, mean_ptr);
 
-.. rubric:: USM usage
+        // wait until computations are completed
+        event.wait();
 
-You can also use USM with raw pointers by using the sycl::malloc_shared/malloc_device functions.
+        // ...
+    }
 
 **Parent topic:** :ref:`onemkl_stats`