diff --git a/README.rst b/README.rst
index f6f790327b..cd0e7fa24d 100644
--- a/README.rst
+++ b/README.rst
@@ -130,9 +130,9 @@ Jupyter ноутбуки с примерами находятся в репоз
 
 Расширенные примеры:
 
-- Задача с кредитным скорингом `binary classification task <https://github.com/aimclub/FEDOT/blob/master/cases/credit_scoring/credit_scoring_problem.py>`__
-- Прогнозирование временных рядов `random process regression <https://github.com/aimclub/FEDOT/blob/master/cases/metocean_forecasting_problem.py>`__
-- Обнаружение спама `natural language preprocessing <https://github.com/aimclub/FEDOT/blob/master/cases/spam_detection.py>`__
+- Задача с кредитным скорингом `binary classification task <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/credit_scoring/credit_scoring_problem.py>`__
+- Прогнозирование временных рядов `random process regression <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/metocean_forecasting_problem.py>`__
+- Обнаружение спама `natural language preprocessing <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/spam_detection.py>`__
 - Предсказание сорта вина `multi-modal data <https://github.com/aimclub/FEDOT/blob/master/examples/advanced/multimodal_text_num_example.py>`__
 
 
diff --git a/README_en.rst b/README_en.rst
index 8c3442f671..0598df8250 100644
--- a/README_en.rst
+++ b/README_en.rst
@@ -131,9 +131,9 @@ Also, external examples are available:
 
 Extended examples:
 
-- Credit scoring problem, i.e. `binary classification task <https://github.com/aimclub/FEDOT/blob/master/cases/credit_scoring/credit_scoring_problem.py>`__
-- Time series forecasting, i.e. `random process regression <https://github.com/aimclub/FEDOT/blob/master/cases/metocean_forecasting_problem.py>`__
-- Spam detection, i.e. `natural language preprocessing <https://github.com/aimclub/FEDOT/blob/master/cases/spam_detection.py>`__
+- Credit scoring problem, i.e. `binary classification task <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/credit_scoring/credit_scoring_problem.py>`__
+- Time series forecasting, i.e. `random process regression <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/metocean_forecasting_problem.py>`__
+- Spam detection, i.e. `natural language preprocessing <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/spam_detection.py>`__
 - Wine variety prediction with `multi-modal data <https://github.com/aimclub/FEDOT/blob/master/examples/advanced/multimodal_text_num_example.py>`__
 
 
diff --git a/cases/evo_operators_comparison/req.txt b/cases/evo_operators_comparison/req.txt
deleted file mode 100644
index eccb2357d1..0000000000
--- a/cases/evo_operators_comparison/req.txt
+++ /dev/null
@@ -1 +0,0 @@
-fedot==0.6.1
\ No newline at end of file
diff --git a/docs/source/advanced/automated_pipelines_design.rst b/docs/source/advanced/automated_pipelines_design.rst
index 16b340efa6..05287d6941 100644
--- a/docs/source/advanced/automated_pipelines_design.rst
+++ b/docs/source/advanced/automated_pipelines_design.rst
@@ -9,4 +9,4 @@ Example of running optimization through the API can be found in the `api classif
 
 If instead users need to customize the optimization algorithm (e.g. with custom genetic operators like mutations or crossover or custom verification rules) then it's possible by directly using `ComposerBuilder` class or one of the optimizers from GOLEM.
 
-Example of a customized usage can be found in `credit scoring case problem <https://github.com/aimclub/FEDOT/blob/master/cases/credit_scoring/credit_scoring_problem_multiobj.py>`_.
+Example of a customized usage can be found in `credit scoring case problem <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/credit_scoring/credit_scoring_problem_multiobj.py>`_.
diff --git a/docs/source/advanced/hyperparameters_tuning.rst b/docs/source/advanced/hyperparameters_tuning.rst
index 3617901dc0..d396f3589e 100644
--- a/docs/source/advanced/hyperparameters_tuning.rst
+++ b/docs/source/advanced/hyperparameters_tuning.rst
@@ -721,8 +721,8 @@ Another examples can be found here:
 * `Tuning pipelines with sparse_lagged / lagged node  <https://github.com/aimclub/FEDOT/blob/master/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py>`_
 * `Topaz multi time series forecasting <https://github.com/aimclub/FEDOT/blob/master/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py>`_
 * `Custom model tuning <https://github.com/aimclub/FEDOT/blob/master/examples/advanced/time_series_forecasting/custom_model_tuning.py>`_
-* `Case: river level forecasting with composer <https://github.com/aimclub/FEDOT/blob/master/cases/river_levels_prediction/river_level_case_composer.py>`_
-* `Case: river level forecasting (manual) <https://github.com/aimclub/FEDOT/blob/master/cases/river_levels_prediction/river_level_case_manual.py>`_
+* `Case: river level forecasting with composer <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/river_levels_prediction/river_level_case_composer.py>`_
+* `Case: river level forecasting (manual) <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/river_levels_prediction/river_level_case_manual.py>`_
 
 **Multitask**
 
diff --git a/docs/source/basics/pipeline_save_load.rst b/docs/source/basics/pipeline_save_load.rst
index 3e2d95adfb..48892cb6ca 100644
--- a/docs/source/basics/pipeline_save_load.rst
+++ b/docs/source/basics/pipeline_save_load.rst
@@ -19,7 +19,7 @@ FEDOT provides methods for saving and loading pipelines in the ``Pipeline`` clas
             .. code-block:: python
 
                 problem = 'classification'
-                train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
+                train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
 
                 baseline_model = Fedot(problem=problem, timeout=1, seed=42)
                 baseline_model.fit(features=train_data_path, target='target', predefined_model='rf')
@@ -62,7 +62,7 @@ FEDOT provides methods for saving and loading pipelines in the ``Pipeline`` clas
             .. code-block:: python
 
                 problem = 'classification'
-                train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
+                train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
 
                 baseline_model = Fedot(problem=problem, timeout=1, seed=42)
                 baseline_model.fit(features=train_data_path, target='target', predefined_model='rf')
@@ -99,7 +99,7 @@ FEDOT provides methods for saving and loading pipelines in the ``Pipeline`` clas
             .. code-block:: python
 
                 problem = 'classification'
-                train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
+                train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
 
                 baseline_model = Fedot(problem=problem, timeout=1, seed=42)
                 baseline_model.fit(features=train_data_path, target='target', predefined_model='rf')
@@ -137,7 +137,7 @@ FEDOT provides methods for saving and loading pipelines in the ``Pipeline`` clas
             .. code-block:: python
 
                 problem = 'classification'
-                train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
+                train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
 
                 baseline_model = Fedot(problem=problem, timeout=1, seed=42)
                 baseline_model.fit(features=train_data_path, target='target', predefined_model='rf')
@@ -175,7 +175,7 @@ FEDOT provides methods for saving and loading pipelines in the ``Pipeline`` clas
             .. code-block:: python
 
                 problem = 'classification'
-                train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
+                train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
 
                 baseline_model = Fedot(problem=problem, timeout=1, seed=42)
                 baseline_model.fit(features=train_data_path, target='target', predefined_model='rf')
diff --git a/docs/source/basics/tabular_data.rst b/docs/source/basics/tabular_data.rst
index 7a4d792bde..6c324d5b84 100644
--- a/docs/source/basics/tabular_data.rst
+++ b/docs/source/basics/tabular_data.rst
@@ -113,4 +113,4 @@ More details you can find in the follow links:
 
 **Cases**
 
-* `Case: Credit scoring problem <https://github.com/aimclub/FEDOT/blob/master/cases/credit_scoring/credit_scoring_problem.py>`_
\ No newline at end of file
+* `Case: Credit scoring problem <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/credit_scoring/credit_scoring_problem.py>`_
\ No newline at end of file
diff --git a/docs/source/basics/ts_forecasting.rst b/docs/source/basics/ts_forecasting.rst
index 306aa35f66..db1c39cbf2 100644
--- a/docs/source/basics/ts_forecasting.rst
+++ b/docs/source/basics/ts_forecasting.rst
@@ -624,5 +624,5 @@ Examples
 
 **Cases**
 
-* `Case: river level forecasting with composer <https://github.com/aimclub/FEDOT/blob/master/cases/river_levels_prediction/river_level_case_composer.py>`_
-* `Case: river level forecasting (manual) <https://github.com/aimclub/FEDOT/blob/master/cases/river_levels_prediction/river_level_case_manual.py>`_
+* `Case: river level forecasting with composer <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/river_levels_prediction/river_level_case_composer.py>`_
+* `Case: river level forecasting (manual) <https://github.com/aimclub/FEDOT/blob/master/examples/real_cases/river_levels_prediction/river_level_case_manual.py>`_
diff --git a/docs/source/faq/abstract.rst b/docs/source/faq/abstract.rst
index 40b2f3e392..6ee8d1da81 100644
--- a/docs/source/faq/abstract.rst
+++ b/docs/source/faq/abstract.rst
@@ -1,13 +1,22 @@
 Abstract
 ========
 
-.. topic:: What is Fedot?
+.. topic:: What is FEDOT?
 
-    *Fedot is the AutoML-like framework for the automated generation of the
+    *FEDOT is the AutoML-like framework for the automated generation of the
     data-driven composite models. It can solve classification, regression,
     clustering, and forecasting problems.*
 
-.. topic:: Why should I use Fedot instead of existing state-of-the-art solutions (H2O/TPOT/etc)?
+.. topic:: What FEDOT is framework.
+
+    *While the exact difference between 'library' and 'framework' is a bit ambiguous and
+    context-dependent in many cases, we still consider FEDOT as a framework.*
+
+    *The reason is that is can be used not only to solve pre-defined AutoML task,
+    but also can be used to build new derivative solutions.
+    *As an examples:* `FEDOT.NAS`_, `FEDOT.Industrial`_.
+
+.. topic:: Why should I use FEDOT instead of existing state-of-the-art solutions (H2O/TPOT/etc)?
 
     *In practice, the existing AutoML solutions are really effective for the
     limited set of problems only. During the model learning, modern AutoML
@@ -21,16 +30,16 @@ Abstract
     efficient way. Also, we are aimed to outperform the existing solutions
     even for well-known benchmarks (e.g. PMLB datasets).*
 
-.. topic:: Can I install Fedot using pip/conda?
+.. topic:: Can I install FEDOT using pip/conda?
 
-    *Yes, follow the* `link`_
+    *Yes, follow the* `link`_.
 
-.. topic:: Can I use Fedot in my project/research/etc?
+.. topic:: Can I use FEDOT in my project/research/etc?
 
     *Yes, you can. The Fedot is published under the BSD-3 license. Also, we
     will be happy to help the users to adopt Fedot to their needs.*
 
-.. topic:: Why it is named Fedot?
+.. topic:: Why it is named FEDOT?
 
     *We decided to use this archaic Russian first name to add a bit of
     fantasy spirit into the development process.*
@@ -40,3 +49,9 @@ Abstract
 
 .. _link: https://pypi.org/project/fedot
 .. `link` replace:: *link*
+
+.. _FEDOT.NAS: https://github.com/ITMO-NSS-team/nas-fedot
+.. `FEDOT.NAS` replace:: *FEDOT.NAS*
+
+.. _FEDOT.Industrial: https://github.com/aimclub/Fedot.Industrial
+.. `FEDOT.Industrial` replace:: *FEDOT.Industrial*
diff --git a/examples/advanced/additional_learning.py b/examples/advanced/additional_learning.py
index 0d40a56a23..1239978f68 100644
--- a/examples/advanced/additional_learning.py
+++ b/examples/advanced/additional_learning.py
@@ -13,8 +13,8 @@
 
 
 def run_additional_learning_example():
-    train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
-    test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
+    train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
+    test_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_test.csv'
 
     train_data = pd.read_csv(train_data_path)
     test_data = pd.read_csv(test_data_path)
diff --git a/examples/advanced/automl/pipeline_from_automl.py b/examples/advanced/automl/pipeline_from_automl.py
index 15e7d6edd0..b0d10f4c09 100644
--- a/examples/advanced/automl/pipeline_from_automl.py
+++ b/examples/advanced/automl/pipeline_from_automl.py
@@ -1,12 +1,12 @@
 from datetime import timedelta
 
-from fedot.core.repository.operation_types_repository import OperationTypesRepository
 from sklearn.metrics import roc_auc_score as roc_auc
 
-from cases.data.data_utils import get_scoring_case_data_paths
+from examples.real_cases.data.data_utils import get_scoring_case_data_paths
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
+from fedot.core.repository.operation_types_repository import OperationTypesRepository
 
 
 # TODO not working now - add switch to other repository.json
diff --git a/examples/advanced/automl/tpot_vs_fedot.py b/examples/advanced/automl/tpot_vs_fedot.py
index 51491f6fd3..0e6842d55f 100644
--- a/examples/advanced/automl/tpot_vs_fedot.py
+++ b/examples/advanced/automl/tpot_vs_fedot.py
@@ -55,7 +55,7 @@ def run_tpot_vs_fedot_example(train_file_path: str, test_file_path: str):
 
 
 if __name__ == '__main__':
-    train_file_path = "../../../cases/data/scoring/scoring_train.csv"
-    test_file_path = "../../../cases/data/scoring/scoring_test.csv"
+    train_file_path = "../../real_examples/real_cases/data/scoring/scoring_train.csv"
+    test_file_path = "../../real_examples/real_cases/data/scoring/scoring_test.csv"
 
     run_tpot_vs_fedot_example(train_file_path, test_file_path)
diff --git a/examples/advanced/decompose/classification_refinement_example.py b/examples/advanced/decompose/classification_refinement_example.py
index 1806571eb3..5a4c714deb 100644
--- a/examples/advanced/decompose/classification_refinement_example.py
+++ b/examples/advanced/decompose/classification_refinement_example.py
@@ -1,6 +1,6 @@
 from golem.core.tuning.simultaneous import SimultaneousTuner
 
-from cases.credit_scoring.credit_scoring_problem import get_scoring_data, calculate_validation_metric
+from examples.real_cases.credit_scoring.credit_scoring_problem import get_scoring_data, calculate_validation_metric
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
diff --git a/examples/advanced/decompose/refinement_forecast_example.py b/examples/advanced/decompose/refinement_forecast_example.py
index 52ea9d361d..17502970d8 100644
--- a/examples/advanced/decompose/refinement_forecast_example.py
+++ b/examples/advanced/decompose/refinement_forecast_example.py
@@ -14,7 +14,6 @@
 from fedot.core.repository.tasks import TaskTypesEnum, Task, TsForecastingParams
 from fedot.core.utils import set_random_seed
 
-
 warnings.filterwarnings('ignore')
 
 
@@ -163,6 +162,6 @@ def run_refinement_forecast(path_to_file, len_forecast=100, lagged=150,
 if __name__ == '__main__':
     set_random_seed(2020)
 
-    path = '../../../cases/data/time_series/economic_data.csv'
+    path = '../../real_examples/real_cases/data/time_series/economic_data.csv'
     run_refinement_forecast(path, len_forecast=50, validation_blocks=5,
                             lagged=50, vis_with_decompose=True)
diff --git a/examples/advanced/decompose/regression_refinement_example.py b/examples/advanced/decompose/regression_refinement_example.py
index e371375937..509e0f7b9f 100644
--- a/examples/advanced/decompose/regression_refinement_example.py
+++ b/examples/advanced/decompose/regression_refinement_example.py
@@ -130,5 +130,5 @@ def run_river_experiment(file_path, with_tuning=False):
 
 
 if __name__ == '__main__':
-    run_river_experiment(file_path=f'{fedot_project_root()}/cases/data/river_levels/station_levels.csv',
+    run_river_experiment(file_path=f'{fedot_project_root()}/examples/real_cases/data/river_levels/station_levels.csv',
                          with_tuning=True)
diff --git a/cases/evo_operators_comparison/evo_operators_comparison.py b/examples/advanced/evo_operators_comparison.py
similarity index 97%
rename from cases/evo_operators_comparison/evo_operators_comparison.py
rename to examples/advanced/evo_operators_comparison.py
index 1a89fe81be..6f75500f0d 100644
--- a/cases/evo_operators_comparison/evo_operators_comparison.py
+++ b/examples/advanced/evo_operators_comparison.py
@@ -3,20 +3,19 @@
 from typing import Sequence, Optional
 
 import numpy as np
-from matplotlib import pyplot as plt
-from sklearn.metrics import roc_auc_score as roc_auc
-
-from fedot.core.composer.composer_builder import ComposerBuilder
-from fedot.core.data.data import InputData
 from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
 from golem.core.optimisers.genetic.operators.crossover import CrossoverTypesEnum
 from golem.core.optimisers.genetic.operators.inheritance import GeneticSchemeTypesEnum
 from golem.core.optimisers.genetic.operators.mutation import MutationTypesEnum
 from golem.core.optimisers.opt_history_objects.opt_history import OptHistory
+from matplotlib import pyplot as plt
+from sklearn.metrics import roc_auc_score as roc_auc
 
+from fedot.core.composer.composer_builder import ComposerBuilder
+from fedot.core.data.data import InputData
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
-from fedot.core.repository.operation_types_repository import get_operations_for_task
 from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.operation_types_repository import get_operations_for_task
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root
 
@@ -178,8 +177,8 @@ def run_experiment_with_saved_histories(save_dir):
 
 
 if __name__ == '__main__':
-    train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
-    test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
+    train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
+    test_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_test.csv'
 
     run_experiment(train_data_path,
                    test_data_path,
diff --git a/examples/advanced/fedot_based_solutions/external_optimizer.py b/examples/advanced/fedot_based_solutions/external_optimizer.py
index 498d461b09..206200fac1 100644
--- a/examples/advanced/fedot_based_solutions/external_optimizer.py
+++ b/examples/advanced/fedot_based_solutions/external_optimizer.py
@@ -7,8 +7,8 @@
 
 
 def run_with_random_search_composer():
-    train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
-    test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
+    train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
+    test_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_test.csv'
 
     composer_params = {'available_operations': ['class_decompose', 'rf', 'linear', 'xgboost', 'dt'],
                        'optimizer': RandomMutationSearchOptimizer}
diff --git a/examples/advanced/gpu_example.py b/examples/advanced/gpu_example.py
index abd78bd92a..2a05565f1f 100644
--- a/examples/advanced/gpu_example.py
+++ b/examples/advanced/gpu_example.py
@@ -69,8 +69,8 @@ def run_pipeline_with_specific_evaluation_mode(train_data: InputData, test_data:
 
 
 def get_scoring_data() -> Tuple[InputData, InputData]:
-    train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
-    test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
+    train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
+    test_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_test.csv'
 
     train_data = InputData.from_csv(train_data_path)
     test_data = InputData.from_csv(test_data_path)
diff --git a/examples/advanced/multi_modal_pipeline.py b/examples/advanced/multi_modal_pipeline.py
index 0c79ca9a12..427e2d316a 100644
--- a/examples/advanced/multi_modal_pipeline.py
+++ b/examples/advanced/multi_modal_pipeline.py
@@ -3,7 +3,7 @@
 
 from sklearn.metrics import f1_score as f1
 
-from cases.dataset_preparation import unpack_archived_data
+from examples.real_cases.dataset_preparation import unpack_archived_data
 from fedot import Fedot
 from fedot.core.data.data import InputData, OutputData
 from fedot.core.data.data_split import train_test_data_setup
diff --git a/examples/advanced/parallelization_comparison.py b/examples/advanced/parallelization_comparison.py
index 2776213ef3..d2e36e9b42 100644
--- a/examples/advanced/parallelization_comparison.py
+++ b/examples/advanced/parallelization_comparison.py
@@ -28,7 +28,7 @@ def run_experiments(timeout: float = None, partitions_n=10, n_jobs=-1):
     :param n_jobs: how many processors you want to use in a multiprocessing mode
 
     """
-    train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
+    train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
 
     problem = 'classification'
 
diff --git a/examples/advanced/profiler_example.py b/examples/advanced/profiler_example.py
index c26d23d2fa..a0871fa60a 100644
--- a/examples/advanced/profiler_example.py
+++ b/examples/advanced/profiler_example.py
@@ -3,10 +3,9 @@
 from golem.utilities.profiler.memory_profiler import MemoryProfiler
 from golem.utilities.profiler.time_profiler import TimeProfiler
 
-from cases.credit_scoring.credit_scoring_problem import run_credit_scoring_problem, get_scoring_data
+from examples.real_cases.credit_scoring.credit_scoring_problem import run_credit_scoring_problem, get_scoring_data
 from fedot.core.utils import set_random_seed
 
-
 if __name__ == '__main__':
     set_random_seed(1)
     # JUST UNCOMMENT WHAT TYPE OF PROFILER DO YOU NEED
diff --git a/examples/advanced/remote_execution/remote_fit_example.py b/examples/advanced/remote_execution/remote_fit_example.py
index e8a4ee42f4..bd1aabbc23 100644
--- a/examples/advanced/remote_execution/remote_fit_example.py
+++ b/examples/advanced/remote_execution/remote_fit_example.py
@@ -16,7 +16,7 @@ def run_experiment():
     # WARNING - THIS SCRIPT CAN BE EVALUATED ONLY WITH THE ACCESS TO DATAMALL SYSTEM
 
     # LOCAL RUN
-    folder = fedot_project_root().joinpath('cases', 'data', 'scoring')
+    folder = fedot_project_root().joinpath('examples', 'real_cases', 'data', 'scoring')
     path = folder.joinpath('scoring_train.csv')
 
     start = datetime.now()
diff --git a/examples/advanced/remote_execution/ts_composer_with_integration.py b/examples/advanced/remote_execution/ts_composer_with_integration.py
index c43f6b0fb8..c3c556cff3 100644
--- a/examples/advanced/remote_execution/ts_composer_with_integration.py
+++ b/examples/advanced/remote_execution/ts_composer_with_integration.py
@@ -20,7 +20,7 @@ def run_automl(data: MultiModalData, features_to_use,
                timeout: int = 1):
     """ Launch AutoML FEDOT algorithm for time series forecasting task """
 
-    metocean_folder = fedot_project_root().joinpath('cases', 'data', 'metocean')
+    metocean_folder = fedot_project_root().joinpath('examples', 'real_cases', 'data', 'metocean')
 
     connect_params = {}
     exec_params = {
@@ -73,7 +73,7 @@ def run_automl(data: MultiModalData, features_to_use,
     features_to_use = ['wind_speed', 'sea_height']
 
     data = MultiModalData.from_csv_time_series(
-        file_path=fedot_project_root().joinpath('cases/data/metocean/metocean_data_train.csv'),
+        file_path=fedot_project_root().joinpath('examples/real_cases/data/metocean/metocean_data_train.csv'),
         columns_to_use=features_to_use,
         target_column='sea_height',
         index_col='datetime')
diff --git a/examples/advanced/structural_analysis/dataset_access.py b/examples/advanced/structural_analysis/dataset_access.py
index 25411f24e1..c09dd071b3 100644
--- a/examples/advanced/structural_analysis/dataset_access.py
+++ b/examples/advanced/structural_analysis/dataset_access.py
@@ -7,11 +7,11 @@
 
 
 def get_scoring_data():
-    file_path_train = join('cases', 'data', 'scoring', 'scoring_train.csv')
+    file_path_train = join('examples', 'real_cases', 'data', 'scoring', 'scoring_train.csv')
     full_path_train = join(str(fedot_project_root()), file_path_train)
 
     # a dataset for a final validation of the composed model
-    file_path_test = join('cases', 'data', 'scoring', 'scoring_test.csv')
+    file_path_test = join('examples', 'real_cases', 'data', 'scoring', 'scoring_test.csv')
     full_path_test = join(str(fedot_project_root()), file_path_test)
     task = Task(TaskTypesEnum.classification)
     train = InputData.from_csv(full_path_train, task=task)
@@ -21,7 +21,7 @@ def get_scoring_data():
 
 
 def get_kc2_data():
-    file_path = join('cases', 'data', 'kc2', 'kc2.csv')
+    file_path = join('examples', 'real_cases', 'data', 'kc2', 'kc2.csv')
     full_path = join(str(fedot_project_root()), file_path)
     task = Task(TaskTypesEnum.classification)
     data = InputData.from_csv(full_path, task=task)
@@ -31,7 +31,7 @@ def get_kc2_data():
 
 
 def get_cholesterol_data():
-    file_path = join('cases', 'data', 'cholesterol', 'cholesterol.csv')
+    file_path = join('examples', 'real_cases', 'data', 'cholesterol', 'cholesterol.csv')
     full_path = join(str(fedot_project_root()), file_path)
     task = Task(TaskTypesEnum.regression)
     data = InputData.from_csv(full_path, task=task)
diff --git a/examples/advanced/time_series_forecasting/custom_model_tuning.py b/examples/advanced/time_series_forecasting/custom_model_tuning.py
index 32f0c5d078..e5f516e9b1 100644
--- a/examples/advanced/time_series_forecasting/custom_model_tuning.py
+++ b/examples/advanced/time_series_forecasting/custom_model_tuning.py
@@ -142,7 +142,7 @@ def run_pipeline_tuning(time_series, len_forecast, pipeline_type):
 
 
 if __name__ == '__main__':
-    df = pd.read_csv('../../../cases/data/time_series/metocean.csv')
+    df = pd.read_csv('../../real_examples/real_cases/data/time_series/metocean.csv')
     time_series = np.array(df['value'])
     run_pipeline_tuning(time_series=time_series,
                         len_forecast=50,
diff --git a/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py b/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py
index 9c4f309cab..4855679815 100644
--- a/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py
+++ b/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py
@@ -5,15 +5,14 @@
 from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
 from golem.core.optimisers.genetic.operators.mutation import MutationTypesEnum
 from golem.core.tuning.simultaneous import SimultaneousTuner
-
-from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from matplotlib import pyplot as plt
 from sklearn.metrics import mean_squared_error, mean_absolute_error
 
-from cases.multi_ts_level_forecasting import prepare_data
+from examples.real_cases.multi_ts_level_forecasting import prepare_data
 from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline
 from fedot.core.composer.composer_builder import ComposerBuilder
 from fedot.core.composer.gp_composer.specific_operators import parameter_change_mutation
+from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.metrics_repository import \
     RegressionMetricsEnum
diff --git a/examples/advanced/time_series_forecasting/nemo.py b/examples/advanced/time_series_forecasting/nemo.py
index daeb1913f9..6cba4e6f51 100644
--- a/examples/advanced/time_series_forecasting/nemo.py
+++ b/examples/advanced/time_series_forecasting/nemo.py
@@ -1,17 +1,17 @@
 import warnings
+from copy import deepcopy
 
 import numpy as np
 import pandas as pd
 from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
 
-from fedot.core.pipelines.pipeline import Pipeline
-from fedot.core.pipelines.node import PipelineNode
 from fedot.core.data.data import InputData
 from fedot.core.data.multi_modal import MultiModalData
-
+from fedot.core.pipelines.node import PipelineNode
+from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.repository.dataset_types import DataTypesEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
-from copy import deepcopy
+
 warnings.filterwarnings('ignore')
 
 
@@ -86,15 +86,14 @@ def return_working_pipeline():
 
 len_forecast = 40
 ts_name = 'sea_level'
-path_to_file = '../../cases/data/nemo/sea_surface_height.csv'
-path_to_exog_file = '../../cases/data/nemo/sea_surface_height_nemo.csv'
+path_to_file = '../../examples/real_cases/data/nemo/sea_surface_height.csv'
+path_to_exog_file = '../../examples/real_cases/data/nemo/sea_surface_height_nemo.csv'
 
 df = pd.read_csv(path_to_file)
 time_series = np.array(df[ts_name])
 df = pd.read_csv(path_to_exog_file)
 exog_variable = np.array(df[ts_name])
 
-
 # Let's divide our data on train and test samples
 train_data = time_series[:-len_forecast]
 test_data = time_series[-len_forecast:]
diff --git a/examples/advanced/time_series_forecasting/nemo_multiple.py b/examples/advanced/time_series_forecasting/nemo_multiple.py
index 8c72ad2ccf..383c7280d5 100644
--- a/examples/advanced/time_series_forecasting/nemo_multiple.py
+++ b/examples/advanced/time_series_forecasting/nemo_multiple.py
@@ -248,8 +248,8 @@ def boxplot_visualize(df, label):
 
 def run_single_example(len_forecast=40, visualization=False):
     ts_name = 'sea_level'
-    path_to_file = '../../cases/data/nemo/sea_surface_height.csv'
-    path_to_exog_file = '../../cases/data/nemo/sea_surface_height_nemo.csv'
+    path_to_file = '../../examples/real_cases/data/nemo/sea_surface_height.csv'
+    path_to_exog_file = '../../examples/real_cases/data/nemo/sea_surface_height_nemo.csv'
 
     df = pd.read_csv(path_to_file)
     time_series = df[ts_name]
@@ -323,8 +323,8 @@ def run_prediction_examples(mode='single', visualization=False):
     if mode == 'single':
         run_single_example(len_forecast=40, visualization=visualization)
     if mode == 'multiple':
-        run_multiple_example(path_to_file='../../cases/data/nemo/SSH_points_grid.csv',
-                             path_to_exog_file='../../cases/data/nemo/SSH_nemo_points_grid.csv',
+        run_multiple_example(path_to_file='../../examples/real_cases/data/nemo/SSH_points_grid.csv',
+                             path_to_exog_file='../../examples/real_cases/data/nemo/SSH_nemo_points_grid.csv',
                              out_path=None,
                              len_forecast=30,
                              visualization=visualization)
diff --git a/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py b/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py
index ddbf36e46e..9d37588fdb 100644
--- a/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py
+++ b/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py
@@ -129,7 +129,7 @@ def visualize(tuned, no_tuned, time, method_name):
 
 
 def run_tuning_comparison(n_repits=10, ts_size=1000, forecast_length=50, visualization=True):
-    file_path = os.path.join(str(fedot_project_root()), 'cases/data/time_series/temperature.csv')
+    file_path = os.path.join(str(fedot_project_root()), 'examples/real_cases/data/time_series/temperature.csv')
     df = pd.read_csv(file_path)
     time_series = np.array(df['value'])[:ts_size]
 
diff --git a/examples/project_import_export.py b/examples/project_import_export.py
index 92cb60dd63..03b27abd69 100644
--- a/examples/project_import_export.py
+++ b/examples/project_import_export.py
@@ -1,6 +1,6 @@
 from sklearn.metrics import roc_auc_score as roc_auc
 
-from cases.data.data_utils import get_scoring_case_data_paths
+from examples.real_cases.data.data_utils import get_scoring_case_data_paths
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
diff --git a/cases/__init__.py b/examples/real_cases/__init__.py
similarity index 100%
rename from cases/__init__.py
rename to examples/real_cases/__init__.py
diff --git a/examples/real_cases/credit_scoring/__init__.py b/examples/real_cases/credit_scoring/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/cases/credit_scoring/credit_scoring_problem.py b/examples/real_cases/credit_scoring/credit_scoring_problem.py
similarity index 93%
rename from cases/credit_scoring/credit_scoring_problem.py
rename to examples/real_cases/credit_scoring/credit_scoring_problem.py
index e6818f866a..a312bafd6d 100644
--- a/cases/credit_scoring/credit_scoring_problem.py
+++ b/examples/real_cases/credit_scoring/credit_scoring_problem.py
@@ -48,11 +48,11 @@ def get_scoring_data():
 
     # a dataset that will be used as a train and test set during composition
 
-    file_path_train = 'cases/data/scoring/scoring_train.csv'
+    file_path_train = 'examples/real_cases/data/scoring/scoring_train.csv'
     full_path_train = fedot_project_root().joinpath(file_path_train)
 
     # a dataset for a final validation of the composed model
-    file_path_test = 'cases/data/scoring/scoring_test.csv'
+    file_path_test = 'examples/real_cases/data/scoring/scoring_test.csv'
     full_path_test = fedot_project_root().joinpath(file_path_test)
 
     return full_path_train, full_path_test
diff --git a/cases/credit_scoring/credit_scoring_problem_multiobj.py b/examples/real_cases/credit_scoring/credit_scoring_problem_multiobj.py
similarity index 98%
rename from cases/credit_scoring/credit_scoring_problem_multiobj.py
rename to examples/real_cases/credit_scoring/credit_scoring_problem_multiobj.py
index 4a104531cb..919c2b60da 100644
--- a/cases/credit_scoring/credit_scoring_problem_multiobj.py
+++ b/examples/real_cases/credit_scoring/credit_scoring_problem_multiobj.py
@@ -7,15 +7,15 @@
 from golem.visualisation.opt_viz_extra import OptHistoryExtraVisualizer
 from sklearn.metrics import roc_auc_score as roc_auc
 
-from cases.credit_scoring.credit_scoring_problem import get_scoring_data
+from examples.real_cases.credit_scoring.credit_scoring_problem import get_scoring_data
 from fedot.core.composer.composer_builder import ComposerBuilder
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.operation_types_repository import get_operations_for_task
 from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, ComplexityMetricsEnum
+from fedot.core.repository.operation_types_repository import get_operations_for_task
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import set_random_seed
 
diff --git a/examples/real_cases/data/__init__.py b/examples/real_cases/data/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/cases/data/arctic/topaz_multi_ts.csv b/examples/real_cases/data/arctic/topaz_multi_ts.csv
similarity index 100%
rename from cases/data/arctic/topaz_multi_ts.csv
rename to examples/real_cases/data/arctic/topaz_multi_ts.csv
diff --git a/cases/data/cancer/cancer_test.csv b/examples/real_cases/data/cancer/cancer_test.csv
similarity index 100%
rename from cases/data/cancer/cancer_test.csv
rename to examples/real_cases/data/cancer/cancer_test.csv
diff --git a/cases/data/cancer/cancer_train.csv b/examples/real_cases/data/cancer/cancer_train.csv
similarity index 100%
rename from cases/data/cancer/cancer_train.csv
rename to examples/real_cases/data/cancer/cancer_train.csv
diff --git a/cases/data/cholesterol/cholesterol.csv b/examples/real_cases/data/cholesterol/cholesterol.csv
similarity index 100%
rename from cases/data/cholesterol/cholesterol.csv
rename to examples/real_cases/data/cholesterol/cholesterol.csv
diff --git a/cases/data/data_utils.py b/examples/real_cases/data/data_utils.py
similarity index 64%
rename from cases/data/data_utils.py
rename to examples/real_cases/data/data_utils.py
index e405b34118..b489fad7e7 100644
--- a/cases/data/data_utils.py
+++ b/examples/real_cases/data/data_utils.py
@@ -5,8 +5,8 @@
 
 
 def get_scoring_case_data_paths() -> Tuple[str, str]:
-    train_file_path = os.path.join('cases', 'data', 'scoring', 'scoring_train.csv')
-    test_file_path = os.path.join('cases', 'data', 'scoring', 'scoring_test.csv')
+    train_file_path = os.path.join('examples', 'real_cases', 'data', 'scoring', 'scoring_train.csv')
+    test_file_path = os.path.join('examples', 'real_cases', 'data', 'scoring', 'scoring_test.csv')
     full_train_file_path = os.path.join(str(fedot_project_root()), train_file_path)
     full_test_file_path = os.path.join(str(fedot_project_root()), test_file_path)
 
diff --git a/cases/data/gapfilling/ts_temperature_gapfilling.csv b/examples/real_cases/data/gapfilling/ts_temperature_gapfilling.csv
similarity index 100%
rename from cases/data/gapfilling/ts_temperature_gapfilling.csv
rename to examples/real_cases/data/gapfilling/ts_temperature_gapfilling.csv
diff --git a/cases/data/kc2/kc2.csv b/examples/real_cases/data/kc2/kc2.csv
similarity index 100%
rename from cases/data/kc2/kc2.csv
rename to examples/real_cases/data/kc2/kc2.csv
diff --git a/cases/data/lena_levels/multi_sample.csv b/examples/real_cases/data/lena_levels/multi_sample.csv
similarity index 100%
rename from cases/data/lena_levels/multi_sample.csv
rename to examples/real_cases/data/lena_levels/multi_sample.csv
diff --git a/cases/data/metocean/metocean_data_test.csv b/examples/real_cases/data/metocean/metocean_data_test.csv
similarity index 100%
rename from cases/data/metocean/metocean_data_test.csv
rename to examples/real_cases/data/metocean/metocean_data_test.csv
diff --git a/cases/data/metocean/metocean_data_train.csv b/examples/real_cases/data/metocean/metocean_data_train.csv
similarity index 100%
rename from cases/data/metocean/metocean_data_train.csv
rename to examples/real_cases/data/metocean/metocean_data_train.csv
diff --git a/cases/data/mm_imdb.tar.gz b/examples/real_cases/data/mm_imdb.tar.gz
similarity index 100%
rename from cases/data/mm_imdb.tar.gz
rename to examples/real_cases/data/mm_imdb.tar.gz
diff --git a/cases/data/multivariate_ssh.csv b/examples/real_cases/data/multivariate_ssh.csv
similarity index 100%
rename from cases/data/multivariate_ssh.csv
rename to examples/real_cases/data/multivariate_ssh.csv
diff --git a/cases/data/nemo/SSH_nemo_points_grid.csv b/examples/real_cases/data/nemo/SSH_nemo_points_grid.csv
similarity index 100%
rename from cases/data/nemo/SSH_nemo_points_grid.csv
rename to examples/real_cases/data/nemo/SSH_nemo_points_grid.csv
diff --git a/cases/data/nemo/SSH_points_grid.csv b/examples/real_cases/data/nemo/SSH_points_grid.csv
similarity index 100%
rename from cases/data/nemo/SSH_points_grid.csv
rename to examples/real_cases/data/nemo/SSH_points_grid.csv
diff --git a/cases/data/nemo/sea_surface_height.csv b/examples/real_cases/data/nemo/sea_surface_height.csv
similarity index 100%
rename from cases/data/nemo/sea_surface_height.csv
rename to examples/real_cases/data/nemo/sea_surface_height.csv
diff --git a/cases/data/nemo/sea_surface_height_nemo.csv b/examples/real_cases/data/nemo/sea_surface_height_nemo.csv
similarity index 100%
rename from cases/data/nemo/sea_surface_height_nemo.csv
rename to examples/real_cases/data/nemo/sea_surface_height_nemo.csv
diff --git a/cases/data/nemo/test_nemo.csv b/examples/real_cases/data/nemo/test_nemo.csv
similarity index 100%
rename from cases/data/nemo/test_nemo.csv
rename to examples/real_cases/data/nemo/test_nemo.csv
diff --git a/cases/data/river_levels/station_levels.csv b/examples/real_cases/data/river_levels/station_levels.csv
similarity index 100%
rename from cases/data/river_levels/station_levels.csv
rename to examples/real_cases/data/river_levels/station_levels.csv
diff --git a/cases/data/scoring/scoring_test.csv b/examples/real_cases/data/scoring/scoring_test.csv
similarity index 100%
rename from cases/data/scoring/scoring_test.csv
rename to examples/real_cases/data/scoring/scoring_test.csv
diff --git a/cases/data/scoring/scoring_train.csv b/examples/real_cases/data/scoring/scoring_train.csv
similarity index 100%
rename from cases/data/scoring/scoring_train.csv
rename to examples/real_cases/data/scoring/scoring_train.csv
diff --git a/cases/data/spam/spamham.csv b/examples/real_cases/data/spam/spamham.csv
similarity index 100%
rename from cases/data/spam/spamham.csv
rename to examples/real_cases/data/spam/spamham.csv
diff --git a/cases/data/spamham.tar.gz b/examples/real_cases/data/spamham.tar.gz
similarity index 100%
rename from cases/data/spamham.tar.gz
rename to examples/real_cases/data/spamham.tar.gz
diff --git a/cases/data/time_series/economic_data.csv b/examples/real_cases/data/time_series/economic_data.csv
similarity index 100%
rename from cases/data/time_series/economic_data.csv
rename to examples/real_cases/data/time_series/economic_data.csv
diff --git a/cases/data/time_series/metocean.csv b/examples/real_cases/data/time_series/metocean.csv
similarity index 100%
rename from cases/data/time_series/metocean.csv
rename to examples/real_cases/data/time_series/metocean.csv
diff --git a/cases/data/time_series/temperature.csv b/examples/real_cases/data/time_series/temperature.csv
similarity index 100%
rename from cases/data/time_series/temperature.csv
rename to examples/real_cases/data/time_series/temperature.csv
diff --git a/cases/data/time_series/traffic.csv b/examples/real_cases/data/time_series/traffic.csv
similarity index 100%
rename from cases/data/time_series/traffic.csv
rename to examples/real_cases/data/time_series/traffic.csv
diff --git a/cases/dataset_preparation.py b/examples/real_cases/dataset_preparation.py
similarity index 100%
rename from cases/dataset_preparation.py
rename to examples/real_cases/dataset_preparation.py
diff --git a/cases/kc2_sourcecode_defects_classification.py b/examples/real_cases/kc2_sourcecode_defects_classification.py
similarity index 97%
rename from cases/kc2_sourcecode_defects_classification.py
rename to examples/real_cases/kc2_sourcecode_defects_classification.py
index 5ec2f20df7..d2de03e884 100644
--- a/cases/kc2_sourcecode_defects_classification.py
+++ b/examples/real_cases/kc2_sourcecode_defects_classification.py
@@ -8,7 +8,7 @@
 
 
 def get_kc2_data():
-    file_path = 'cases/data/kc2/kc2.csv'
+    file_path = 'examples/real_cases/data/kc2/kc2.csv'
     full_path = join(str(fedot_project_root()), file_path)
     task = Task(TaskTypesEnum.classification)
     data = InputData.from_csv(full_path, task=task, target_columns='problems')
diff --git a/cases/metocean_forecasting_problem.py b/examples/real_cases/metocean_forecasting_problem.py
similarity index 94%
rename from cases/metocean_forecasting_problem.py
rename to examples/real_cases/metocean_forecasting_problem.py
index 87bb65c5bc..e4f9d1d0e9 100644
--- a/cases/metocean_forecasting_problem.py
+++ b/examples/real_cases/metocean_forecasting_problem.py
@@ -63,10 +63,10 @@ def run_metocean_forecasting_problem(train_file_path, test_file_path,
     # the dataset was obtained from NEMO model simulation for sea surface height
 
     # a dataset that will be used as a train and test set during composition
-    file_path_train = 'cases/data/metocean/metocean_data_train.csv'
+    file_path_train = 'examples/real_cases/data/metocean/metocean_data_train.csv'
 
     # a dataset for a final validation of the composed model
-    file_path_test = 'cases/data/metocean/metocean_data_test.csv'
+    file_path_test = 'examples/real_cases/data/metocean/metocean_data_test.csv'
 
     run_metocean_forecasting_problem(file_path_train, file_path_test,
                                      forecast_length=6, timeout=5,
diff --git a/cases/multi_target_levels_forecasting.py b/examples/real_cases/multi_target_levels_forecasting.py
similarity index 97%
rename from cases/multi_target_levels_forecasting.py
rename to examples/real_cases/multi_target_levels_forecasting.py
index 24aa988c4f..3e5b775e4c 100644
--- a/cases/multi_target_levels_forecasting.py
+++ b/examples/real_cases/multi_target_levels_forecasting.py
@@ -100,5 +100,5 @@ def run_multi_output_case(path, vis=False):
 
 
 if __name__ == '__main__':
-    path_file = fedot_project_root() / 'cases/data/lena_levels/multi_sample.csv'
+    path_file = fedot_project_root() / 'examples/real_cases/data/lena_levels/multi_sample.csv'
     run_multi_output_case(path_file, vis=True)
diff --git a/cases/multi_ts_level_forecasting.py b/examples/real_cases/multi_ts_level_forecasting.py
similarity index 96%
rename from cases/multi_ts_level_forecasting.py
rename to examples/real_cases/multi_ts_level_forecasting.py
index 2d7ddb46c7..a20251a1f4 100644
--- a/cases/multi_ts_level_forecasting.py
+++ b/examples/real_cases/multi_ts_level_forecasting.py
@@ -19,7 +19,7 @@ def prepare_data(forecast_length, is_multi_ts):
     target_column = '61_91'
     task = Task(TaskTypesEnum.ts_forecasting,
                 TsForecastingParams(forecast_length=forecast_length))
-    file_path = os.path.join(str(fedot_project_root()), 'cases/data/arctic/topaz_multi_ts.csv')
+    file_path = os.path.join(str(fedot_project_root()), 'examples/real_cases/data/arctic/topaz_multi_ts.csv')
     if is_multi_ts:
         data = InputData.from_csv_multi_time_series(
             file_path=file_path,
diff --git a/cases/multivariate_ts_forecasting.py b/examples/real_cases/multivariate_ts_forecasting.py
similarity index 96%
rename from cases/multivariate_ts_forecasting.py
rename to examples/real_cases/multivariate_ts_forecasting.py
index 7893a4b705..2f6bbeafd8 100644
--- a/cases/multivariate_ts_forecasting.py
+++ b/examples/real_cases/multivariate_ts_forecasting.py
@@ -42,7 +42,7 @@ def plot_results(full_df: pd.DataFrame, target_column: int, forecast: np.array,
 def launch_fedot_forecasting(target_column: int = 1, forecast_horizon: int = 50,
                              number_of_series_to_use: int = 25):
     """ Example how to launch FEDOT AutmoML for multivariate forecasting """
-    path_to_file = fedot_project_root() / 'cases' / 'data' / 'multivariate_ssh.csv'
+    path_to_file = fedot_project_root() / 'examples' / 'real_cases' / 'data' / 'multivariate_ssh.csv'
     df = pd.read_csv(path_to_file, parse_dates=['datetime'])
     train_df, test_df = train_test_split(df, forecast_horizon)
 
diff --git a/examples/real_cases/river_levels_prediction/__init__.py b/examples/real_cases/river_levels_prediction/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/cases/river_levels_prediction/river_level_case_composer.py b/examples/real_cases/river_levels_prediction/river_level_case_composer.py
similarity index 99%
rename from cases/river_levels_prediction/river_level_case_composer.py
rename to examples/real_cases/river_levels_prediction/river_level_case_composer.py
index b60f51e3cc..457a92301f 100644
--- a/cases/river_levels_prediction/river_level_case_composer.py
+++ b/examples/real_cases/river_levels_prediction/river_level_case_composer.py
@@ -3,12 +3,10 @@
 
 import numpy as np
 import pandas as pd
+from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
 from golem.core.tuning.simultaneous import SimultaneousTuner
-
 from sklearn.metrics import mean_absolute_error, mean_squared_error
 
-from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
-
 from fedot.core.composer.composer_builder import ComposerBuilder
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
diff --git a/cases/river_levels_prediction/river_level_case_manual.py b/examples/real_cases/river_levels_prediction/river_level_case_manual.py
similarity index 100%
rename from cases/river_levels_prediction/river_level_case_manual.py
rename to examples/real_cases/river_levels_prediction/river_level_case_manual.py
diff --git a/cases/spam_detection.py b/examples/real_cases/spam_detection.py
similarity index 87%
rename from cases/spam_detection.py
rename to examples/real_cases/spam_detection.py
index 5ec26a2ee2..e4e58ec523 100644
--- a/cases/spam_detection.py
+++ b/examples/real_cases/spam_detection.py
@@ -2,7 +2,7 @@
 
 from sklearn.metrics import roc_auc_score as roc_auc
 
-from cases.dataset_preparation import unpack_archived_data
+from examples.real_cases.dataset_preparation import unpack_archived_data
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.pipelines.node import PipelineNode
@@ -24,7 +24,7 @@ def execute_pipeline_for_text_problem(train_data, test_data):
 
 
 def run_text_problem_from_meta_file():
-    data_file_abspath = os.path.abspath(os.path.join('data', 'spam', 'spamham.csv'))
+    data_file_abspath = os.path.abspath(os.path.join('../../examples/real_cases/data', 'spam', 'spamham.csv'))
 
     data = InputData.from_text_meta_file(meta_file_path=data_file_abspath)
 
@@ -36,7 +36,7 @@ def run_text_problem_from_meta_file():
 
 
 def run_text_problem_from_files():
-    data_abspath = os.path.abspath(os.path.join('data', 'spamham'))
+    data_abspath = os.path.abspath(os.path.join('../../examples/real_cases/data', 'spamham'))
 
     unpack_archived_data(data_abspath)
 
diff --git a/cases/time_series_gapfilling_case.py b/examples/real_cases/time_series_gapfilling_case.py
similarity index 97%
rename from cases/time_series_gapfilling_case.py
rename to examples/real_cases/time_series_gapfilling_case.py
index 67c48cea36..c08811341a 100644
--- a/cases/time_series_gapfilling_case.py
+++ b/examples/real_cases/time_series_gapfilling_case.py
@@ -129,7 +129,7 @@ def run_gapfilling_case(file_path):
 # Example of using the algorithm to fill in gaps in a time series
 # The data is daily air temperature values from the weather station
 if __name__ == '__main__':
-    dataframe = run_gapfilling_case('cases/data/gapfilling/ts_temperature_gapfilling.csv')
+    dataframe = run_gapfilling_case('examples/real_cases/data/gapfilling/ts_temperature_gapfilling.csv')
 
     # Display metrics
     print_metrics(dataframe)
diff --git a/examples/simple/api_builder/classification_with_api_builder.py b/examples/simple/api_builder/classification_with_api_builder.py
index f3af6f09c9..9c90702135 100644
--- a/examples/simple/api_builder/classification_with_api_builder.py
+++ b/examples/simple/api_builder/classification_with_api_builder.py
@@ -3,8 +3,8 @@
 
 
 if __name__ == '__main__':
-    train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
-    test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
+    train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
+    test_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_test.csv'
 
     fedot = (FedotBuilder(problem='classification')
              .setup_composition(timeout=10, with_tuning=True, preset='best_quality')
diff --git a/examples/simple/classification/api_classification.py b/examples/simple/classification/api_classification.py
index a75dafbb18..3c60824f3c 100644
--- a/examples/simple/classification/api_classification.py
+++ b/examples/simple/classification/api_classification.py
@@ -4,8 +4,8 @@
 
 def run_classification_example(timeout: float = None, visualization=False, with_tuning=True):
     problem = 'classification'
-    train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
-    test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
+    train_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_train.csv'
+    test_data_path = f'{fedot_project_root()}/examples/real_cases/data/scoring/scoring_test.csv'
 
     baseline_model = Fedot(problem=problem, timeout=timeout)
     baseline_model.fit(features=train_data_path, target='target', predefined_model='rf')
diff --git a/examples/simple/interpretable/api_explain.py b/examples/simple/interpretable/api_explain.py
index ee3b97acb9..1e5b61271e 100644
--- a/examples/simple/interpretable/api_explain.py
+++ b/examples/simple/interpretable/api_explain.py
@@ -5,7 +5,7 @@
 
 
 def run_api_explain_example(visualization=False, timeout=None, with_tuning=True):
-    train_data = pd.read_csv(f'{fedot_project_root()}/cases/data/cancer/cancer_train.csv', index_col=0)
+    train_data = pd.read_csv(f'{fedot_project_root()}/examples/real_cases/data/cancer/cancer_train.csv', index_col=0)
     figure_path = 'api_explain_example.png'
 
     # Feature and class names for visualization
diff --git a/examples/simple/interpretable/pipeline_explain.py b/examples/simple/interpretable/pipeline_explain.py
index 1467cb0acb..024125867b 100644
--- a/examples/simple/interpretable/pipeline_explain.py
+++ b/examples/simple/interpretable/pipeline_explain.py
@@ -10,7 +10,7 @@
 
 def run_pipeline_explain():
     # Specifying paths
-    train_data_path = os.path.join(fedot_project_root(), 'cases', 'data', 'cancer', 'cancer_train.csv')
+    train_data_path = os.path.join(fedot_project_root(), 'examples', 'real_cases', 'data', 'cancer', 'cancer_train.csv')
     figure_path = 'pipeline_explain_example.png'
 
     # Feature and class names for visualization
diff --git a/examples/simple/pipeline_tune.py b/examples/simple/pipeline_tune.py
index 66ce2608d8..9c98e5bab4 100644
--- a/examples/simple/pipeline_tune.py
+++ b/examples/simple/pipeline_tune.py
@@ -1,10 +1,8 @@
-from copy import deepcopy
-
 import numpy as np
 from golem.core.tuning.simultaneous import SimultaneousTuner
 from sklearn.metrics import roc_auc_score as roc_auc
 
-from cases.data.data_utils import get_scoring_case_data_paths
+from examples.real_cases.data.data_utils import get_scoring_case_data_paths
 from examples.simple.classification.classification_pipelines import classification_complex_pipeline
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.pipeline import Pipeline
diff --git a/examples/simple/pipeline_tuning_with_iopt.py b/examples/simple/pipeline_tuning_with_iopt.py
index dee9153183..553060d1f0 100644
--- a/examples/simple/pipeline_tuning_with_iopt.py
+++ b/examples/simple/pipeline_tuning_with_iopt.py
@@ -54,7 +54,7 @@ def tune_pipeline(pipeline: Pipeline,
                 .add_node('knnreg', 1)
                 .join_branches('rfr')
                 .build())
-    data_path = f'{fedot_project_root()}/cases/data/cholesterol/cholesterol.csv'
+    data_path = f'{fedot_project_root()}/examples/real_cases/data/cholesterol/cholesterol.csv'
 
     data = InputData.from_csv(data_path,
                               task=Task(TaskTypesEnum.regression))
diff --git a/examples/simple/regression/api_regression.py b/examples/simple/regression/api_regression.py
index b1a905982a..44127bd3c7 100644
--- a/examples/simple/regression/api_regression.py
+++ b/examples/simple/regression/api_regression.py
@@ -9,7 +9,7 @@
 
 def run_regression_example(visualise: bool = False, with_tuning: bool = True,
                            timeout: float = 2., preset: str = 'auto'):
-    data_path = f'{fedot_project_root()}/cases/data/cholesterol/cholesterol.csv'
+    data_path = f'{fedot_project_root()}/examples/real_cases/data/cholesterol/cholesterol.csv'
 
     data = InputData.from_csv(data_path,
                               task=Task(TaskTypesEnum.regression))
diff --git a/examples/simple/time_series_forecasting/fitted_values.py b/examples/simple/time_series_forecasting/fitted_values.py
index 7eb4a94119..8afe680051 100644
--- a/examples/simple/time_series_forecasting/fitted_values.py
+++ b/examples/simple/time_series_forecasting/fitted_values.py
@@ -18,7 +18,7 @@ def show_fitted_time_series(len_forecast=24):
     task = Task(TaskTypesEnum.ts_forecasting,
                 TsForecastingParams(forecast_length=len_forecast))
 
-    ts_input = InputData.from_csv_time_series(file_path='../../../cases/data/time_series/metocean.csv',
+    ts_input = InputData.from_csv_time_series(file_path='../../real_examples/real_cases/data/time_series/metocean.csv',
                                               task=task, target_column='value')
 
     pipeline = get_simple_short_lagged_pipeline()
diff --git a/test/data/datasets.py b/test/data/datasets.py
index 122b8d0f92..be1cdf4e74 100644
--- a/test/data/datasets.py
+++ b/test/data/datasets.py
@@ -5,7 +5,7 @@
 import pandas as pd
 from sklearn.model_selection import train_test_split
 
-from cases.metocean_forecasting_problem import prepare_input_data
+from examples.real_cases.metocean_forecasting_problem import prepare_input_data
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.data.supplementary_data import SupplementaryData
@@ -37,7 +37,7 @@ def get_split_data():
 
 
 def get_cholesterol_dataset():
-    data_path = f'{fedot_project_root()}/cases/data/cholesterol/cholesterol.csv'
+    data_path = f'{fedot_project_root()}/examples/real_cases/data/cholesterol/cholesterol.csv'
     data = InputData.from_csv(data_path, task=Task(TaskTypesEnum.regression))
     train, test = train_test_data_setup(data)
     return train, test
@@ -69,11 +69,11 @@ def get_dataset(task_type: str, validation_blocks: Optional[int] = None, n_sampl
 
 
 def get_multimodal_ts_data(size=500):
-    file_path_train = 'cases/data/metocean/metocean_data_train.csv'
+    file_path_train = 'examples/real_cases/data/metocean/metocean_data_train.csv'
     full_path_train = os.path.join(str(fedot_project_root()), file_path_train)
 
     # a dataset for a final validation of the composed model
-    file_path_test = 'cases/data/metocean/metocean_data_test.csv'
+    file_path_test = 'examples/real_cases/data/metocean/metocean_data_test.csv'
     full_path_test = os.path.join(str(fedot_project_root()), file_path_test)
 
     target_history, add_history, _ = prepare_input_data(full_path_train, full_path_test,
diff --git a/test/integration/quality/test_quality_improvement.py b/test/integration/quality/test_quality_improvement.py
index 58c14d63cb..0d25480868 100644
--- a/test/integration/quality/test_quality_improvement.py
+++ b/test/integration/quality/test_quality_improvement.py
@@ -12,8 +12,8 @@
 
 def test_classification_quality_improvement():
     # input data initialization
-    train_data_path = fedot_project_root().joinpath('cases/data/scoring/scoring_train.csv')
-    test_data_path = fedot_project_root().joinpath('cases/data/scoring/scoring_test.csv')
+    train_data_path = fedot_project_root().joinpath('examples/real_cases/data/scoring/scoring_train.csv')
+    test_data_path = fedot_project_root().joinpath('examples/real_cases/data/scoring/scoring_test.csv')
 
     seed = 50
     problem = 'classification'
@@ -45,8 +45,8 @@ def test_classification_quality_improvement():
 
 def test_multiobjective_improvement():
     # input data initialization
-    train_data_path = fedot_project_root().joinpath('cases/data/scoring/scoring_train.csv')
-    test_data_path = fedot_project_root().joinpath('cases/data/scoring/scoring_test.csv')
+    train_data_path = fedot_project_root().joinpath('examples/real_cases/data/scoring/scoring_train.csv')
+    test_data_path = fedot_project_root().joinpath('examples/real_cases/data/scoring/scoring_test.csv')
     problem = 'classification'
     seed = 50
 
diff --git a/test/integration/real_applications/test_real_cases.py b/test/integration/real_applications/test_real_cases.py
index 57e7e3f343..3816023e89 100644
--- a/test/integration/real_applications/test_real_cases.py
+++ b/test/integration/real_applications/test_real_cases.py
@@ -2,11 +2,11 @@
 from golem.core.tuning.simultaneous import SimultaneousTuner
 from sklearn.metrics import mean_squared_error
 
-from cases.credit_scoring.credit_scoring_problem import run_credit_scoring_problem
-from cases.metocean_forecasting_problem import run_metocean_forecasting_problem
-from cases.river_levels_prediction.river_level_case_manual import run_river_experiment
-from cases.spam_detection import run_text_problem_from_saved_meta_file
-from cases.time_series_gapfilling_case import run_gapfilling_case
+from examples.real_cases.credit_scoring.credit_scoring_problem import run_credit_scoring_problem
+from examples.real_cases.metocean_forecasting_problem import run_metocean_forecasting_problem
+from examples.real_cases.river_levels_prediction.river_level_case_manual import run_river_experiment
+from examples.real_cases.spam_detection import run_text_problem_from_saved_meta_file
+from examples.real_cases.time_series_gapfilling_case import run_gapfilling_case
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.utils import fedot_project_root
diff --git a/test/integration/test_profiler.py b/test/integration/test_profiler.py
index 5cb55fe344..6a01cdb910 100644
--- a/test/integration/test_profiler.py
+++ b/test/integration/test_profiler.py
@@ -5,7 +5,7 @@
 from golem.utilities.profiler.memory_profiler import MemoryProfiler
 from golem.utilities.profiler.time_profiler import TimeProfiler
 
-from cases.credit_scoring.credit_scoring_problem import get_scoring_data, run_credit_scoring_problem
+from examples.real_cases.credit_scoring.credit_scoring_problem import get_scoring_data, run_credit_scoring_problem
 
 
 @pytest.fixture(scope='session', autouse=True)
diff --git a/test/sensitivity/test_sensitivity.py b/test/sensitivity/test_sensitivity.py
index 8688c3896b..db27a35e6f 100644
--- a/test/sensitivity/test_sensitivity.py
+++ b/test/sensitivity/test_sensitivity.py
@@ -1,7 +1,7 @@
 import os
 from unittest.mock import patch
 
-from cases.data.data_utils import get_scoring_case_data_paths
+from examples.real_cases.data.data_utils import get_scoring_case_data_paths
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
diff --git a/test/unit/data/test_data_merge_text.py b/test/unit/data/test_data_merge_text.py
index 6080e7f0d8..3743ed2607 100644
--- a/test/unit/data/test_data_merge_text.py
+++ b/test/unit/data/test_data_merge_text.py
@@ -10,7 +10,7 @@
 
 
 def load_sample_text(file_path=None, label_col='label'):
-    file_path = file_path or fedot_project_root().joinpath('cases', 'data', 'spam', 'spamham.csv')
+    file_path = file_path or fedot_project_root().joinpath('examples', 'real_cases', 'data', 'spam', 'spamham.csv')
     df_text = pd.read_csv(file_path)
     df_text = df_text.sample(frac=1).reset_index(drop=True)
 
diff --git a/test/unit/data_operations/test_data_operation_params.py b/test/unit/data_operations/test_data_operation_params.py
index 5d9d69397c..96c6ce4def 100644
--- a/test/unit/data_operations/test_data_operation_params.py
+++ b/test/unit/data_operations/test_data_operation_params.py
@@ -69,7 +69,7 @@ def test_ransac_with_invalid_params_fit_correctly():
     than the number of objects
     """
 
-    data_path = f'{fedot_project_root()}/cases/data/cholesterol/cholesterol.csv'
+    data_path = f'{fedot_project_root()}/examples/real_cases/data/cholesterol/cholesterol.csv'
 
     data = InputData.from_csv(data_path)
     train, test = train_test_data_setup(data)