Small edits (#476)

docs/clearml_data/clearml_data.md

@@ -41,7 +41,7 @@ guide for more info!
 
 ## Using ClearML Data
 
-ClearML Data offers two interfaces:
+ClearML Data supports two interfaces:
 - `clearml-data` - A CLI utility for creating, uploading, and managing datasets. See [CLI](clearml_data_cli.md) for a reference of `clearml-data` commands.
 - `clearml.Dataset` - A python interface for creating, retrieving, managing, and using datasets. See [SDK](clearml_data_sdk.md) for an overview of the basic methods of the `Dataset` module.
 

docs/clearml_data/data_management_examples/data_man_cifar_classification.md

@@ -2,13 +2,13 @@
 title: Dataset Management with CLI and SDK
 ---
 
-In this tutorial, we are going to manage the CIFAR dataset with `clearml-data` CLI, and then use ClearML's [`Dataset`](../../references/sdk/dataset.md) 
+In this tutorial, you are going to manage the CIFAR dataset with `clearml-data` CLI, and then use ClearML's [`Dataset`](../../references/sdk/dataset.md) 
 class to ingest the data.
 
 ## Creating the Dataset
 
 ### Downloading the Data
-Before we can register the CIFAR dataset with `clearml-data`, we need to obtain a local copy of it.
+Before registering the CIFAR dataset with `clearml-data`, you need to obtain a local copy of it.
 
 Execute this python script to download the data
 ```python
@@ -43,7 +43,7 @@ New dataset created id=ee1c35f60f384e65bc800f42f0aca5ec
 Where `ee1c35f60f384e65bc800f42f0aca5ec` is the dataset ID.
 
 ## Adding Files
-Add the files we just downloaded to the dataset: 
+Add the files that were just downloaded to the dataset: 
 
 ```
 clearml-data add --files <dataset_path>
@@ -72,7 +72,7 @@ In the panel's **CONTENT** tab, you can see a table summarizing version contents
 
 ## Using the Dataset
 
-Now that we have a new dataset registered, we can consume it.
+Now that you have a new dataset registered, you can consume it.
 
 The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) example 
 script demonstrates using the dataset within Python code.
@@ -102,7 +102,7 @@ hyperparameters. Passing `alias=<dataset_alias_string>` stores the dataset’s I
 `dataset_alias_string` parameter in the experiment's **CONFIGURATION > HYPERPARAMETERS > Datasets** section. This way 
 you can easily track which dataset the task is using. 
 
-The Dataset's [`get_local_copy`](../../references/sdk/dataset.md#get_local_copy) method will return a path to the cached, 
-downloaded dataset. Then we provide the path to PyTorch's dataset object.
+The Dataset's [`get_local_copy`](../../references/sdk/dataset.md#get_local_copy) method returns a path to the cached, 
+downloaded dataset. Then the dataset path is input to PyTorch's `datasets` object.
 
 The script then trains a neural network to classify images using the dataset created above.

docs/clearml_data/data_management_examples/data_man_folder_sync.md

@@ -78,4 +78,4 @@ Upload completed (742 bytes)
 Dataset closed and finalized
 ```
 
-We can see that 2 files were added or modified, just as we expected!
+See that 2 files were added or modified, just as expected!

docs/clearml_data/data_management_examples/data_man_python.md

@@ -18,7 +18,7 @@ demonstrates how to do the following:
 
 ### Downloading the Data
 
-We first need to obtain a local copy of the CIFAR dataset.
+You first need to obtain a local copy of the CIFAR dataset.
 
  ```python
  from clearml import StorageManager
@@ -79,7 +79,7 @@ In the panel's **CONTENT** tab, you can see a table summarizing version contents
 
 ## Data Ingestion
 
-Now that we have a new dataset registered, we can consume it!
+Now that a new dataset is registered, you can consume it!
 
 The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) script 
 demonstrates data ingestion using the dataset created in the first script.

docs/clearml_data/data_management_examples/data_man_simple.md

@@ -48,7 +48,7 @@ to captures all files and sub-folders:
    
    
 :::note
-After creating a dataset, we don't have to specify its ID when running commands, such as *add*, *remove* or *list*
+After creating a dataset, its ID doesn't need to be specified when running commands, such as `add`, `remove`, or `list`
 :::
 
 3. Close the dataset - this command uploads the files. By default, the files are uploaded to the file server, but  
@@ -151,7 +151,7 @@ You'll need to input the Dataset ID you received when created the dataset above
    clearml-data list --id 8b68686a4af040d081027ba3cf6bbca6
    ```
 
-   And we see that our changes have been made! `new_data.txt` has been added, and `dancing.jpg` has been removed. 
+   And see that the changes have been made! `new_data.txt` has been added, and `dancing.jpg` has been removed. 
 
    ```
    file name                                                        | size       | hash