For this blog post, we sat down with three learners to understand how using Workspace has benefited their learning journeys and helped them advance their career prospects.
<h3 id="the-perfect-upskilling-environment">The perfect upskilling environment</h3>
Muhammad Salman is a behavioral science practitioner in Singapore who has been using Datacamp as a way to upskill in data analysis with R.
After getting started with DataCamp out of an interest in AI, he realized along the way that his interests aligned better with R. Using DataCamp, He made the switch and has been diligently practicing since the start of the year, completing a total of 15 courses!
Muhammad used Workspace as a tool to continue his learning journey and apply his newly acquired data skills.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1632319319/how-workspace-empowers-learners-2_phbv1i.png"></center>
<h3 id="the-ideal-personal-development-environment">The ideal personal development environment</h3>
Nathan, a product manager at edX, started using DataCamp in early 2017. Back then, he used it as a means to discover Python and analytics and completed over 15 courses and data science career tracks!
Once Workspace was released, he started to use it as a portfolio with a two-fold purpose: First, to be able to connect all the things he’s learned as practice, and second, to build a learner record of all his courses and work completed that can be viewed on his profile. He continues to explain that the primary goal was to create an artifact of his thinking—“what I can” versus “what I cannot.”
The idea of Workspace for Nathan was not just knowledge acquisition, but retention and practice. This is seen in his Workspace publication on Airbnb in New York based on a dataset from Kaggle, which felt close to home given he was in the process of booking a honeymoon. It’s the little things like that that can make practicing and applying your skills even more worthwhile.
<blockquote>
Workspace was the ideal tool for me to really push the limits of my data science learnings. - Nathan
</blockquote>
Nathan is going to be starting his Masters in Applied Data Science this fall at the University of Michigan. He accredits DataCamp for helping him get accepted and we can’t wait to see the amazing things he is going to accomplish!
<h3 id="the-convenient-online-coding-tool">The convenient online coding tool</h3>
Ray started with a pharmaceutical background but aims to diversify into data science. To do so, he enrolled in a graduate degree program at UC Boulder to kickstart his career.
Ray is primarily using Workspace as a tool to build out his portfolio for career development purposes. He used his skills to publish a wide array of insights, ranging from an analysis on global health and wealth to dice games. Ray has been using Workspace as his go-to-tool to practice and apply his code.
The unique value proposition that Workspace provides, like the cloud-based coding environment with no installation, is what Ray finds the most enticing:
<blockquote>
What I enjoy most about Workspace is that I can almost have a bit of a pseudo-cloud set up where I can... log onto single-point and continue (working). - Ray
</blockquote>
Interested in finding out how Workspace can improve your learning journey? Get started with <a target="_blank" href="http://workspace.datacamp.com/">Workspace</a> today!
<center><a target="_blank" href="http://workspace.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1627915174/how_learners_use_workspace_banner_kej2sm.png" /></a></center>

https://www.datacamp.com/community/blog/how-workspace-empowers-learners

Learn how Workspace is becoming the go-to tool for our learners to apply their data science skills and enhance their career opportunities.

How Workspace empowers learners’ personal development

We sat down with our DataCamp Workspace users to learn more about their unique experiences while using the tool.. Who would’ve thought that a simple in-browser tool could transition to such a multi-purpose platform? Keep reading to find out how.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1627563037/blog_post_on_workspace__1__qvioy2.png" /></a></center>
<h3 id="the-ideal-tool-to-create-your-portfolio">The ideal tool to create your portfolio</h3>
Our first stop is with <a target="_blank" href="https://www.datacamp.com/profile/rpallangyo">Richard Pallangyo</a>—originally from Tanzania, Richard is currently living in the United States. After successfully completing his undergraduate degree, he is now looking to pursue a career in data science and software.
Through his learning journey on DataCamp, Richard discovered Workspace: “As long as I have access to the internet, I think I will prefer to use Workspace.” We couldn’t agree more! Richard has a total of nine Workspace publications that he used to build a portfolio for various job interviews. During one of his interviews, he leveraged his DataCamp profile to share his extensive portfolio.
<blockquote>
“They found one of my projects to be very interesting and wanted me to talk about it in the interview.” - Richard Pallangyo
</blockquote>
<h3 id="the-data-visualization-opportunities">The data visualization opportunities</h3>
Next, we talked to <a target="_blank" href="https://www.datacamp.com/profile/jorgitorr">Jorge Rodriquez Ramos</a>, a postdoctoral researcher in biophysics. In Jorge’s world of academia, the go-to programming language is usually MATLAB, but after initially discovering R, he came across Python and never turned back.
Jorge got started with DataCamp through a colleague&#39;s recommendation and ended up completing 14 courses!.
After upskilling on DataCamp, Jorge utilized Workspace to put his newfound learnings to the test. He used Workspace to create simple but very clear data visualizations to share with his colleagues, as an alternative to the more traditional, static plots that are often still used in the academic world. Jorge knows people who still use Fortran 90 and 77 to code, so by using Workspace to create easy-to-read visualizations, he is really “ahead of the curve.”
<h3 id="putting-newly-acquired-data-skills-into-practice">Putting newly acquired data skills into practice</h3>
Lastly, we talked to <a target="_blank" href="https://www.datacamp.com/profile/nsanjaysanju676">Sanjay</a>, a pre-final year student in India pursuing math and computing majors while balancing freelance work as a probability and statistics subject matter expert.
His friends introduced him to DataCamp and he quickly became a fan of the skill and career tracks: “The instructors are highly knowledgeable, everything is practically oriented—it’s amazing.” His friends also reminded him that DataCamp has a new product called Workspace, which he then started to explore. (My two cents on this? Sanjay has good friends.)
Sanjay&#39;s Workspace publication on COVID-19 data was based on a dataset from Kaggle and was created for the sole purpose of an academic assignment his professor gave him. He started working outside of Workspace and quickly realized that Workspace would make his life a lot easier. He created an in-depth analysis based on his DataCamp learnings, published them, and submitted them by simply sending his professor a link.
<blockquote>
“I learned most of my information from DataCamp’s skill tracks and the career tracks. I applied those skills in my Workspace project.” - Sanjay N.
</blockquote>
<h3 id="what-s-next-">What’s next?</h3>
Workspace is your one-stop solution to write, run, and share your analysis. And with such a flexible and versatile tool, the opportunities are endless.
<center><a target="_blank" href="http://workspace.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1627915174/how_learners_use_workspace_banner_kej2sm.png" /></a></center>

https://www.datacamp.com/community/blog/how-learners-use-workspace

From upskilling to portfolio curation - see how learners are utilizing Workspace.

How Workspace is enabling learners across the globe

Table of Contents
<ul>
<li><a href="#importing-the-data">Importing the Data</a></li>
<li><a href="#preprocessing">Preprocessing</a></li>
<li><a href="#training">Training</a></li>
<li><a href="#getting-the-validation-set-predictions">Getting the Validation Set Predictions</a></li>
<li><a href="#getting-the-test-set-predictions">Getting the Test Set Predictions</a></li>
<li><a href="generating-the-output-dataframe">Generating the Output Dataframe</a></li>
<li><a href="#generating-the-output-file">Generating the Output File</a></li>
</ul>
In this tutorial, we will go through the Titanic dataset, analyze it, and submit the results to the actual competition. You have already learned how to fetch the data and import it in your Kaggle Notebook in <a target="_blank" href="https://www.datacamp.com/community/tutorials/tutorial-kaggle-datasets-tutorials-kaggle-notebooks">this tutorial</a>. Now we will be using DataCamp Workspace as the data science notebook. You will be learning how to use both tools on different platforms so that you can choose your favorite.
First, refer to the "Competitions" section in Kaggle and search for Titanic (Figure 5.4). Before going directly into the notebook, you should understand all the information shared in the "Description and Rules" sections. It is important to also check other users' notebooks, as this could give you more ideas about the solution.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748483/image6_irvam2.png"></center>
<center>Figure 5.4: Titanic - Machine Learning from Disaster</center>

The competition is about using machine learning to create a model that predicts which passengers would have survived the Titanic shipwreck. We will be using a dataset that includes passenger information like name, gender, age, etc. There will be 2 different datasets that we will be using. The first one is titled &#39;train.csv&#39;. In this dataset, besides the passenger information, there are also the ground truth values such as &#39;survived&#39; and &#39;not survived&#39;. In the second dataset which is titled &#39;test.csv&#39;, there will not be any <a target="_blank" href="https://towardsdatascience.com/in-ai-the-objective-is-subjective-4614795d179b">ground truth</a> values, since we are asked to predict those and submit our results.
The submission file will have only 2 columns: PassengerId and Survived. The submission system will produce errors if you try to upload a file with more than 2 values. Please note that you would be able to submit 10 submissions a day.
With the information provided in the "Competition Description" section, the next step would be to fetch the data to our DataCamp Workspace and analyze the data.
Firstly, you need to download the dataset in Data Explorer by going into the Data tab (Figure 5.5). You should be downloading both &#39;train.csv&#39; and &#39;test.csv&#39;.
After downloading the files, you should be navigating to <a target="_blank" href="https://www.datacamp.com/workspace/dashboard">DataCamp Workspace</a>. Please note that you need to create an account first. Creating an account and using the workspace is free.
You can easily drag and drop files on the left side of the notebook (Figure 5.6).
 
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748482/image2_v8jxnm.png"></center>
 
<center>Figure 5.5: Downloading the Dataset</center>
 
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748482/image5_vv8uem.png"></center>
 
<center>Figure 5.6: Adding the Dataset to DataCamp Workspace</center>
 
The next step is to check the data dictionary in the dataset description section. This will help us to decide which columns to visualize.
<ul>
<li>survival: Survival</li>
<li>pclass: Ticket class</li>
<li>sex: Sex</li>
<li>Age: Age in years</li>
<li>sibsp: # of siblings / spouses aboard the Titanic</li>
<li>parch: # of parents / children aboard the Titanic</li>
<li>ticket: Ticket number</li>
<li>fare: Passenger fare</li>
<li>cabin: Cabin number</li>
<li>embarked: Port of embarkation</li>
</ul><div style="padding-bottom:20px;" id="importing-the-data"></div>
<h2>Importing the Data</h2>
You can use bash commands in jupyter notebooks by placing an exclamation mark in front of the cell. We will be checking the location of the uploaded dataset files using pwd and ls commands.
<pre><code class="lang-python">!pwd; ls
</code></pre>/work/files/workspace
notebook.ipynb test.csv train.csv
Pandas is a Python library and it is used for data manipulation and data analysis processes. We have now removed Ticket, Name, and PassengerId columns since they do not have any meaningful numerical or categorical data. Now you will be able to see the rest of the features to be used.
In the next couple of lines, the training and the test dataset files are read using the read_csv method of Pandas. Note that Survived and PassengerId columns are added respectively to train and test dataframes. We will be feeding the model separately using the features in columns_to_be_added_as_features and the labels in the Survived column. But in order to make the preprocessing simpler, we can combine them for now. The test_df_matcher variable includes PassengerId as well. We will be using it to match the PassengerId with the test set predictions, to generate the CSV file which will be uploaded to Kaggle as our result.
<pre><code class="lang-python">import pandas as pd
columns_to_be_added_as_features = [&#39;Sex&#39;,&#39;Age&#39;,&#39;SibSp&#39;,&#39;Parch&#39;,&#39;Pclass&#39;,&#39;Fare&#39;,&#39;Embarked&#39;]
train_df = pd.read_csv(&#39;train.csv&#39;, usecols=columns_to_be_added_as_features + [&#39;Survived&#39;])
test_df_matcher = pd.read_csv(&#39;test.csv&#39;, usecols=columns_to_be_added_as_features + [&#39;PassengerId&#39;])
test_df = test_df[columns_to_be_added_as_features]
</code></pre>With the head() method, we can see the first 5 rows alongside their column names. You can define the amount of the initial rows to be shown as the parameter inside the head() method. The number of rows' values in the training set and in the test set are printed respectively in the following cell.
<pre><code class="lang-python">print(train_df.head())
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748482/image1_azdsq9.png"></center>
<center>Figure 5.7: Dataframe</center>

<pre><code class="lang-python">print(&quot;Number of rows in training set: {}&quot;.format(len(train_df)))
print(&quot;Number of rows in test set: {}&quot;.format(len(test_df)))
</code></pre>Number of rows in training set: 891
Number of rows in test set: 418
<h2 id="preprocessing">Preprocessing</h2>
We should convert the type of the values to float, which will be used as features in the model. This conversion will allow us to perform some numerical operations when normalizing the data.
<pre><code class="lang-python">for column_title in columns_to_be_added_as_features:
 if column_title in [&#39;Embarked&#39;, &quot;Sex&quot;]:
 continue
 train_df[column_title] = pd.to_numeric(train_df[column_title], downcast=&quot;float&quot;)
 test_df[column_title] = pd.to_numeric(test_df[column_title], downcast=&quot;float&quot;)

train_df[&quot;Survived&quot;] = pd.to_numeric(train_df[&quot;Survived&quot;], downcast=&quot;float&quot;)
</code></pre>We need to feed the model using numerical values. In the code piece below the strings are converted into numerical values
<pre><code class="lang-python">train_df[&#39;Embarked&#39;].replace(&#39;Q&#39;, 0,inplace=True)
train_df[&#39;Embarked&#39;].replace(&#39;S&#39;, 1,inplace=True)
train_df[&#39;Embarked&#39;].replace(&#39;C&#39;, 2,inplace=True)

test_df[&#39;Embarked&#39;].replace(&#39;Q&#39;, 0,inplace=True)
test_df[&#39;Embarked&#39;].replace(&#39;S&#39;, 1,inplace=True)
test_df[&#39;Embarked&#39;].replace(&#39;C&#39;, 2,inplace=True)

train_df[&#39;Sex&#39;].replace(&#39;male&#39;, 0,inplace=True)
train_df[&#39;Sex&#39;].replace(&#39;female&#39;, 1,inplace=True)

test_df[&#39;Sex&#39;].replace(&#39;male&#39;, 0,inplace=True)
test_df[&#39;Sex&#39;].replace(&#39;female&#39;, 1,inplace=True)

print(train_df.head())
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748482/image3_glx4ay.png"></center>
<center>Figure 5.8: Dataframe with Numerical Values</center>

Below, all the features are normalized, which means scaled between 0 and 1 in this case.
<pre><code class="lang-python">#Credit: Michael Aquilina,
#https://stackoverflow.com/questions/26414913/normalize-columns-of-pandas-data-frame
def normalize(df):
 result = df.copy()
 for feature_name in df.columns:
 max_value = df[feature_name].max()
 min_value = df[feature_name].min()
 result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
 return result

train_df = normalize(train_df)
test_df = normalize(test_df)

print(train_df.head())
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748482/image4_iebh1v.png"></center>
<center>Figure 5.9: Normalized Dataframe</center>

Since the dataset will be split into training and validation, we will be shuffling the dataset first to avoid groupings in only one set.
<pre><code class="lang-python">#Credit: Kris, https://stackoverflow.com/questions/29576430/shuffle-dataframe-rows
train_df = train_df.sample(frac=1).reset_index(drop=True)
</code></pre>All the empty values will be replaced with 0; this choice has been made for time-saving purposes. However, you can read <a target="_blank" href="https://towardsdatascience.com/7-ways-to-handle-missing-values-in-machine-learning-1a6326adf79e">this tutorial</a> to learn how to handle missing values in machine learning.
<pre><code class="lang-python">train_df = train_df.fillna(0)
test_df = test_df.fillna(0)
</code></pre>Below, the validation's ratio was set to 0.2, which means 20% of data will be used to validate it. To split the dataset into training and validation sets, we will be using Sklearn&#39;s train_test_split method. Then we will be splitting the features and labels as shown in the last 4 lines of the code piece below.
<pre><code class="lang-python">validation_set_ratio = 0.2
validation_set_size = int(len(train_df)*validation_set_ratio)
training_set_size = len(train_df) - validation_set_size

print(&quot;Total set size: {}&quot;.format(len(train_df)))
print(&quot;Training set size: {}&quot;.format(training_set_size))
print(&quot;Validation set size: {}&quot;.format(validation_set_size))

from sklearn.model_selection import train_test_split

train, val = train_test_split(train_df, test_size=validation_set_ratio)

train_X = train[columns_to_be_added_as_features]
train_Label = train[[&#39;Survived&#39;]]

val_X = val[columns_to_be_added_as_features]
val_Label = val[[&#39;Survived&#39;]]
</code></pre><h2 id="training">Training</h2>
Support vector machines are supervised learning models that are used for classification and regression analysis. This model was chosen with the parameters below. You should be trying other models with different parameter combinations. There is no need to start and stop the code every time you want to modify a parameter or to try another model, as you can automate this process. If you are interested in Support Vector Machines, you can view the <a target="_blank" href="https://www.datacamp.com/community/tutorials/svm-classification-scikit-learn-python">Support Vector Machines with Scikit-learn</a> tutorial on DataCamp.
<pre><code class="lang-python">from sklearn.svm import SVC

SVM_KERNEL = &quot;linear&quot;
SVM_C = 10
SVM_GAMMA = 0.00001

svm_model = SVC(kernel = SVM_KERNEL, C = SVM_C, gamma = SVM_GAMMA)
svm_model.fit(train_X, train_Label)
</code></pre><h2 id="getting-the-validation-set-predictions">Getting the Validation Set Predictions</h2>
In this part, we will be feeding the model validation set features and getting the predictions. Then we will be comparing them with the ground truth values. The Sklearn&#39;s metrics.accuracy_score method was used to make the accuracy calculation easier. The validation set accuracy is calculated as 0.799, which is a good start.
<pre><code class="lang-python">from sklearn import metrics

y_pred = svm_model.predict(val_X)
print(&quot;Accuracy:&quot;,metrics.accuracy_score(val_Label, y_pred))
</code></pre>Accuracy: 0.7988826815642458
<h2 id="getting-the-test-set-predictions">Getting the Test Set Predictions</h2>
Now we proceed to get the predictions for the actual test set. Because there are no ground truth values for the test set, we can only know the accuracy after the predictions file is uploaded to Kaggle.
<pre><code class="lang-python">test_pred = svm_model.predict(test_df)
</code></pre><h2 id="generating-the-output-dataframe">Generating the Output Dataframe</h2>
The PassengerId column is fetched from the dataframe named test_df_matcher. When it is directly returned, the type of the return value will be Series. For this reason, we will be casting it to Pandas DataFrame type. Then we will be inserting the test set predictions as the second column of the dataframe.
<pre><code class="lang-python">result = pd.DataFrame(test_df_matcher[&#39;PassengerId&#39;])
print(result.head(10))
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748483/image9_l38s1p.png"></center>
<center>Figure 5.10: PassengerId Dataframe</center>

<pre><code class="lang-python">result.insert(1, &quot;Survived&quot;, test_pred, True)
result[&quot;Survived&quot;] = pd.to_numeric(result[&quot;Survived&quot;], downcast=&quot;integer&quot;)
print(result.head(10))
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748483/image8_talxf3.png"></center>
<center>Figure 5.11: Finalized Dataframe</center>

<h2 id="generating-the-output-file">Generating the Output File</h2>
Finally, the output format is ready. As the last step, the index values will be removed by passing False to the index parameter. Now you can download the generated file and upload it to Kaggle. This way, you will learn the test set accuracy, after which you will be placed on the leaderboard.
<pre><code class="lang-python">result.to_csv(&quot;out.csv&quot;, index=False)
</code></pre>We will now return to the competition page and click on "Submit Predictions" and then upload the predictions file. After a few seconds, you will get your test accuracy (Figure 5.12).
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646748483/image7_rdjv5h.png"></center>
<center>Figure 5.12: Accuracy of the Test Set</center>

<h2 id="conclusion">Conclusion</h2>
In this tutorial, you have learned how to analyze a dataset and submit your results to a Kaggle competition. If you enjoy doing data science competitions you can learn more about DataCamp Competitions <a target="_blank" href="https://www.datacamp.com/data-science-competitions">here</a>.

https://www.datacamp.com/community/tutorials/tutorial-kaggle-competition-tutorial-machine-learning-from-titanic

Prepare for your first Kaggle Competition with this step-by-step tutorial.  

Learn how to analyze the Titanic dataset in Kaggle and submit the results to the competition.  

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