Pygame is loaded with python projects to build portfolio url the modules you need for computer graphics and sound. Within seconds, our Python code returns the portfolio with the highest Sharpe Ratio as well as the portfolio with the minimum risk. But otherwise, you should also consider topics that may interest a broader audience. The number will then be displayed to the user. When we surf the internet, many unwanted websites keep showing up. Post your code on GitHub GitHub is a popular place where programmers share their code and project results.

Jupyter Notebook allows you to easily mix code, text, and images in Python. This IDE provides great opportunities for creating visually appealing documents that seamlessly combine your code, visualizations, tables, and explanations.

However, based on your personal preferences, you may choose to work with another Python IDE. In the end, find something that you're comfortable with. GitHub is a popular place where programmers share their code and project results. Generally, it's common practice among Python Projects To Build Portfolio Zoom data scientists to make their personal projects publicly available.

While business projects are usually not open source due to competition considerations, big tech companies like Facebook and Google make lots of their projects open. So, when you make your work public on GitHub, you demonstrate that you belong to the community of data scientists contributing to open-source work. Data science is all about telling stories with data, so it's important to show that you feel comfortable using Python and major data science libraries.

However, you don't create plots just to have a pretty picture, and you don't run machine learning algorithms just to get accurate models.

As a data scientist , you should be able to add meaning to your findings, differentiate between what's important and what isn't, and elaborate on any interesting insights that you get from your data. Thus, it's essential that your data science portfolio include a detailed interpretation of each project's results.

Beyond a proficiency in Python for data science, hiring managers have another set of very important skills they look for when searching for data scientists: written and oral communication. In fact, your ability to communicate complex machine learning concepts in simple terms predicts how well you're going to communicate with your teammates and managers. Are you able to explain the results of your machine learning model so that it makes sense to a non-IT person?

Writing a blog is a great way to demonstrate that you really understand what the data is "telling" you and can explain the results to somebody who's maybe not as familiar with data science. You can use Medium or other blogging platforms to start your data science blog. Building a portfolio is an iterative process. As you acquire new skills, discover new tools, or read about another interesting technique, your portfolio should also be updated to reflect your newfound knowledge.

Don't think that you can't edit your project after you make it public—it's absolutely acceptable and common practice to iterate and improve upon your projects after they've been published, especially on GitHub. Discovered how to create interactive visualizations? Consider enhancing some of your projects with these plots. Learned about another trick that can boost the performance of your machine learning model?

Make sure to update the projects in your portfolio accordingly. Follow these tips, and your data science portfolio will help you land your first data science job much faster. But of course, Python Projects To Build Portfolio Design you first need to become very comfortable with Python for data science and master other essential data science skills. Why Have a Portfolio at All? Creating a Data Science Portfolio That Rocks So you've learned the basics of Python for data science and are looking for a place to start your data science portfolio.

Build a portfolio around your interests What are you interested in? Pick projects that others will understand Make sure that the projects in your portfolio aren't so specific that only experts in the area will be able to follow the story. Avoid common datasets Commonly available datasets provide a great opportunity to practice newly acquired skills and concepts, so feel free to use them as an exercise.

Consider these libraries: requests will help you get HTML content. Balance your portfolio with different projects Employers are looking for a specific set of skills when searching for a data scientist.

Use your portfolio to showcase your skills in Python for data science by including different types of projects: A data cleaning project will demonstrate how you're able to use the pandas library for preparing your data for analysis. A data visualization project will show your skills in creating appealing yet meaningful visualizations using available Python libraries matplotlib, seaborn, plotly, cufflinks, bokeh.

A machine-learning project is needed to demonstrate your skills in supervised and unsupervised learning using the scikit-learn library. A story-telling project will verify your ability to derive non-trivial insights from data. Participate in competitions Competitions are quite popular in the data science community. By participating in different data science competitions, you'll be able to: Practice your coding and data science skills.

Assess where you stand compared to other data scientists. Demonstrate your achievements to potential employers. Check out the following data science competition platforms if you're interested: Kaggle DrivenData Codalab 6. Check out portfolios of other successful data scientists It's always easier to create something when you see good examples. Consider using Jupyter Notebook Jupyter Notebook allows you to easily mix code, text, and images in Python.

Post your code on GitHub GitHub is a popular place where programmers share their code and project results. Tell stories with your data Data science is all about telling stories with data, so it's important to show that you feel comfortable using Python and major data science libraries. Start a blog Beyond a proficiency in Python for data science, hiring managers have another set of very important skills they look for when searching for data scientists: written and oral communication.

Update your portfolio Building a portfolio is an iterative process. Wrap-up Follow these tips, and your data science portfolio will help you land your first data science job much faster.

Looking for a data science job? Based on what we learned, we should be able to get the Rp and Op of any portfolio. The last element in the Sharpe Ratio is the Risk free rate Rf. A common proxy for the risk free rate is to use Treasury Bill yields. The higher the Sharpe Ratio , the better a portfolio returns have been relative to the taken risk. If we could choose between multiple portfolio options with similar characteristics, we should select the portfolio with the highest Sharpe Ratio.

In the next section we are going to calculate the Sharpe Ratio for multiple random generated portfolios. Now that we know a bit more about portfolio optimization lets find out how to optimize a portfolio using Python. To keep things consistent, I will follow the same methodology that we applied in my previous post in order to calculate portfolio returns and portfolio risk.

Therefore, I will not go into the details on how to do Python Projects To Build Portfolio Keyword this part since you can refer to my previous post. We will start by retrieving stock prices using a financial free API and creating a Pandas Dataframe with the daily stock returns. This part of the code is exactly the same that I used in my previous article. If you have questions feel free to have a look at it. You can obtain a free API Key with requests per month after signing up.

Then, pass your API key with the get request as shown below:. Next, we are going to generate random portfolios i. We start by defining empty lists where we will append the calculated portfolio returns , risk and Sharpe Ratio for each of the random portfolios. The calculation will happen in a for loop. Note that we use Numpy to generate random arrays containing each of the portfolio weights. For simplicity reasons we have assumed a Risk free rate of 0. Having our portfolio weights, we can move on to calculate the annualised portfolio returns, risk and Sharpe Ratio.

If you are unfamiliar with the calculation, feel free to have a look at my previous post where portfolio risk calculation is explained in details. Now that we have created random portfolios, we can visualize them using a Scatter plot in Matplotlib:. In the graph, each point represents a portfolio. We see that portfolios with the higher Sharpe Ratio are shown as yellow.

Yellow coloured portfolios are preferable since they offer better risk adjusted returns. But how can we identify which portfolio i. And what about the portfolio with the highest return? And lowest risk?

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