By using Jupyter Notebook we perform future employee description for succinctly outline the ideal candidate's key traits, skills, and alignment with the organization's goals.

Jupyter notebooks outlining theory and calculations for hot polaron cooling in halide perovskite solar cells

## Step 1 - Scraping Complete your initial scraping using Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter. * Create a Jupyter Notebook file called `mission_to_mars.ipynb` and use this to complete all of your scraping and analysis tasks. The following outlines what you need to scrape. ### NASA Mars News * Scrape the [Mars News Site](https://redplanetscience.com/) and collect the latest News Title and Paragraph Text. Assign the text to variables that you can reference later. ```python # Example: news_title = "NASA's Next Mars Mission to Investigate Interior of Red Planet" news_p = "Preparation of NASA's next spacecraft to Mars, InSight, has ramped up this summer, on course for launch next May from Vandenberg Air Force Base in central California -- the first interplanetary launch in history from America's West Coast." ``` ### JPL Mars Space Images - Featured Image * Visit the url for the Featured Space Image site [here](https://spaceimages-mars.com). * Use splinter to navigate the site and find the image url for the current Featured Mars Image and assign the url string to a variable called `featured_image_url`. * Make sure to find the image url to the full size `.jpg` image. * Make sure to save a complete url string for this image. ```python # Example: featured_image_url = 'https://spaceimages-mars.com/image/featured/mars2.jpg' ``` ### Mars Facts * Visit the Mars Facts webpage [here](https://galaxyfacts-mars.com) and use Pandas to scrape the table containing facts about the planet including Diameter, Mass, etc. * Use Pandas to convert the data to a HTML table string. ### Mars Hemispheres * Visit the astrogeology site [here](https://marshemispheres.com/) to obtain high resolution images for each of Mar's hemispheres. * You will need to click each of the links to the hemispheres in order to find the image url to the full resolution image. * Save both the image url string for the full resolution hemisphere image, and the Hemisphere title containing the hemisphere name. Use a Python dictionary to store the data using the keys `img_url` and `title`. * Append the dictionary with the image url string and the hemisphere title to a list. This list will contain one dictionary for each hemisphere. ```python # Example: hemisphere_image_urls = [ {"title": "Valles Marineris Hemisphere", "img_url": "..."}, {"title": "Cerberus Hemisphere", "img_url": "..."}, {"title": "Schiaparelli Hemisphere", "img_url": "..."}, {"title": "Syrtis Major Hemisphere", "img_url": "..."}, ] ``` - - - ## Step 2 - MongoDB and Flask Application Use MongoDB with Flask templating to create a new HTML page that displays all of the information that was scraped from the URLs above. * Start by converting your Jupyter notebook into a Python script called `scrape_mars.py` with a function called `scrape` that will execute all of your scraping code from above and return one Python dictionary containing all of the scraped data. * Next, create a route called `/scrape` that will import your `scrape_mars.py` script and call your `scrape` function. * Store the return value in Mongo as a Python dictionary. * Create a root route `/` that will query your Mongo database and pass the mars data into an HTML template to display the data. * Create a template HTML file called `index.html` that will take the mars data dictionary and display all of the data in the appropriate HTML elements. Use the following as a guide for what the final product should look like, but feel free to create your own design.  - - - ## Step 3 - Submission To submit your work to BootCampSpot, create a new GitHub repository and upload the following: 1. The Jupyter Notebook containing the scraping code used. 2. Screenshots of your final application. 3. Submit the link to your new repository to BootCampSpot. 4. Ensure your repository has regular commits and a thorough README.md file ## Hints * Use Splinter to navigate the sites when needed and BeautifulSoup to help find and parse out the necessary data. * Use Pymongo for CRUD applications for your database. For this homework, you can simply overwrite the existing document each time the `/scrape` url is visited and new data is obtained. * Use Bootstrap to structure your HTML template.

Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.

# Employee Database: A Mystery in Two Parts  ## Background It is a beautiful spring day, and it is two weeks since you have been hired as a new data engineer at Pewlett Hackard. Your first major task is a research project on employees of the corporation from the 1980s and 1990s. All that remain of the database of employees from that period are six CSV files. In this assignment, you will design the tables to hold data in the CSVs, import the CSVs into a SQL database, and answer questions about the data. In other words, you will perform: 1. Data Modeling 2. Data Engineering 3. Data Analysis ## Instructions #### Data Modeling Inspect the CSVs and sketch out an ERD of the tables. Feel free to use a tool like [http://www.quickdatabasediagrams.com](http://www.quickdatabasediagrams.com). #### Data Engineering * Use the information you have to create a table schema for each of the six CSV files. Remember to specify data types, primary keys, foreign keys, and other constraints. * Import each CSV file into the corresponding SQL table. #### Data Analysis Once you have a complete database, do the following: 1. List the following details of each employee: employee number, last name, first name, gender, and salary. 2. List employees who were hired in 1986. 3. List the manager of each department with the following information: department number, department name, the manager's employee number, last name, first name, and start and end employment dates. 4. List the department of each employee with the following information: employee number, last name, first name, and department name. 5. List all employees whose first name is "Hercules" and last names begin with "B." 6. List all employees in the Sales department, including their employee number, last name, first name, and department name. 7. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. 8. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name. ## Bonus (Optional) As you examine the data, you are overcome with a creeping suspicion that the dataset is fake. You surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee. To confirm your hunch, you decide to take the following steps to generate a visualization of the data, with which you will confront your boss: 1. Import the SQL database into Pandas. (Yes, you could read the CSVs directly in Pandas, but you are, after all, trying to prove your technical mettle.) This step may require some research. Feel free to use the code below to get started. Be sure to make any necessary modifications for your username, password, host, port, and database name: ```sql from sqlalchemy import create_engine engine = create_engine('postgresql://localhost:5432/<your_db_name>') connection = engine.connect() ``` * Consult [SQLAlchemy documentation](https://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql) for more information. * If using a password, do not upload your password to your GitHub repository. See [https://www.youtube.com/watch?v=2uaTPmNvH0I](https://www.youtube.com/watch?v=2uaTPmNvH0I) and [https://martin-thoma.com/configuration-files-in-python/](https://martin-thoma.com/configuration-files-in-python/) for more information. 2. Create a bar chart of average salary by title. 3. You may also include a technical report in markdown format, in which you outline the data engineering steps taken in the homework assignment. ## Epilogue Evidence in hand, you march into your boss's office and present the visualization. With a sly grin, your boss thanks you for your work. On your way out of the office, you hear the words, "Search your ID number." You look down at your badge to see that your employee ID number is 499942. ## Submission * Create an image file of your ERD. * Create a `.sql` file of your table schemata. * Create a `.sql` file of your queries. * (Optional) Create a Jupyter Notebook of the bonus analysis. * Create and upload a repository with the above files to GitHub and post a link on BootCamp Spot.

A Jupyter notebook outlining a supervised machine learning approach to predict the outcome of badminton matches