Part I - WeatherPy In this example, you’ll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you’ll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities. Your first objective is to build a series of scatter plots to showcase the following relationships: Temperature (F) vs. Latitude Humidity (%) vs. Latitude Cloudiness (%) vs. Latitude Wind Speed (mph) vs. Latitude After each plot add a sentence or too explaining what the code is and analyzing. Your next objective is to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude): Northern Hemisphere - Temperature (F) vs. Latitude Southern Hemisphere - Temperature (F) vs. Latitude Northern Hemisphere - Humidity (%) vs. Latitude Southern Hemisphere - Humidity (%) vs. Latitude Northern Hemisphere - Cloudiness (%) vs. Latitude Southern Hemisphere - Cloudiness (%) vs. Latitude Northern Hemisphere - Wind Speed (mph) vs. Latitude Southern Hemisphere - Wind Speed (mph) vs. Latitude After each pair of plots explain what the linear regression is modelling such as any relationships you notice and any other analysis you may have. Your final notebook must: Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Perform a weather check on each of the cities using a series of successive API calls. Include a print log of each city as it’s being processed with the city number and city name. Save a CSV of all retrieved data and a PNG image for each scatter plot. Part II - VacationPy Now let’s use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment. Note: if you having trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry. Create a heat map that displays the humidity for every city from the part I of the homework. heatmap Narrow down the DataFrame to find your ideal weather condition. For example: A max temperature lower than 80 degrees but higher than 70. Wind speed less than 10 mph. Zero cloudiness. Drop any rows that don’t contain all three conditions. You want to be sure the weather is ideal. Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number. Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates. Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country. hotel map As final considerations: Create a new GitHub repository for this project called API-Challenge (note the kebab-case). Do not add to an existing repo You must complete your analysis using a Jupyter notebook. You must use the Matplotlib or Pandas plotting libraries. For Part I, you must include a written description of three observable trends based on the data. You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels. For max intensity in the heat map, try setting it to the highest humidity found in the data set. Hints and Considerations The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook. You may want to start this assignment by refreshing yourself on the geographic coordinate system. Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome. A starter code for Citipy has been provided. However, if you’re craving an extra challenge, push yourself to learn how it works: citipy Python library. Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors – spending hours investigating their entire code – when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don’t let this be you! Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we’re recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so? In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes). Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters. Remember that each coordinate will trigger a separate call to the Google API. If you’re creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities. Lastly, remember – this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you’ve gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!

Agent Skills for SEO keyword research using Google Trends data via SerpApi. Built for AI agents that generate tech and developer-focused blog content.

Agent Skill for querying Google Trends via Apify REST API

A Claude Code skill that scrapes and analyzes Google Trends data using Playwright. No API keys needed — free, local, and fully autonomous. Based on luminati-io/google-trends-api by Bright Data.

Analyze weather trends in relation to the equator (latitude 0) using heat maps, scatter plots, and other calculations. Utilize API skills by pulling hotels from specific cities using google, and finding their location on a map.

A dataset of Data Scientist job listings across the U.S., sourced via the Google Jobs API. Includes roles, salaries, locations, and descriptions—ideal for market analysis, skill trends, and salary benchmarking.

Full end-to-end Data Analyst project: Data wrangling, API & web scraping, trend analysis, and interactive dashboards (Power BI & Google Looker Studio) using real-world developer survey data. Skills: Python, Pandas, data cleaning, EDA, visualization, dashboard storytelling.

AI Job Assistant is a smart career platform that analyzes resumes, extracts skills, and provides job recommendations. It offers market insights like salary trends, top hiring locations, and job competition. Integrated with a ChatBot using Google Gemini API, it helps users with career guidance and job-related queries.

**Software Engineer III // Full Time, Permanent// REMOTE** **Salary:- 12-16 LPA per year** Essential Job Requirements  1. Education and Experience Proven work experience of 6 to 8 years in software development Broad experience designing, programming, and implementing large information systems. Ability to provide in-depth evaluation and analysis of unique complex technological issues. Modify existing software to correct errors and improve the quality, reliability, productivity and performance of the application as well as make recommendations for improvement & innovation Experience working in an Agile/Scrum development process BS/MS degree in Computer Science, Engineering or a related subject 2. Required Skills, Roles and responsibilities Deep understanding of Micro services is mandatory Knowledge of messaging systems, e.g. Kafka, MQTT, RabbitMQ Frameworks: Microsoft .Net/.Net Core Design Patterns: API/Micro service Driven Development Languages: C#, JavaScript libraries a plus (Angular/ React) Agile Methodologies & Practices: SCRUM Thorough knowledge of API specification languages like Swagger and JSON Schema Experience in Relational Databases and NoSQL is desired Experience working on Azure/AWS/GCP Solid understanding with hands-on implementation of REST APIs Must have experience with implementation of Google APIs/ Payment APIs integration Knowledge of Git or any other version control system. Ability to create technical documentation Reviewing code work for accuracy and functionality. Delegating tasks to team members. Keeping up-to-date with industry trends and technology developments 3. Preferred Skills Experience in Python/ PHP is a plus Knowledge using Domain Driven Design Hands-on experience with Azure is a plus 4. Physical Requirements Work 45 hours a week although overtime work is often necessary to meet deadlines. Extended periods of time sitting at a desk and use of computer and phone during conference calls.