Hamoye Data Science Internship Program (HDSC) presents you with the most efficient, cost-effective introduction to the skills you need to build a career in Artificial Intelligence and Data Science. The Data Science internship program is open to all university/college students, recent graduates, and career professionals seeking experience with real-world projects to acquire skills required to get a head start as a Data Scientist or MLOps Engineer in any sector, including but not limited to eCommerce, fintech, healthcare, energy, investment, insurance, banking, etc.
My Grade: 80.0% School: OneSchool :: Certificate
Electric Utilities report a huge amount of information to government and public agencies. They include very granular data on fuel burned, electricity generated, power plant usage patterns, plant capacity factors and emissions from greenhouse gases. However, this data is not well documented and sometimes they are provided in a format that makes it difficult to understand. This course explores how machine learning techniques can be an invaluable tool for solving one of the grand challenges posed to humanity - climate change. At the end of this course, you will work on cleaning, wrangling, exploring, providing summary statistics and interesting visualisations on a public utility data containing millions of rows and tens of files in structured format.
My Grade: 75.0% School: OneSchool :: Certificate
The dataset for the remainder of this quiz is the Appliances Energy Prediction data. The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters). The attribute information can be seen below.
Project is created with:
- Python : 3.6
- Google Colab