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Tag: datacleaning

Accessing and Cleaning Data from Garmin Wearables for Analysis

A Friendly how-to guide on extracting Garmin Fenix data for people in a hurry

Photo by Zan on Unsplash

Wake TimeStepsCyclesAscent/DescentHeart RateActivity Type and Minutes (Moderate and Vigorous)Total Moving TimeWalking Step Length, Average SpeedCalories burned, Total fat calories burnedTraining Stress ScoreMax & Average TemperatureStress LevelSpeed & IntensityResting Metabolic Rate, Resting Heart RateSwimming: Pool length, stroke length and distance, swimming cadence, first lap indexRunning: Max running cadence, max general cadence, total strides

TimeDistance travelledLocation (Longitude/Latitude)Altitude & TemperatureHeart RateCadence (and Fractional Cadence)Speed & PowerLeft-Right Balance

Use These Unique Data Sets to Sharpen Your Data Science Skills

Want to get your hands on some real-world data sets right now? Kick off your bootcamp prep with this list of hot-button data sets curated to help you hone different data science skills.

Sponsored Post.

Want to warm up your data science skills before jumping into a bootcamp program? Aspiring data scientists can practice key techniques like data cleaning, data analysis, data visualization and even machine learning with free, publicly available data sets. Hands-on data science exploration is one of the most effective ways to prepare for a data science bootcamp. In addition to learning more about your strengths, interests, and the skills you’ll need to grow, you’ll also gain experience working with the intricacies and idiosyncrasies of real-world data.

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Is Hands-On Knowledge More Important than Theory?

Photo by Katherine Volkovski on Unsplash
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Using Automation in AI with Recent Enterprise Tools

A Classic Data Science Project and approach looks like this:

Data Science (DS) and Machine Learning (ML) are the spines of today’s data-driven business decision-making.

From a human viewpoint, ML often consists of multiple phases: from gathering requirements and datasets to deploying a model, and to support human decision-making—we refer to these stages together as DS/ML Lifecycle. There are also various personas in the DS/ML team and these personas must coordinate across the lifecycle: stakeholders set requirements, data scientists define a plan, and data engineers and ML engineers support with data cleaning and model building. Later, stakeholders verify the model, and domain experts use model inferences in decision making, and so on.

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