My aim, as always, was to keep the projects as diverse as possible so you can pick the ones that fit into your data science journey. If you're a beginner, I would suggest starting with the PalmerPenguins dataset as most folks aren't even aware of it right now. A great chance to get a head start. I would love to hear your thoughts on which open source project you found the most useful. Or let me know if you want me to feature any other data science projects here or in next month's edition.
I knew SQL long before learning about Pandas, and I was intrigued by the way Pandas faithfully emulates SQL. Stereotypically, SQL is for analysts, who crunch data into informative reports, whereas Python is for data scientists, who use data to build (and overfit) models. Although they are almost functionally equivalent, I'd argue both tools are essential for a data scientist to work efficiently. From my experience with Pandas, I've noticed the following: Those problems are naturally solved when I began feature engineering directly in SQL. If you know a little bit of SQL, it's time to put it into good use.
Apple Inc. is one of the biggest technology companies in the world that designs, develops, and sells consumer electronics, computer software, and online services. Apple is constantly in need of creative, passionate, and dedicated data scientists that can sit on any number of their teams. From its researched-based artificial intelligence development team at Siri to cloud-base architecture development team at iCloud, Apple has slowly but steadily been building data science teams to handle the avalanche of data accumulated on a daily basis. As with other big tech companies, the role of a data scientist at Apple varies a lot and is dependent on the teams you are assigned to. This means the job will require everything from analytics to machine learning software design to plain engineering.
With the ever-increasing volume, variety, and velocity of available data, scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms enabling us to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. A question that frequently comes up in many data-related discussions is what the difference between DS, ML, and AI is? Can they even be compared? Depending on who you talk to, how many years of experience they have had, and what projects they have worked on, you may get widely different answers to the above question. In this blog, I will attempt to answer this based on my research, academic, and industry experience; and having facilitated numerous conversations on the topic.
On Tuesday, a number of AI researchers, ethicists, data scientists, and social scientists released a blog post arguing that academic researchers should stop pursuing research that endeavors to predict the likelihood that an individual will commit a criminal act, as based upon variables like crime statistics and facial scans. The blog post was authored by the Coalition for Critical Technology, who argued that the utilization of such algorithms perpetuates a cycle of prejudice against minorities. Many studies of the efficacy of face recognition and predictive policing algorithms find that the algorithms tend to judge minorities more harshly, which the authors of the blog post argue is due to the inequities in the criminal justice system. The justice system produces biased data, and therefore the algorithms trained on this data propagate those biases, the Coalition for Critical Technology argues. The coalition argues that the very notion of "criminality" is often based on race, and therefore research done on these technologies assumes the neutrality of the algorithms when in truth no such neutrality exists.
Few issues are as important to businesses today than sustainability. Because the modern consumer cares about the environment, companies need to meet higher expectations about eco-friendly practices. Supply chains, in particular, have a lot of room to improve. It's no secret that logistics chains aren't exactly eco-friendly. They account for more than 80% of carbon emissions globally. The modern business world can't exist without supply chains, but the natural world won't exist in the same way if they don't improve. The good news is there's an . . .
An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital--before they to return to the doctor's office with further complications. But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that aren't optimized for their patients. Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.
You will learn both Python and R Programming with Data Science in this course. Python: You will first learn how to Install Anaconda and Jupyter on your desktop/laptop Python: You will understand and learn the basics of For Loops and Advanced For Loops. You will have clarity on Python generators and will master the flow of your code using "If Else" Python: You will understand Why foundations Modify Lists and Dictionaries and Functions. Learn how to analyze, retrieve and clean data with Python Python: Learn Concatenation (Combining Tables) with Python and Pandas and Manipulating Time and Date Data with Python Datetime Python: You will learn to Use Pandas with Large Data Sets, Time Series Analysis and Effective Data Visualization in Python R: You will learn the most important tools in R that will allow you to do data science R: You will have the tools to tackle a wide variety of data science challenges, using the best parts of R. R: You will learn how to Tidy the data. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored.
Recently, there is increasing attention towards machine learning and its application within personal and business contexts. "The study of computer algorithms that improve automatically through experience" Machine learning leverages mathematical models and big data to solve business problems. This requires two key support areas of data engineering and data science. Within data engineering, the evolution of cloud computing has allowed big data to be stored inexpensively. Within data science, the rise of data scientists and data science tools have allowed better ease of model building and exploration. However, machine learning is NOT the summit of a data analytics evolution journey.