Get yourself trained on Data Mining with with this Online Training Data Mining with Python! Real-Life Data Science Exercises.
Online Training Data Mining with Python! Real-Life Data Science Exercises
Learn Data Mining With Fascinating ExamplesDo you want to learn data science? Youve come to the right place. This course was funded by a #1 project on Kickstarter with the Mammoth Interactive student community.Learn the backbone of data mining with data scientist and mentor Koyuki Nakamori from Mammoth Interactive. Enroll now to learn data classification, visualization, plotting and statistical analysis.Learn To Build Predictive ModelsNot a single second is wasted in this engaging course. Our amazing instructor Koyuki Nakamori explains everything from a basic, beginner level to make each step understandable to all. You will learn to create, evaluate and use data models to make predictions. And so much more.This is an incredible course full of cutting-edge information. Grow your skills and become an indispensable data scientist today in one compact, no-nonsense 5 hour masterclass only from Mammoth Interactive.Grab The Future By Understanding DataWhat is data mining? Data mining is getting useful actionable insights from data. Whoever owns data owns the future. But owning data is not good enough. You need to know how to draw insights from a dataset, draw useful insights, look at statistics, and find patterns using a dataset.Gain An Empowering, Competitive SkillsetLearning data science is empowering because you will get competitive advantages as a company or individual. Everyone should know how to create basic visualizations from data to help you predict the future.Use a practical dataset to learn data wrangling. You will learn how to clean data, filter noise, make data available for analysis. You will learn about statistics and perform simple statistics with a range of examples.Practice With Realistic ProjectsYou will use real world examples of data mining and datasets to learn each topic step by step.You will learn cluster analysis, classification and regression, including logistic regression. You will be able to use the K-NN classifier and SVM. You will learn association, correlation, and detecting outliers in univariate, multivariate and high dimensional spaces. You will also learn dimensionality reduction.Practice with pop quizzes embedded in lectures for you to test yourself along the way. You will be challenged to complete more complex tasks on your own. You will be introduced to frameworks, including Apache Spark, the number one framework used for distributed processing. It is a streamlined alternative to Map-Reduce. Spark applications can be written in Scala, Java or Python.Learn Machine Learning for Data ScienceLet’s design chains of transformations together! You will learn how to chain Spark dataframe methods together to perform data munging. You will understand the Spark-ML API, and recognize the differences from SK-Learn.With concrete examples you will chain Spark-ML Transformers and Estimators together to compose Machine Learning pipelines. You will learn how to mine and store data. We cover text mining, network mining, the Python Matrix library, and mining a database-SQL.You will also learn natural language processing from scratch, including how to clean text data. You will learn how to use the Count Vectorizer and TFIDF. You will also complete a practical example using Spam data.You will learn how to continue your data science journey on your own. You will be able to find challenges and train yourself to learn more in the field. You will be equipped with all the tools to ready you in the field.Enroll Now To Join The Mammoth Community
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.