- Use real world examples of data mining and datasets.
- Practice for real world projects such as: learn how to find data on a house when looking to become a homeowner. You will be able to look through house data to find useful information from a text dataset.
- Clean data, filter noise, make data available for analysis.
- Perform cluster analysis, classification and regression, including logistic regression
- Use the K-NN classifier and SVM.
- Detect outliers in univariate, multivariate and high dimensional spaces
- Learn how to use Apache Spark, the number one framework used for distributed processing.
- And many more topics explained with concrete examples
- All levels welcome!
- If you have no coding experience: we’ve included an introductory section on the Python programming language with Nimish Narang from Mammoth Interactive.
- Mac/Windows compatible!
- Install the free package manager Anaconda. We will show you how to install necessary packages from there.
Do you want to learn data science? You’ve 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 Models
Not 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 Data
What 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 Skillset
Learning 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 Projects
You 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 Science
Let'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
- Take this course if you want to become a data scientist.
- Take this course if you are interested in data analysis.
- Take this course if you want a competitive edge with your tech skills.
- Take this course if you want a compact, no-nonsense 5 hour bootcamp to learn one of the most in demand skills of the year
- 7 hours on-demand video
- 9 Articles
- 8 Supplemental Resources
- Full lifetime access
Koyuki is a data scientist with a background in mathematics, statistics, and operations research. A mentor to many junior data scientists, Koyuki says, 'Love what I do and eager to teach and learn more!'
StartLearning Python in Pycharm (2:40)
StartSource Files - Learning Python with Mammoth Interactive
StartDownloading and Installing Pycharm and Python
StartDeclaring Variables in Python (13:17)
StartUsing and Converting Variables (12:35)
StartTypes of Collections in Python (12:47)
StartCollections Operations (8:42)
StartControl Flow: 'If' Statements (12:50)
Start'While' Loops and 'For' Loops (10:44)
StartClasses and Objects (15:40)