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"Data Science is the sexiest job of the 21st century - It has exciting work and incredible pay".
Learning Data Science though is not an easy task. The field traverses through Computer Science, Programming, Information Theory, Statistics and Artificial Intelligence. College/University courses in this field are expensive. Becoming a Data Scientist through self-study is challenging since it requires going through multiple books, websites, searches and exercises and you will still end up feeling "not complete" at the end of it. So how do you acquire full-stack Data Science skills that will get you a and give you the confidence to execute it?
Applied Data Science with Python addresses the problem. This course provides extensive, end-to-end coverage of all activities performed in a Data Science project. If teaches application of the latest techniques in data acquisition, transformation and predictive analytics to solve real world business problems. The goal of this course is to teach practice rather than theory. Rather than deep dive into formulae and derivations, it focuses on using existing libraries and tools to produce solutions. It also keeps things simple and easy to understand.
Through this course, we strive to make you fully equipped to become a developer who can execute full fledged Data Science projects. By taking this course, you will
By becoming a student of V2 Maestros, you will also get maximum discounts on all of our other current and future courses (coupon codes inside the course material). You will also get prompt support of all your queries and questions. We continuously strive to improve our course material to reflect the latest trends and technologies
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|Section 1: Introduction|
About this coursePreview
About V2 MaestrosPreview
|Section 2: What is Data Science?|
Basic Elements of Data Science
Learning from relationshipsPreview
Modeling and Prediction
Use Cases for Data Science
|Section 3: Data Science Life Cycle|
Stage 1 - Setup
Stage 2 - Data Engineering
Stage 3 & 4 - Analysis and Production
|Section 4: Statistics for Data Science|
Types of Data
|Section 5: Python for Data Science|
Python libraries Overview
Examples 1 - Series and Data Frames
Examples 2 - Grouping and Graphics
|Section 6: Data Engineering|
Text Preprocessing TF-IDF
Python examples for Data Engineering
|Section 7: Machine Learning and Predictive Analysis|
Types of AnalyticsPreview
Types of Learning
Analyzing results and errors
Python Use Case : Linear Regression
Python Use Case : Decision Trees
Naive Bayes Classifier
Python Use Case : Naive Bayes
Python Use Case : Random Forests
Python Use Case : K-Means Clustering
Association Rules Mining
Python Use Case : Association Rules Mining
|Section 8: Advanced Topics|
Artificial Neural Networks and Support Vector Machines
Bagging and Boosting
Python Use Case : Advanced Methods
|Section 9: Conclusion|
BONUS Lecture : Other courses you should check out
V2 Maestros is dedicated to teaching big data / data science at affordable costs to the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building data science talent across the world.