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Learn By Building Real World Projects

Projects in Data Science

The practical guide to master real world Data science

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  •   Lectures: 41
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  •   Videos: 4 hours
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  •   Level: All
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  •   Language: English
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  •   Last Updated: 01/2019

  • Instructor : Eduonix Learning Solutions

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Today, the need for storing different types of data has become an essential requirement of any company whether it is big or small. Everyone wants to keep the records of their accounts, customers feedback, user details, a track of an employee and so on. This brings the concept of data science in the picture. They want to keep it easy, simple and hassle-free, and this is why understanding data science for the developers becomes important. Actually, data science can do a lot more than this and it can involve mathematics, chemometrics, computer science and other fields. Knowing this fact, we launched this course completely revolving around all the concepts and theories of applied data science.

Why should I choose this book?

Data science will soon be the option of choice in numerous public domains including health sector. To overcome this demand, the knowledge of applied data science will give you chance to fulfill all the requirements of your client. This course includes various topics covering all the aspects of data science making the concepts well-understood and well-versed for you.

What makes this book so valuable?

This course gives an insight into the basics of applied data science using Python covering topics like introduction to data science, setup, and extended data analysis such as dataset, analyzing output and others. Additionally, it also comprises of topics for making you understand linear and logistic regression. Furthermore, data visualization (MySQL, Plotly, Scattered plot, Advanced Scattered plot), time series, K-Means Clustering and decision tree are also explained.

This course includes

  1. Overview of Applied Data Science and setup
  2. Extended Data Analysis including problem statement, dataset, analyzing output and others.
  3. Linear regression, logistic regression and analyzing the output
  4. Data Visualization of MySQL, Plotly, scattered plot and advanced scattered plot
  5. Time series, data evaluation, plotting the data and time series model
  6. Basics of K-Means clustering, its demonstration, centroids for clustering and output analysis
  7. Decision tree, sections of a decision tree, selecting threshold and much more!

There is no such thing called right time, its you who make the time right! So, stop thinking and explore the concepts of Applied data science now!


SECTION 1 - Introduction

SECTION 2 - Extended Data Analysis

SECTION 3 - Regression

SECTION 4 - Data Visualization

SECTION 5 - Activity

SECTION 6 - Time Series

SECTION 7 - K-Means Clustering

SECTION 8 - Decision Tree