Master in Applied Data Science with Python from Scratch

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

Overview of Applied Data Science and setup
Extended Data Analysis including problem statement, dataset, analyzing output and others.
Linear regression, logistic regression and analyzing the output
Data Visualization of MySQL, Plotly, scattered plot and advanced scattered plot
Time series, data evaluation, plotting the data and time series model
Basics of K-Means clustering, its demonstration, centroids for clustering and output analysis
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

	1	Introduction

	2	Overview of Applied Data Science

	3	Environmental setup - Jupyter Notebook

SECTION 2 - Extended Data Analysis

	4	Understanding Problem Statement

	5	About Dataset - Summary of Referees

	6	Understanding the Dataset Dimension

	7	Analysing the Output

	8	Creating tidy tables

	9	Analysing the Table Structure

SECTION 3 - Regression

	10	Regression - Supervised Learning

	11	Problem Statement- Boston Data

	12	Understanding Linear Regression

	13	Analysing and plotting Output

SECTION 4 - Data Visualization

	14	Problem Statement- Data Visualization

	15	MySQL Data

	16	Plotly for Visualization

	17	Database Connectivity and Configuration

	18	Scattered Plot

	19	Advanced Scattered Plot

	20	Analysing Output

SECTION 5 - Activity

	21	Activity for Extended Data Analysis

	22	Plots of Data Analysis

	23	Activity- Temperature Analysis

	24	Performing Linear Regression

	25	Activity Population Analysis

SECTION 6 - Time Series

	26	Time Series

	27	Problem Statement

	28	Evaluating Data

	29	Plotting the Time Series Data

	30	Time Series Model

SECTION 7 - K-Means Clustering

	31	Introduction to Clustering

	32	Demonstration of K-Means Cluster

	33	Problem Statement

	34	Defining Centroids for Clustering

	35	Computing Distance and new Centroids

	36	Analysing the Output of Cluster

SECTION 8 - Decision Tree

	37	Understanding Decision Tree

	38	Problem Statement- Decision Tree

	39	Implementing sections of Decision Trees

	40	Selecting Threshold

	41	Analysing Output

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