Data Science from Scratch: Data Science for Beginners and Layman

by Narendra Mohan Mittal

**Who this book is written for**
We presume that the data developers, students and data scientists reading this book are knowledgeable about data science, common machine learning methods, and popular data science tools, and have in the course of their work run proof of concept studies, and built prototypes. We offer a book that introduces advanced techniques and methods for building data science solutions to this audience, showing them how to construct commercial-grade data products.
**Inside The Book**
1. **Data Science in a Big Data World**
2. **The Data Science Process**
3. **Key Objectives of Data Science**
4. **General Introduction to Machine Learning**
5. **Internet of Things (IoT)**
6. **The Practical Concepts of Machine Learning**
7. **Transitioning from Data Developer to Data Scientist**
8. **Natural Language Processing**
9. **Approach to Data Cleaning**
10. **GIT Repositories**
11. **Neural Networks**

**Data Science in a Big Data World**
The main things that set a data scientist apart from a statistician are the ability to work with big data and experience in machine learning, computing, and algorithm building. Their tools tend to differ too, with data scientist job descriptions more frequently mentioning the ability to use Hadoop, Pig, Spark, R, Python, and Java, among others.

In data science and big data, you’ll come across many different types of data, and each of them tends to require different tools and techniques. The main categories of data are these:
1. **Structured**
2. **Unstructured**
3. **Natural language**
4. **Machine-generated**
5. **Graph-based**
6. **Audio, video, and images**
7. **Streaming**
**Let’s explore all these interesting data types in this book.**

**Key Objectives of Data Science**
Transitioning from Data Developer to Data Scientist, the idea of how data science is defined is a matter of opinion. I personally like the explanation that data science is a progression or, even better.
This data science evolution consists of a series of steps or phases that a data scientist tracks, comprising the following:
1. Collecting data
2. Processing data
3. Exploring and visualizing data
4. Analyzing (data) and/or applying machine learning (to data)
5. Deciding (or planning) based on acquired insight

While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science. Here’s what to expect:
* Provides a background in big data and data engineering before moving on to data science.
* Includes coverage of big data frameworks like Hadoop and NoSQL
* Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things
* Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate
* Learn how data science is applied to generate value
* Use data visualization to communicate insights
* Covers big data frameworks and applications
It's a big, big data world out there—let *Data Science help* you harness its power and gain a competitive edge for your organization.

283 pages, Kindle Edition

Published December 20, 2018