Data Science from Scratch: The #1 Data Science Guide for Everything A Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Tree
☆★Buy the Paperback version of this book, and get the Kindle eBook version included for FREE★☆ If you are looking to start a new career that is in high demand, then you need to continue reading. Data scientists are changing the way big data is used in different institutions.
Big data is everywhere, but without the right person to interpret it, it means nothing.
So where do business find these people to help change their business?
You could be that person.
It has become a universal truth that businesses are full of data.
With the use of big data, the US healthcare could reduce their health-care spending by $300 billion to $450 billion.
It can easily be seen that the value of big data lies in the analysis and processing of that data, and that’s where data science comes in.
★★ Grab your copy today and learn ★★ ♦ In depth information about what data science is and why it is important.
♦ The prerequisites you will need to get started in data science.
♦ What it means to be a data scientist.
♦ The roles that hacking and coding play in data science.
♦ The different coding languages that can be used in data science.
♦ Why python is so important.
♦ How to use linear algebra and statistics.
♦ The different applications for data science.
♦ How to work with the data through munging and cleaning
♦ And much more...
The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow.
As businesses and the internet change, so will data science. This means it’s important to be flexible.
When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in?
If you want to get started in a new, ever growing, career, don’t wait any longer. Scroll up and click the buy now button to get this book today!
Steven Cooper is a freelance writer, producer, and the author of three previous novels. A former television reporter, he has received multiple Emmy awards and nominations, a national Edward R. Murrow Award, and many honors from the Associated Press. He taught writing at Rollins College (Winter Park, FL) from 2007 to 2012. He currently lives in Atlanta.
Absolutely awful book. Half of it is full of superfluous language to fill up space the rest is just buzz words to create a word soup. “Data Science” and “Big Data” are repeated hundreds of times. “Analytics” and “analytical” are also used just as often. It’s like the tip of the tip of the snowflake at the tip of the iceberg. How this was written? Then peer reviewed?? And actually published???? Amazing. It’s possibly the worst book I’ve ever read. It’s so bad I got the audio version just to fall asleep to. You will learn absolutely nothing except the ambiguity of data science and big data. I am thinking of writing the author it’s just so amazing bad! My capstone project I wrote at 25 just hoping to graduate and get by was light years better.
It promises a comprehensive guide but falls short, especially for coders seeking depth. The book covers key topics like Python and machine learning but often skims over complex concepts, leaving readers wanting more detail. The hands-on coding approach feels disjointed, with oversimplified examples. Overall, it’s not in-depth enough for serious learners, making it better as a supplementary resource than a primary guide.