Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Algorithms
Once you have your data, and the hardware and software to manipulate it,
you need to bring them together. Put your data on your programming
software. Find a dataset for free online to work with when you are first

---

starting out. Kaggle.com is free and has a lot of data sets to choose from in
CSV format, which will be easy to work with once you have Pandas library
imported into your Python.
The best algorithms to start with are linear and logistic regression for
supervised learning and k-means clusters in unsupervised learning. These
will be relatively easy starting out, and you can build towards other models
from there.
Visualization tools
You have your data, and now you have created models using one of the
programming languages, and you have a whole collection of data science
libraries to help you do all this faster. Your computer is running well, and
you can create models independently.
You may have created models that display interesting results, but in order to
break it down into layman's terms and communicate your findings with
stakeholders, you'll need to organize it in a way that's easy to visualize. If
you're a data scientist on a marketing project, you may have created a
model that helps break down customers into categories and predicts trends
in buying habits. But if you want to communicate these results to the rest of
your marketing team, you'll need to find a way to communicate so that even
people who aren't familiar with data science can understand your results.
Breaking down your data into charts and graphs and visual will help
compliment your analytics skills. Being able to make visualizations of your
data is extremely important when you are communicating with an audience
who isn't familiar with data analysis

---

A popular toolset for data professionals is Tableau. Tools like these are
called data visualization software. At some companies, there are employees
whose entire job involves taking hard to read data and presenting it in a way
that is easy to visualize.
Software like tableau is very commonly used by businesses that rely on data
to make decisions. Tableau is useful because its relatively easy to use, and
data can be viewed in real-time through its platform. You can customize a
dashboard of tools for creating reports and charts with your data. It also
gives you the ability to share your results with other people from your
company. Tableau can be used to create graphs and scatterplots from that
data that you have analyzed in your programming language.