by AI Publishing (Author)
Statistical methods are an integral part of data science. Hence, a
formal training in statistics is indispensable for data scientists.
If
you are keen on getting your foot into the lucrative data science and
analysis universe, you need to have a fundamental understanding of
statistical analysis. Besides, Python is a versatile programming
language you need to master to become a career data scientist.
As a
data scientist, you will identify, clean, explore, analyze, and
interpret trends or possible patterns in complex data sets. The
explosive growth of Big Data means you have to manage enormous amounts
of data, clean it, manipulate it, and process it. Only then the most
relevant data can be used.
Python is a natural data science tool
as it has an assortment of useful libraries, such as Pandas, NumPy,
SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And
Python’s focus on simplicity makes it relatively easy for you to learn.
Importantly, the ease of performing repetitive tasks saves you precious
time. Long story short—Python is simply a high-priority data science
tool.
How Is This Book Different?
The book focuses equally on the theoretical as well as practical
aspects of data science. You will learn how to implement elementary data
science tools and algorithms from scratch. The book contains an
in-depth theoretical and analytical explanation of all data science
concepts and also includes dozens of hands-on, real-life projects that
will help you understand the concepts better.
The ready-to-access
Python codes at various places right through the book are aimed at
shortening your learning curve. The main goal is to present you with the
concepts, the insights, the inspiration, and the right tools needed to
dive into coding and analyzing data in Python.
The main benefit of
purchasing this book is you get quick access to all the extra content
provided with this book—Python codes, exercises, references, and PDFs—on
the publisher’s website, at no extra price. You get to experiment with
the practical aspects of Data Science right from page 1.
Beginners
in Python and statistics will find this book extremely informative,
practical, and helpful. Even if you aren’t new to Python and data
science, you’ll find the hands-on projects in this book immensely
helpful.
The topics covered include:
- Introduction to Statistics
- Getting Familiar with Python
- Data Exploration and Data Analysis
- Pandas, Matplotlib, and Seaborn for Statistical Visualization
- Exploring Two or More Variables and Categorical Data
- Statistical Tests and ANOVA
- Confidence Interval
- Regression Analysis
- Classification Analysis
