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coll. - Data Science with Python

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Data Science with Python: summary, description and annotation

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Unleash the power of Python and its robust data science capabilities

About This Book
  • Unleash the power of Python 3 objects
  • Learn to use powerful Python libraries for effective data processing and analysis
  • Harness the power of Python to analyze data and create insightful predictive models
  • Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
Who This Book Is For

Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Pythons data science capabilities for doing real-world data analysis.

What You Will Learn
  • Install and setup Python
  • Implement objects in Python by creating classes and defining methods
  • Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis
  • Create effective visualizations for presenting your data using Matplotlib
  • Process and analyze data using the time series capabilities of pandas
  • Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
  • Apply data mining concepts to real-world problems
  • Compute on big data, including real-time data from the Internet
  • Explore how to use different machine learning models to ask different questions of your data
In Detail

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, youll have gained key skills and be ready for the material in the next module.

The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, its time that you dive into the field of data science. In the second module, youll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, well discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.

Style and approach

This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.

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Python: Real-World Data Science

Table of Contents
Python: Real-World Data Science

Python: Real-World Data Science

A course in four modules

Unleash the power of Python and its robust data science capabilities with your Course Guide Ankita Thakur

Learn to use powerful Python libraries for effective data processing and - photo 1

Learn to use powerful Python libraries for effective data processing and analysis

To contact your Course Guide

Email: <>

Meet Your Course Guide Hello and welcome to this Data Science with Python - photo 2
Meet Your Course Guide

Hello and welcome to this Data Science with Python course. You now have a clear pathway from learning Python core features right through to getting acquainted with the concepts and techniques of the data science fieldall using Python!

This course has been planned and created for you by me Ankita Thakur I am your - photo 3

This course has been planned and created for you by me Ankita Thakur I am your Course Guide, and I am here to help you have a great journey along the pathways of learning that I have planned for you.

I've developed and created this course for you and you'll be seeing me through the whole journey, offering you my thoughts and ideas behind what you're going to learn next and why I recommend each step. I'll provide tests and quizzes to help you reflect on your learning, and code challenges that will be pitched just right for you through the course.

If you have any questions along the way, you can reach out to me over e-mail or telephone and I'll make sure you get everything from the course that we've planned for you to start your career in the field of data science. Details of how to contact me are included on the first page of this course.

What's so cool about Data Science?

What is Data Science and why is there so much of buzz about this in the world? Is it of great importance? Well, the following sentence will answer all such questions:

"This hot new field promises to revolutionize industries from business to government, health care to academia."

-- The New York Times

The world is generating data at an increasing pace. Consumers, sensors, or scientific experiments emit data points every day. In finance, business, administration, and the natural or social sciences, working with data can make up a significant part of the job. Being able to efficiently work with small or large datasets has become a valuable skill. Also, we live in a world of connected things where tons of data is generated and it is humanly impossible to analyze all the incoming data and make decisions. Human decisions are increasingly replaced by decisions made by computers. Thanks to the field of Data Science !

Data science has penetrated deeply in our connected world and there is a growing demand in the market for people who not only understand data science algorithms thoroughly, but are also capable of programming these algorithms. A field that is at the intersection of many fields, including data mining, machine learning, and statistics, to name a few. This puts an immense burden on all levels of data scientists; from the one who is aspiring to become a data scientist and those who are currently practitioners in this field.

Treating these algorithms as a black box and using them in decision-making systems will lead to counterproductive results. With tons of algorithms and innumerable problems out there, it requires a good grasp of the underlying algorithms in order to choose the best one for any given problem.

Python as a programming language has evolved over the years and today, it is the number one choice for a data scientist. Python has become the most popular programming language for data science because it allows us to forget about the tedious parts of programming and offers us an environment where we can quickly jot down our ideas and put concepts directly into action. It has been used in industry for a long time, but it has been popular among researchers as well.

In contrast to more specialized applications and environments, Python is not only about data analysis. The list of industrial-strength libraries for many general computing tasks is long, which makes working with data in Python even more compelling. Whether your data lives inside SQL or NoSQL databases or is out there on the Web and must be crawled or scraped first, the Python community has already developed packages for many of those tasks.

Course Structure

Frankly speaking, it's a wise decision to know the nitty-gritty of Python as it's a trending language. I'm sure you'll gain lot of knowledge through this course and be able to implement all those in practice. However, I want to highlight that the road ahead may be bumpy on occasions, and some topics may be more challenging than others, but I hope that you will embrace this opportunity and focus on the reward. Remember that we are on this journey together, and throughout this course, we will add many powerful techniques to your arsenal that will help us solve even the toughest problems the data-driven way.

I've created this learning path for you that consist of four models. Each of these modules are a mini-course in their own way, and as you complete each one, you'll have gained key skills and be ready for the material in the next module.

So lets now look at the pathway these modules createbasically all the topics - photo 4

So let's now look at the pathway these modules createbasically all the topics that will be exploring in this learning journey.

Course Journey

We start the course with our very first module, Python Fundamentals , to help you get familiar with Python. Installing Python correctly is equal to half job done. This module starts with the installation of Python, IPython, and all the necessary packages. Then, we'll see the fundamentals of object-oriented programming because Python itself is an object-oriented programming language. Finally, we'll make friends with some of the core concepts of Pythonhow to get Python programming basics nailed down.

Then well move towards the analysis part The second module Data Analysis - photo 5

Then we'll move towards the analysis part. The second module, Data Analysis , will get you started with Python data analysis in a practical and example-driven way. You'll see how we can use Python libraries for effective data processing and analysis. So, if you want to to get started with basic data processing tasks or time series, then you can find lot of hands-on knowledge in the examples of this module.

The third module Data Mining is designed in a way that you have a good - photo 6

The third module, Data Mining , is designed in a way that you have a good understanding of the basics, some best practices to jump into solving problems with data mining, and some pointers on the next steps you can take. Now, you can harness the power of Python to analyze data and create insightful predictive models.

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