LEARNING PATH: Python: Complete Data Analysis With Python

LEARNING PATH: Python: Complete Data Analysis With Python, available at $19.99, has an average rating of 4.08, with 33 lectures, 2 quizzes, based on 6 reviews, and has 47 subscribers.

You will learn about Installation of the core Python tools required for data analysis Explore the different data types in Python UseNumPy for fast array computation Use Pandas for data analysis Frame a data science problem and use Python tools to solve it Read and write data in text format Master concepts involved in interacting with databases This course is ideal for individuals who are This Learning Path is targeted at aspiring data analysts who have some prior knowledge on Python. It is particularly useful for This Learning Path is targeted at aspiring data analysts who have some prior knowledge on Python.

Enroll now: LEARNING PATH: Python: Complete Data Analysis With Python

Summary

Title: LEARNING PATH: Python: Complete Data Analysis With Python

Price: $19.99

Average Rating: 4.08

Number of Lectures: 33

Number of Quizzes: 2

Number of Published Lectures: 33

Number of Published Quizzes: 2

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Installation of the core Python tools required for data analysis
Explore the different data types in Python
UseNumPy for fast array computation
Use Pandas for data analysis
Frame a data science problem and use Python tools to solve it
Read and write data in text format
Master concepts involved in interacting with databases

Who Should Attend

This Learning Path is targeted at aspiring data analysts who have some prior knowledge on Python.

Target Audiences

This Learning Path is targeted at aspiring data analysts who have some prior knowledge on Python.

Python is undoubtedly one of the most popular programming languages that’s being extensively used in the field of data science. There is a rapid increase in the number of data and so for the demand of experts who can analyze these big chunk of data. So if you have basic Python knowledge and want to explore powerful data analysis techniques, then go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

The highlights of this Learning Path are:

Get solutions to your common and not-so-common data science problems
Highly practical, real world examples that make data science your comfort zone
Understand why is Mastering python data analysis with Pandas really useful

Let’s take a look at your learning journey. You will be introduced to the field of data science using Python tools to manage and analyze data. You will learn some of the fundamental tools of the trade and apply them to real data problems. Along the way, the Learning Path discusses the use of Python stack for data analysis and scientific computing, and expands on concepts of data acquisition, data cleaning, data analysis, and machine learning. You will learn how to apply Pandas to important but simple financial tasks such as modeling portfolios, calculating optimal portfolios based upon risk, and much more.

On completion of this Learning Path, you will become an expert in analyzing your data efficiently using Python.�

Meet Your Expert:�

We have the best works of the following esteemed authors to ensure that your learning journey is smooth:

Marco Bonzanini is a data scientist based in London, United Kingdom. He holds a Ph.D. in information retrieval from the Queen Mary University of London. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems.

Prabhat Ranjan has extensive industry experience in Python, R, and machine learning. He has a passion for using Python, Pandas, and R for various new, real-time project scenarios. He is a passionate and experienced trainer when it comes to teaching concepts and advanced scenarios in Python, R, data science, and big data Hadoop.
His teaching experience and strong industry expertise make him the best in this arena.

Course Curriculum

Chapter 1: Data Analysis with Python

Lecture 1: The Course Overview

Lecture 2: Python Core Concepts and Data Types

Lecture 3: Understanding Iterables

Lecture 4: List Comprehensions

Lecture 5: Dates and Times

Lecture 6: Accessing Raw Data

Lecture 7: Creating NumPy Arrays

Lecture 8: Basic Stats and Linear Algebra

Lecture 9: Reshaping, Indexing, and Slicing

Lecture 10: Getting Started with Pandas

Lecture 11: Essential Operations with Data Frames

Lecture 12: Summary Statistics from a Data Frame

Lecture 13: Data Aggregation Over a Data Frame

Lecture 14: Exercise – Titanic Survivor Analysis

Lecture 15: Predicting Titanic survival – A Supervised Learning Problem

Lecture 16: Performing Supervised Learning with Scikit-Learn

Chapter 2: Mastering Python Data Analysis with Pandas

Lecture 1: The Course Overview

Lecture 2: Reading and Writing Data in Text Format

Lecture 3: XML and HTML Web Scrapping

Lecture 4: Interacting with Databases

Lecture 5: Binary Data Formats (Excel and HDF5)

Lecture 6: Data Wrangling/ Munging and Pandas Data Structures

Lecture 7: Combining and Merging Data Sets

Lecture 8: Reshaping, Pivoting, and Advanced Indexing Data Sets

Lecture 9: Data Transformation on Data Sets

Lecture 10: String Manipulations on Data Sets

Lecture 11: Working with Missing Data Sets

Lecture 12: Data Aggregation on Data Sets

Lecture 13: Group-Wise Operations on Data Sets

Lecture 14: Statistical Functions Example

Lecture 15: Windows Functions Example

Lecture 16: Applying Multiple and Different Functions to Dataframe Columns

Lecture 17: Exponentially Weighted Windows

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

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2 stars: 0 votes
3 stars: 1 votes
4 stars: 4 votes
5 stars: 1 votes

Frequently Asked Questions

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