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LEARNING PATH: Python: Complete Data Analysis With Python
Rating: 3.9 out of 5(7 ratings)
51 students

LEARNING PATH: Python: Complete Data Analysis With Python

Fast-track your data analysis journey with Python using its powerful libraries
Created byPackt Publishing
Last updated 11/2017
English

What you'll 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

Course content

2 sections • 33 lectures • 3h 41m total length

  • The Course Overview8:48

    This video provides an overview of the entire course.

  • Python Core Concepts and Data Types11:18

    This video introduces some basic Python syntax and concepts. The aim of this video is to provide you with a brief overview of the most important Python constructs.

  • Understanding Iterables5:54

    This video introduces some details on iterables such as sequences and generators. The aim of this video is to provide you with the tools to iterate over data, so youcan choose the most suitable Python construct and know how to efficiently perform some basic operations over data.

  • List Comprehensions6:05

    This video discusses functional programming concepts such as list comprehensions. This syntax providesyou with an efficient and convenient way to iterate over sequences, building complex statements with little code.

  • Dates and Times5:42

    This video discusses how to deal with dates and times. Dates are often represented in many different ways, and Python offers a unified abstract way to deal with aspects such astime zone, daylight saving time, and operations between dates.

  • Accessing Raw Data7:03

    This video discusses how to access data from local files. The aim of the video is to understand the most common file formats used to exchange data, and how Python makes it easy to access these formats.

  • Creating NumPy Arrays8:20

    This video introduces the NumPy library, the multidimensional array data structure, and the operations to create such arrays. The aim is to provide you with the basic tool to create array for efficient computation.

  • Basic Stats and Linear Algebra7:18

    This video continues the discussion on NumPy, introducing the core arithmetic operations on NumPy arrays, how to calculate statistics on array, and how to perform linear algebra operations on matrices.

  • Reshaping, Indexing, and Slicing6:38

    This video continues the discussion on NumPy, showcasing some advanced operations to change the shape of an array or to access the array efficiently using indexing and slicing.

  • Getting Started with Pandas9:23

    This video introduces the Pandas library and its core data structures, Series, and Data Frame. The aim of this video is to provide you with the basic information to use these structures for many data analysis tasks. 

  • Essential Operations with Data Frames11:45

    This video discusses some of the fundamental operations with Pandas objects. The aim of this video is to provide you with some building blocks to produce data analysis pipelines.

  • Summary Statistics from a Data Frame9:49

    This video discusses how to extract and show summary statistics from a data frame. The aim of this video is to enable you to start with the first steps of exploratory data analysis.

  • Data Aggregation Over a Data Frame8:58

    This video discusses the use of the group-by function for data aggregation over a data frame. The aim of this video is to enable you to perform powerful data aggregations and extract meaningful information from their data.

  • Exercise – Titanic Survivor Analysis13:58

    This video introduces the Titanic disaster data set and discusses some exploratory analysis on the data. The aim of this video is to recap what you learned so far on a real data set, as well as show-case some data visualization examples.

  • Predicting Titanic survival – A Supervised Learning Problem10:16

    This video introduces some concepts of machine learning and in particular, of supervised learning (classification), including how to evaluate a classification system. The aim of this video is to learn how to frame a prediction problem using the Titanic disaster data set.

  • Performing Supervised Learning with Scikit-Learn13:38

    This video applies the concepts of supervised learning discussed in the previous video and puts everything in practice using scikit-learn. The aim of this video is to have an end-to-end working example of the machine learning application.

  • Data analysis with python:

Requirements

  • Knowledge on Python is assumed

Description

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.

Who this course is for:

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

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