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Analyze and understand your data with the power and simplicity of Python.

Course Description

Python features numerous numerical and mathematical toolkits such as: Numpy, Scipy, Scikit learn and SciKit, all used for data analysis and machine learning. With the aid of all of these, Python has become the language of choice for data scientists for data analysis, visualization, and machine learning.
This course aims to teach Python developers how to perform data analysis with the language by taking advantage of the core data science libraries in the Python ecosystem. The learning objective for viewers is to understand how to locate, manipulate, and analyse data with Python, with the ability to analyse large and small sets of data using libraries such as Numpy, pandas, IPython and SciPy.
This is a two-part series. The first series is focused on getting and manipulation sizeable amounts of data using modern techniques. The second series is focused on advanced analysis of the data to include modern machine learning techniques.

Learning Outcomes

  • Advanced and recommend software engineering development practices.
  • How to scrape the twitter stream to collect real-time data
  • Smart storing of data using advanced abstractions and Object-Oriented programming
  • Easy and practical data manipulation techniques for dealing with large volumes of data
  • Natural Language Processing tools, specially designed for working with sentences and other forms of textual data
  • Predictive methods that can forecast and predict future trends based on current data
  • Data analytics techniques to tease out unseen data relationships
  • Dashboard application development to help share and monitor your progress/analysis


 Prior knowledge of Python Data Analysis is needed.

Who is this course intended for?

This course appeals to Python developers who want to be capable of performing core data analysis tasks with Python’s libraries and tools, including data retrieval, cleaning, manipulation, visualization and storage. Those who want to handle large sets of structured and unstructured data, and discovering and delivering insight with various forms of analysis will find this course spot-on!

Your Instructor

Packt Publishing

Packt has been committed to developer learning since 2004. A lot has changed in software since then – but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content – more than 4000 books and video courses -Packt’s mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages to cutting-edge data analytics, and DevOps, Packt takes software professionals in every field to what’s important to them now.

From skills that will help you to develop and future-proof your career to immediate solutions to everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Course Curriculum

Getting Started with Python
The Course Overview 00:00:00
Getting started with Python 00:00:00
Getting Data using the Twitter API 00:00:00
Collecting and Storing Tweets 00:00:00
Database Design 00:00:00
Pandas and Databases 00:00:00
Numerical Computing with Pandas
Panda Series, Dataframes, and Columnar Operations 00:00:00
Grouping Operations and Working with Date Columns 00:00:00
Merging Operations and Exporting data to JSON/CSV 00:00:00
Scientific Computing with NumPy/SciPy
Array Features, Bucketting Arrays and Histogram Functions 00:00:00
Simple Aggregations 00:00:00
Linear Algebra 00:00:00
Presenting Stories via Simple Visualizations
Introducting PyQT and MatplotLib 00:00:00
Creating Charts 00:00:00
Simple XY Plots with Axis Scales 00:00:00
Using the NLTK Package
Introduction to the NTLK Package 00:00:00
Bag of Words 00:00:00
Classification of Words 00:00:00
Stemming 00:00:00
Simple Sentiment Analysis 00:00:00
Getting Insights from Tweets
Grouping By Dimensions and Classification of Data Types 00:00:00
Trend Analysis and Deriving New Metrics 00:00:00
Correlation Analysis 00:00:00
Course Summary 00:00:00

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