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Data Analysis with Python

  • Course Code: Data Analysis /BI - Data Analysis with Python
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Data Analysis with Python skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You’ll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You’ll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis – pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You’ll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. 

Working in a hands-on learning environment, led by our Data Analysis with Python expert instructor, students will learn about and explore: 

  • Bridge your data analysis with the power of programming, complex algorithms, and AI 
  • Use Python and its extensive libraries to power your way to new levels of data insight 
  • Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series 
  • Explore this modern approach across with key industry case studies and hands-on projects 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • A new toolset that has been carefully crafted to meet for your data analysis challenges 
  • Full and detailed case studies of the toolset across several of today’s key industry contexts 
  • Become super productive with a new toolset across Python and Jupyter Notebook 
  • Look into the future of data science and which directions to develop your skills next 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  

Course Agenda / Topics 

  1. Programming and Data Science – A New Toolset 
  • Programming and Data Science – A New Toolset 
  • What is data science 
  • Is data science here to stay? 
  • Why is data science on the rise? 
  • What does that have to do with developers? 
  • Putting these concepts into practice 
  • Deep diving into a concrete example 
  • Data pipeline blueprint 
  • What kind of skills are required to become a data scientist? 
  • IBM Watson DeepQA 
  • Back to our sentiment analysis of Twitter hashtags project 
  • Lessons learned from building our first enterprise-ready data pipeline 
  • Data science strategy 
  • Jupyter Notebooks at the center of our strategy 
  1. Python and Jupyter Notebooks to Power your Data Analysis 
  • Python and Jupyter Notebooks to Power your Data Analysis 
  • Why choose Python? 
  • Introducing PixieDust 
  • SampleData – a simple API for loading data 
  • Wrangling data with pixiedust_rosie 
  • Display – a simple interactive API for data visualization 
  • Filtering 
  • Bridging the gap between developers and data scientists with PixieApps 
  • Architecture for operationalizing data science analytics 
  1. Accelerate your Data Analysis with Python Libraries 
  • Accelerate your Data Analysis with Python Libraries 
  • Anatomy of a PixieApp 
  1. Publish your Data Analysis to the Web – the PixieApp Tool 
  • Publish your Data Analysis to the Web – the PixieApp Tool 
  • Overview of Kubernetes 
  • Installing and configuring the PixieGateway server 
  1. Python and PixieDust Best Practices and Advanced Concepts 
  • Python and PixieDust Best Practices and Advanced Concepts 
  • Use @captureOutput decorator to integrate the output of third-party Python libraries 
  • Increase modularity and code reuse 
  • Run Node.js inside a Python Notebook 
  1. Analytics Study: AI and Image Recognition with TensorFlow 
  • Analytics Study: AI and Image Recognition with TensorFlow 
  • What is machine learning? 
  • What is deep learning? 
  • Getting started with TensorFlow 
  • Image recognition sample application 
  1. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis 
  • Analytics Study: NLP and Big Data with Twitter Sentiment Analysis 
  • Getting started with Apache Spark 
  • Twitter sentiment analysis application 
  • Part 1 – Acquiring the data with Spark Structured Streaming 
  • Part 2 – Enriching the data with sentiment and most relevant extracted entity 
  • Part 3 – Creating a real-time dashboard PixieApp 
  • Part 4 – Adding scalability with Apache Kafka and IBM Streams Designer 
  1. Analytics Study: Prediction – Financial Time Series Analysis and Forecasting 
  • Analytics Study: Prediction – Financial Time Series Analysis and Forecasting 
  • Getting started with NumPy 
  • Statistical exploration of time series 
  • Putting it all together with the StockExplorer PixieApp 
  • Time series forecasting using the ARIMA model 
  1. Analytics Study: Graph Algorithms – US Domestic Flight Data Analysis 
  • Analytics Study: Graph Algorithms – US Domestic Flight Data Analysis 
  • Introduction to graphs 
  • Getting started with the networkx graph library 
  • Part 1 – Loading the US domestic flight data into a graph 
  • Part 2 – Creating the USFlightsAnalysis PixieApp 
  • Part 3 – Adding data exploration to the USFlightsAnalysis PixieApp 
  • Part 4 – Creating an ARIMA model for predicting flight delays 
  1. The Future of Data Analysis and Where to Develop your Skills 
  • The Future of Data Analysis and Where to Develop your Skills 
  • Forward thinking – what to expect for AI and data science 
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