Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.


Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers, analysts, and others with basic Python and developing experience

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others who want to learn a modern approach to data analysis using Python to harness the power of programming and AI across your data.
  • Skill-level: Foundation-level Data Analysis with Python skills for Intermediate skilled team members. This is not a basic class.
  • Developers: Basic (1-2 years’ experience)
  • Python: Basic (1-2 years’ experience)

About this course

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. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. 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 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Data Analysis with Python expert instructor, students will learn about and explore:
  • Bridging your data analysis with the power of programming, complex algorithms, and AI
  • Using Python and its extensive libraries to power your way to new levels of data insight
  • Working with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series
  • Exploring this modern approach across with key industry case studies and hands-on projects
  • 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 todays key industry contexts
  • Becoming super productive with a new toolset across Python and Jupyter Notebook
  • Looking into the future of data science and which directions to develop your skills next

Course breakdown / modules

  • 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

  • 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

  • Anatomy of a PixieApp

  • Overview of Kubernetes
  • Installing and configuring the PixieGateway server

  • Use @captureOutput decorator to integrate the output of third-party Python libraries
  • Increase modularity and code reuse
  • Run Node.js inside a Python Notebook

  • What is machine learning?
  • What is deep learning?
  • Getting started with TensorFlow
  • Image recognition sample application

  • 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

  • Getting started with NumPy
  • Statistical exploration of time series
  • Putting it all together with the StockExplorer PixieApp
  • Time series forecasting using the ARIMA model

  • 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

  • The Future of Data Analysis and Where to Develop your Skills
  • Forward thinking – what to expect for AI and data science