Python for Data Analysis Training Course

Last updated

Course Code



28 hours (usually 4 days including breaks)


  • Programming experience in any language


  • Developers
  • Beginning data scientists
  • Business analysis with technical skills


Your company wishes to gain insights from the data it has collected over the years. Yet you face endless options of tools and approaches for doing so. There are user friendly UI tools such PowerBi, backend command line tools like SQL, and versatile "data wrangling" programming languages like Python and R.

This course elects the most powerful and flexible tool for the job: Python.

Python is a readable programming language with a relatively low learning curve. It offers something that most other solutions cannot: flexibility and adaptability. After the initial investment in learning Python, you can use it to manipulate and visualize your data in ways that would be difficult and time-consuming in other tools.

This instructor-led, live training (online or onsite) is aimed at persons who wish to learn just enough Python to begin crunching numbers from sales data, traffic analytics, customer interactions, etc..

The training is designed to enhance learning, retention, and hands-on practice. By combining different learning approaches such as online learning, live classroom interaction, peer learning and pair programming, this training aims to maximize the effect of each lesson. It reinforces learned concepts through a mix of pre-learning, interactive learning, and post-learning, all while including a social element that makes the course fun and engaging. The training is divided into three parts:

  • Pre-course
  • In-course
  • Post-course

This training is different from pure online learning in that it emphasizes live interaction with the trainer. It is also different from a traditional classroom in that it includes offline learning and self-paced practice.

A unique aspect of NobleProg trainings is its "pair programming" approach to learning. Pair programming allows two or more person's to collaboratively solve challenging and thought-provoking problems on the same machine. This approach has proven to be a powerful and efficient way to teach and learn. NobleProg makes this possible through its learning platform, DaDesktop. DaDesktop provides a collaborative space for participants and instructors to share and interact with each others' machines in real-time. Video conferencing and the ability to record lessons are just some of the features included in DaDesktop as part of this training.

By the end of this training, participants will be able to:

  • Install and configure the necessary software, libraries and development environment to begin writing just enough Python code for data analysis.
  • Analyze data from sources such as Excel, CSV, JSON files and databases.
  • Clean data to improve its usefulness before analyzing it.
  • Perform simple statistical analysis.
  • Generate reports that present the desired data in just the right format, from straight numbers to charts, to graphs and tables.
  • Gain valuable insight from data, including trends in performance, and problem areas to make better business decisions.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline


  • Overview of Python and its Powerful Ecosystem for Data Analysis

Getting Started

  • Setting up the development environment
  • Installing Python, Numpy, and Pandas
  • Installing Jupyter

Pythong Programming for Data Analysis

  • Overview of Python syntax
  • Writing and running Python code

Working with Data

  • Importing a dataset
  • Cleaning the data

The Python Data Frame

  • Understanding data frames
  • Manipulating data in a date frame

Gaining Insights from Data

  • Summarizing the data
  • Generating reports
  • Visualizing data

Saving Your Python Code

  • Saving your code in a version control repository
  • Allowing others to access your code

Improving Your Code

  • Testing your code and fixing the errors
  • Tightening your code using an iterative approach

Taking Your Code to Production

  • Uploading your code to a website
  • Automating the executing of your code

Python Programming Best Practices

Summary and Conclusion



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