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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Python developers with at least one year of experience seeking to use Python packages for data analysis

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts, or others with Python skills who want to get to grips with the most popular Python packages that make Data Analysis possible.
  • Skill-level: Foundation-level Data Analysis with NumPy and Pandas skills for Intermediate skilled team members. This is not a basic class.
  • Developers: Basic to Intermediate (1-5 years’ experience)
  • Python: Basic (1-2 years’ experience)

About this course

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning. Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this course, you will have learned to index and group your data for sophisticated data analysis and manipulation.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Data Analysis with NumPy and Pandas expert instructor, students will learn about and explore:
  • The tools you need to become a data analyst
  • Discovering practical examples to help you grasp data processing concepts
  • Walking through hierarchical indexing and grouping for data analysis
  • Understanding how to install and manage Anaconda
  • Reading, sorting, and mapping data using NumPy and pandas
  • How to create and slice data arrays using NumPy
  • How to subset your DataFrames using pandas
  • Handling missing data in a pandas DataFrame
  • Exploring hierarchical indexing and plotting with pandas

Course breakdown / modules

  • What is Anaconda?
  • Installing Anaconda
  • Exploring Jupyter Notebooks
  • Exploring alternatives to Jupyter
  • Package management with Conda
  • Setting up a database

  • NumPy arrays
  • Special numeric values
  • Creating NumPy arrays

  • Selecting elements explicitly
  • Advanced indexing
  • Expanding arrays
  • Arithmetic and linear algebra with arrays
  • Linear algebra
  • Employing array methods and functions

  • What does pandas do?
  • Exploring series and DataFrame objects
  • Subsetting your data
  • Indexing methods

  • Arithmetic
  • Handling missing data in a pandas DataFrame

  • Index sorting
  • Hierarchical indexing
  • Plotting with pandas