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Python for Data Science
Enroll in our Python course to master skills for data science. Use real-world situations, data reports, and more in this Python class.
How to Become a Data Scientist
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written by Pierian Training founder Jose Portilla!
What You’ll Learn
- Learn how to use data science with Python.
- Create data pipeline workflows to analyze, visualize, and gain insights from data.
- Build a portfolio of data science projects with real world data.
- Analyze your own data sets and gain insights through data science.
- Master critical data science skills.
- Replicate real-world situations and data reports.
- Learn NumPy for numerical processing with Python.
- Conduct feature engineering on real world case studies.
- Learn Pandas for data manipulation with Python.
- Learn Matplotlib to create fully customized data visualizations with Python.
- Learn Seaborn to create beautiful statistical plots with Python.
- Construct a modern portfolio of data science resume projects.
- Get set-up quickly with the Anaconda data science stack environment.
Top Companies Offer Our Courses
Course Content
You’ll learn everything you need to know for the full data science tech stack required to work at the world’s top companies. This Python for Data Science course takes a structured approach that will guide you through understanding not just how to use data science, but why we use them by providing a balance between practical real world case studies and mathematical theory behind the data science algorithms.
Introduction
- What is data science?
- A multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data.
- Data science is used in a wide variety of industries, including healthcare, finance, retail, and marketing.
- Why Python for data science?
- Python is a powerful and versatile programming language that is well-suited for data science.
- Python is easy to learn and use, and it has a large and active community of developers.
- Python has a wide range of libraries and tools that are specifically designed for data science.
- The Python programming language
- Variables, data types, operators, control flow, functions, and modules.
- Jupyter Notebooks
- A web-based interactive environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text.
Data I/O
- Importing data from different sources
- CSV files, Excel files, JSON files, SQL databases, and web APIs.
- Exporting data to different formats
- CSV files, Excel files, JSON files, and SQL databases.
- Working with Excel and CSV files
- Reading and writing data from Excel and CSV files.
- Working with SQL databases
- Connecting to SQL databases, querying data, and updating data.
Numpy
- Numpy arrays
- Multi-dimensional arrays that are used to store and manipulate data.
- Mathematical operations on Numpy arrays
- Basic mathematical operations, such as addition, subtraction, multiplication, and division.
- Broadcasting
- A technique for performing mathematical operations on arrays of different sizes.
- Random number generation
- Generating random numbers for use in data science tasks.
Pandas
- Pandas DataFrames
- Tabular data structures that are used to store and manipulate data.
- Selecting and filtering data
- Selecting and filtering data from DataFrames.
- Grouping and aggregating data
- Grouping data by different categories and aggregating the data.
- Handling missing data
- Handling missing data in DataFrames.
Data visualization
- Matplotlib
- A Python library for creating static, animated, and interactive visualizations.
- Creating basic plots
- Creating basic plots, such as line charts, bar charts, and pie charts.
- Customizing plots
- Customizing the appearance of plots.
- Saving plots
- Saving plots to different file formats.
Seaborn
-
- Seaborn
- A Python library for creating statistical plots.
- Statistical plots
- Creating statistical plots, such as distribution plots, correlation plots, and regression plots.
- Customizing Seaborn plots
- Customizing the appearance of Seaborn plots.
- Seaborn
Intermediate Python developers looking to use Python to explore and visualize large or complex data sets. Check out our Introduction to Python course if you’re new to Python.
Course Description
Data Science is the critical skill of the modern workforce. Data science skills allow for the extraction of key insights and relevant knowledge from large datasets. This data science training course is for Python developers who want to learn how to use the key libraries in the Python Data Science ecosystem. This course covers NumPy for numerical data processing, Pandas for data analysis, and Matplotlib and Seaborn for quick visualizations and statistical reporting. The training is all conducted in a modern Jupyter Notebook format, allowing students to read and write code directly within our training notebooks.
Instructor-Led
Learn directly from our expert instructors via live webinar. Also includes the full video-on-demand course.
On-Demand
Get access to this self-paced Python for Data Science training course plus the entire Pierian on-demand Data Science Training Library with a Data Science Hero membership.
Starting at
$99
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Train your whole team
Empower your employees with the latest technology skills to support professional development at your organization.
Instructor-led, on-demand, or hybrid cohort training approaches motivate employees and encourage team building while supporting the advancement of technical skills at your organization.