1 - Setting Up a Python Data Science Environment
Select Python Data Science ToolsSet Up an Environment Using Jupyter Notebook
2 - Managing and Analyzing Data with NumPy
Create NumPy ArraysLoad and Save NumPy DataAnalyze Data in NumPy Arrays
3 - Transforming Data with NumPy
Manipulate Data in NumPy ArraysModify Data in NumPy Arrays
4 - Managing and Analyzing Data with pandas
Create Series and DataFramesLoad and Save pandas DataAnalyze Data in DataFramesSlice and Filter Data in DataFrames
5 - Transforming and Visualizing Data with pandas
Manipulate Data in DataFramesModify Data in DataFramesPlot DataFrame Data
6 - Visualizing Data with Matplotlib and Seaborn
Create and Save Simple Line PlotsCreate SubplotsCreate Common Types of PlotsFormat PlotsStreamline Plotting with Seaborn
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
This course is designed for students who wish to expand their ability to extract knowledge from business data. The target student for this course understands the principles and benefits of data science and has used basic data-driven tools like Microsoft® Excel ® and Structured Query Language (SQL) queries, but wants to take the next steps into more advanced applications of data science. So, the target student may be a programmer or data analyst looking to solve business problems using powerful programming libraries that go beyond the limitations of prepackaged GUI tools or database queries; libraries that give the data scientist more fine-tuned control over the analysis, manipulation, and presentation of data.A typical student in this course should have several years of experience with computing technology, along with a proficiency in programming
To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including but not limited to: data engineering, data analysis, data storage, data visualization, and statistics.