Data Science I: Essential Tools

Answer complicated questions, uncover greater insights and learn high-demand skills with Python and the power of data science. This course provides the foundational knowledge needed to move beyond the limitations of traditional spreadsheets to streamline and automate time-consuming tasks and encourage data-driven decision making.


The Essentials of Python for Data Science:

-Construct variables in Python and understand commonly used data structures
-Perform iterations in Python to easily apply a process over multiple values
-Encapsulate a process into a function to streamline and reduce repetition
-Utilize if/else statements to handle various cases and conditions
-Understand string data types and common operations applied to strings

Object-Oriented Programming (OOP) and NumPy and Pandas Python packages:

-Understand how OOP is implemented and used throughout Python and its packages
-Use NumPy ndarray data structures for fast, multidimensional numerical operations
-Utilize Pandas’ data structures and load CSV and Excel files as Pandas dataframes
-Combine tables across multiple spreadsheets into one dataframe
-Handle missing entries/data with Pandas, and utilize methods to replace missing values

Advanced Panda Techniques and Task Automation

-Use grouping operations in Pandas to create groups within a dataframe
-Use NumPy functions in Pandas and understand the connection between them
-Utilize manipulation and analysis tools in Pandas for times series
-Up and down sample time series to change frequency
-Understand time series transformations
-Visualize data analysis results using
-Matplotlib to create line and scatter plots, histograms and bar charts
-Seaborn package to generate statistically focused plots

Who should attend? 

Analysts and related positions who have reached the limits of spreadsheets and want to expand their understanding of data science with hands-on experience, and those interested in a career in data science

Key Concepts Covered

Python and relevant packages, data analysis and manipulation, automation of tasks and programs

Prerequisites

To achieve the greatest benefit from this course, students should understand: 

• Basic boolean algebra, such as concepts of “or” and “and”
• Conditional statements
  n Basic statistical definitions (mean, median, mode)
• Basic linear algebra (metrics, arrays)
• Common file formats like Comma Separated Values (CSV)