Data Science Overview with Python

An outline of this training course

This course provides a comprehensive introduction to data preparation, data wrangling, and data science modeling using Python. Students will learn practical skills to extract, clean, and transform data for analysis and modeling.

Covering the data science process, the course emphasizes data quality assessment and improvement. Students will master data manipulation techniques using Python libraries.

In the data wrangling section, students will handle missing values, outliers, and perform feature engineering.

The course's second part focuses on data science modeling, covering popular algorithms like regression, classification, and clustering. Students will train and evaluate models using scikit-learn and learn best practices for evaluation, hyperparameter tuning, and selection.

By course completion, students will understand the data science pipeline and have the necessary tools and techniques to extract insights from raw data.

Whether new to data science or seeking skill enhancement, this course offers a solid foundation in data preparation, wrangling, and modeling with Python.

What are needed to take this course 

This course is designed to be accessible to individuals with a background in programming concepts and Python programming language and a basic understanding of statistics. Prior experience with Python will be beneficial but not mandatory.

Who is the course for

This course is ideal for analysts looking to acquire essential data science skills using Python.

Details of what you will learn during this course

By the end of this course, you will:

• Master data cleaning techniques, including missing value imputation, outlier detection and cleaning, and data normalization.
  
• Apply exploratory data analysis techniques, feature selection methods, and variable reduction.
  
• Utilize exploratory data analysis techniques for data understanding and insights.
  
• Build regression and classification models for predictive data modeling.
  
• Evaluate and interpret results of data models for decision-making.
  

What you get with the course

• A 1+hour self-paced video training
  

Program Level

Intermediate

Field(s) of Study

Computer Software & App

Instruction Delivery Method

QAS Self-study

***This course was published in June 2023

Enterprise DNA is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org