Overview
Learning Path
Career Outlook
Course Overview:
This Data Science with Python certification course provides you an understanding of Data Analysis, Data Wrangling, Data Exploration, Data Visualization, Machine Learning, Python programming, Web scraping, and Natural Language Processing concepts. You will gain hands-on exposure to performing high-level mathematical computations and Data Analysis and Manipulation using data structures and tools.
Course Highlights
- 68 hours of blended learning
- 4 industry-based projects
- Interactive learning with Jupyter notebooks labs
- Lifetime access to self-paced learning
- Dedicated mentoring session from faculty of industry experts
Course Delivery Method:
Online Bootcamp – Online self-paced Video learning and live virtual classroom conducted by industry’s leading coaches. This course includes Simpliearn’s Integrated lab platform.
Prerequisites:
There are no prerequisites for taking the Data Science with Python certification course, but it is recommended that you begin with the courses – Python for Data Science, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science offered as free companions with this course.
Skills Covered:
- Data analysis
- Data wrangling
- Data exploration
- Data visualization
- Mathematical computing
- Web scraping
- Hypothesis building
- Python programming
- NumPy and SciPy package
- Natural language processing (NLP)
Learning Path
Who Will Benefit:
This Data Science with Python certification course is well-suited for Analytics professionals willing to work with Python; IT professionals; and anyone with a genuine interest in the Data Science field.
Key Learning Outcomes:
- Understand the essential concepts of Python programming such as datatypes, tuples, lists, dicts, basic operators, and functions
- Perform high-level mathematical computations using the NumPy and SciPy packages and their large library of mathematical functions
- Perform data analysis and manipulation using data structures and tools provided in the Pandas package
- Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
- Use the Scikit-Learn package for natural language processing and matplotlib library of Python for data visualization
Certification Criteria:
Online Classroom:
- Attend one complete batch of Data Science with Python training
- Submit at least one completed project
Online Self-Learning:
- Complete 85% of the course
- Submit at least one completed project.
Tools covered
