LEARNING DATA SCIENCE
Available Dates & Locations
COURSE OVERVIEW
Learning Data Science course is designed to provide participants with a comprehensive understanding of the foundational concepts, techniques, and tools used in the field of data science. This course is tailored for individuals seeking a career transition to become data scientists, equipping them with the essential skills to analyze, interpret, and draw meaningful insights from data. Through a combination of theoretical knowledge and hands-on practical exercises, participants will develop a strong foundation to embark on a successful journey in the data science field.
COURSE OBJECTIVES
By completely attending this course, participants will be able to:
- Understand the core principles and methodologies of data science.
- Manipulate, clean, and preprocess various types of data for analysis.
- Apply statistical techniques to extract insights and patterns from data.
- Utilize popular programming languages and tools for data analysis, such as Python and Jupyter.
- Develop data visualizations to effectively communicate findings.
- Implement machine learning algorithms for predictive and classification tasks.
- Evaluate and interpret the results of data analysis and machine learning models.
TARGET COMPETENCIES
- Data Preprocessing Skills
- Exploratory Data Analysis
- Python Programming Basics
- Library Data Visualization
- Machine Learning Fundamentals
- Model Evaluation Expertise
This course is ideal for professionals from non-technical backgrounds who aspire to transition into data science roles. Individuals with a curiosity for data analysis and its applications. Career changers interested in leveraging data for decision-making.
The course will be delivered through a combination of Interactive Lectures: In-depth theoretical explanations of data science concepts and methodologies. Hands-on Labs: Practical exercises using real-world datasets to apply the learned concepts. Group Discussions: Collaborative sessions to encourage knowledge sharing and problem-solving. Case Studies: Analysis of real-life data science scenarios to bridge theory and practice. Quizzes and Assessments: Regular evaluations to gauge participant progress.
