Certainly! To ensure a productive learning experience in the Data Science and Big Data Analytics v2 course, students should meet the following minimum prerequisites:
- Basic understanding of statistics and mathematical concepts
- Familiarity with at least one programming language (Python, R, or similar)
- Fundamental knowledge of database concepts and SQL
- Basic proficiency with a computer and the Windows or Linux operating systems
- Willingness to learn and apply new analytical techniques and tools
These prerequisites are intended to provide a foundation upon which the course content can build. The course is designed to be accessible, with the assumption that students are motivated and have a base level of technical acumen. With these prerequisites, students will be better equipped to grasp the principles of Big Data analytics and the role of a Data Scientist.
Target Audience for Data Science and Big Data Analytics v2
Data Science and Big Data Analytics v2 is a comprehensive course designed to equip learners with the skills to analyze large datasets.
- Data Scientists and Analysts
- Big Data Engineers
- IT Professionals seeking data analytics expertise
- Business Analysts looking to understand big data analytics
- Statisticians transitioning to data science roles
- Software Engineers aiming to master data analytics
- Data Visualization Specialists
- Data-driven Product Managers
- Analytics Consultants
- Professionals in roles involving data-driven decision-making
- Graduate students in computer science, statistics, or related fields
- Research Scientists interested in big data analysis
- Database Professionals expanding their roles to include big data
- Machine Learning Engineers
Learning Objectives - What you will Learn in this Data Science and Big Data Analytics v2?
Introduction to the Course's Learning Outcomes:
Embark on a journey through the essentials of Data Science and Big Data Analytics, acquiring critical skills to extract actionable knowledge from complex data.
Learning Objectives and Outcomes:
- Understand the defining characteristics and significance of Big Data within the modern business landscape.
- Recognize the business motivations driving the adoption of Big Data analytics and the impact of data science.
- Identify the crucial role and competencies of a Data Scientist within an organization.
- Comprehend the data analytics lifecycle, including the purpose and sequence of distinct phases.
- Gain knowledge of the discovery and data preparation phases, including the key activities and roles involved.
- Become proficient in model planning and model building, understanding their respective activities and roles.
- Utilize basic R commands to perform preliminary data exploration and analysis.
- Master core statistical measures, visualizations, and hypothesis testing for effective data interpretation.
- Learn advanced analytics techniques such as k-means clustering, association rules, and various regression and classification methods.
- Explore Big Data technologies and tools, including MapReduce, Apache Hadoop, its ecosystem, and advanced SQL methods.
- Implement best practices for operationalizing analytics projects and develop skills in data visualization and presentation for varied audiences.
Intermediate
