Python for Data Analytics and Machine Learning (Oracle) Course Overview

To ensure that students can successfully grasp the concepts covered in the Python for Data Analytics and Machine Learning (Oracle) course, the following are the minimum required prerequisites:

• Basic understanding of programming concepts such as variables, loops, and conditional statements.
• Familiarity with fundamental concepts of mathematics and statistics, including basic algebra.
• Knowledge of any programming language is helpful but not mandatory, as Python will be taught from the basics.
• Comfort with using a computer, managing files, and navigating the operating system.
• Eagerness to learn and willingness to work with new tools and libraries specific to Python.
• Basic comprehension of database concepts and SQL can be beneficial for modules involving data access, but it is not a strict requirement.

Please note that while having prior experience in programming and data analysis can be advantageous, the course is designed to accommodate beginners with step-by-step instructions and practical exercises to build a strong foundation in Python for data analytics and machine learning.

Target Audience for Python for Data Analytics and Machine Learning (Oracle)

Learn Python for Data Analytics and Machine Learning with Koenig Solutions. Ideal for IT professionals looking to advance their data skills.

• Data Analysts
• Machine Learning Engineers
• Data Scientists
• Software Developers interested in data-driven technologies
• IT Professionals seeking to transition into data roles
• Business Intelligence Professionals
• Research Scientists and Academicians in data-intensive fields
• Data Engineers
• Statisticians looking to utilize Python for data analysis
• Database Administrators wanting to leverage Python for data manipulation
• Graduates in Computer Science, Engineering, Statistics, or related fields
• Technology Consultants who advise on data analytics and machine learning strategies

Learning Objectives - What you will Learn in this Python for Data Analytics and Machine Learning (Oracle)?

1. Introduction: This course equips students with the foundational skills to apply Python in data analytics and machine learning, covering data structures, control flows, and key libraries.

2. Learning Objectives and Outcomes:

• Understand the significance of Python in data analytics and machine learning and its ecosystem.
• Gain proficiency in using Python's interpreter and setting up a development environment.
• Master fundamental Python data types and their practical applications in data handling.
• Develop the ability to implement control structures for managing the flow of Python programs.
• Learn to create and use functions in Python for modular and reusable code.
• Manipulate lists in Python for data collection and processing tasks.
• Utilize tuples in Python for creating immutable sequences of elements.
• Employ sets in Python to perform operations with unordered collections of unique elements.
• Manage key-value pairs with dictionaries for structured data storage and retrieval.
• Apply regular expressions for pattern matching and text manipulation in data analysis.
• Get hands-on experience with the re module to perform advanced string operations.
• Work with NumPy for numerical computing and handling large multi-dimensional arrays.
• Use Pandas for data manipulation and analysis, handling structured data with ease.
• Learn to access various data sources, including databases, using Python's connectivity capabilities.
• Create visualizations with Matplotlib to interpret data insights and communicate results effectively.