Data Analytics & Visualization Using Python (with Project)

Crash Course
What you'll learn:
Develop proficiency in Python programming for data analysis.
Acquire the ability to estimate project timelines.
Gain proficiency in NumPy for advanced numerical operations.
Conduct Exploratory Data Analysis (EDA) for insights.
Requirements:
Prior programming experience required.
Description:
Welcome to the Data Analysis course. a fast-paced and intensive crash course tailored for individuals with some prior programming experience. This course is specifically designed for learners looking to quickly refresh their Python skills and delve into the world of data analysis and Visualization, making it an ideal choice for those seeking rapid revision for exams or a swift recap of essential concepts.Module 1: Introduction to Business and Data1.1 Overview: A rapid introduction to the role of data in business and a concise overview of the course curriculum.1.2 Key Concepts: Swiftly grasp key concepts in business data analysis, setting the stage for the rest of the course.1.3 Python Introduction: Quickly refresh your Python knowledge, emphasizing key aspects relevant to business data analysis.Module 2: Python Basics and Jupyter Notebooks2.1.1-2.1.3 Python Programming Basics: A condensed exploration of Python fundamentals, covering syntax, data types, and basic programming concepts.2.2 Understanding Jupyter Notebook: Rapidly familiarize yourself with Jupyter Notebooks for interactive and collaborative data analysis.Module 3: Operators and Conditionals3.1 Operators in Python: Swiftly navigate through the various operators for efficient data manipulation.3.2 Conditionals in Python: Quickly review the use of conditional statements to control program flow.Module 4: Loops and Functions4.1 Loops in Python: Efficiently revisit the use of loops for iterative processes.4.2 Functions in Python: Rapidly refresh your understanding of creating and using functions for modular code.Module 5: Object-Oriented Programming (OOP) and NumPy5.1 Object-Oriented Programming: A brisk exploration of OOP principles for structured code.5.2.1-5.2.2 Arrays in Python and Numpy Overview: Swiftly introduce NumPy for handling arrays and numerical operations.Module 6: pandas Library and Data Manipulation6.1-6.3 Introduction to pandas, pandas Series, and Working with DataFrames: Quickly grasp the essentials of pandas for efficient data manipulation.Module 7: Working with Files and Data Importing7.1-7.3 File Handling, Structured vs. Semi-Structured Data, and Importing JSON and Excel files: Swiftly understand file handling, data structures, and data importing techniques.Module 8: Data Cleaning and Preprocessing8.1-8.2 Data Cleaning Techniques, pandas Methods, and Operations: Efficiently review strategies for cleaning and preprocessing data using pandas.Module 9: Exploratory Data Analysis (EDA)9.1-9.2 Exploratory Data Analysis (EDA) and EDA Practical Session: Quickly revisit techniques for exploring and visualizing data to gain insights.Module 10: Advanced Topics10.1-10.2 Data Gathering Techniques and Practical Exercises with Real-world APIs: Swiftly explore advanced data collection methods and apply them through practical exercises.10.3 Linear Algebra and NumPy: A quick revision of linear algebra concepts and their application using NumPy.Module 11: Capstone Project11. Project - Student Placement Prediction: Apply your refreshed skills to a real-world problem with a focus on quick application and practical understanding.Course Highlights:Ideal for learners with prior programming experience, immediate beginners can also enroll.A crash course designed for quick understanding and application.Perfect for rapid revision and exam preparation.Intensive, hands-on learning with a focus on practical scenarios.Enrol now for an accelerated journey into Python for Business Data Analysis, where swift learning meets practical application!
Who this course is for:
Students Interested in Python for data analysis.
Students Seeking a crash course for quick revision.
Preparing for exams with a focus on data analysis.
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