Data Analytics & Visualization Using Python (with Project)
Get the necessary skills to perform efficient data analysis and visualization using Python
Lectures -28
Resources -1
Duration -6.5 hours
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Course 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 Data
1.1 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 Notebooks
2.1.1-2.1.3 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 Conditionals
3.1 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 Functions
4.1 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 NumPy
5.1 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 Manipulation
6.1-6.3 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 Importing
7.1-7.3 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 Preprocessing
8.1-8.2 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 Topics
10.1-10.2 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 Project
11. 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!
Goals
Enhance Python Proficiency: Quickly refresh and solidify foundational Python programming skills, tailored for data analysis applications in business contexts.
Master Data Analysis Tools: Gain proficiency in essential data analysis tools and libraries, including Jupyter Notebooks, NumPy, and pandas, to handle, manipulate, and analyze data efficiently.
Develop Data Handling Skills: Learn and apply techniques for importing, cleaning, and preprocessing various data types and structures, focusing on practical, real-world datasets.
Apply Exploratory Data Analysis (EDA): Conduct effective exploratory data analysis to uncover patterns, insights, and trends within datasets, utilizing visualization tools for better data comprehension.
Implement Advanced Data Techniques: Understand and implement advanced data gathering techniques and apply linear algebra concepts using NumPy for complex data analysis tasks.
Foster Problem-Solving Abilities: Enhance problem-solving skills through hands-on practice and real-world scenarios, culminating in a capstone project that applies learned concepts to a practical business problem.
Encourage Practical Application: Emphasize the rapid application of concepts through intensive, hands-on learning, ensuring learners can swiftly apply their knowledge to real-world data analysis challenges.
Prepare for Exams and Professional Work: Provide a comprehensive and accelerated revision of key data analysis concepts, making it ideal for exam preparation and quick upskilling for professional projects.
By the end of the course, learners will be equipped with the necessary skills to perform efficient data analysis and visualization, leveraging Python and its powerful libraries to drive business insights and decisions.
Prerequisites
Basic Programming Knowledge: Familiarity with basic programming concepts, preferably in Python, including variables, data types, loops, and functions.
Understanding of Fundamental Data Structures: Basic knowledge of common data structures such as lists, dictionaries, and arrays.
Mathematical Foundation: Basic understanding of algebra and statistics to grasp data analysis concepts effectively.
Problem-Solving Skills: Ability to approach and solve problems logically and systematically.
Familiarity with Computer Operations: Basic computer literacy, including file handling and navigating operating systems.
Analytical Thinking: Capacity to think analytically and critically when dealing with data and interpreting results.
While the course is designed for those with some prior programming experience, immediate beginners who are highly motivated and quick learners can also enroll and benefit from the intensive learning approach.
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Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Business and Data
3 Lectures
-
1.1 Overview 08:12 08:12
-
1.2 Key Concepts 15:11 15:11
-
1.3 Python Introduction 19:22 19:22
Python Basics and Jupyter Notebooks
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Operators and Conditionals
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Loops and function
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Object-Oriented Programming (OOP) and NumPy
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Pandas Library and Data Manipulation
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Working with Files and Data Importing
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Cleaning and Preprocessing
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Exploratory Data Analysis (EDA)
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Advanced Topics
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Capstone Project
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
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Selfcode Academy
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