Published 11/2023
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.92 GB | Duration: 6h 45m
Python’s Essential Libraries: A Practical Guide to Data Analysis & Visualization Mastery
What you’ll learn
Mastery in Python’s popular data analytics libraries: NumPy, Pandas, Matplotlib, Scipy, Seaborn, and Plotly.
Practical skills in data manipulation, analysis, and visualization techniques.
Application of statistical concepts in data analysis.
Solving business problems by translating them into analytical questions.
Efficient handling of large datasets and identifying patterns and trends.
Creation of powerful data visualizations and dashboards for reporting.
Writing scalable Python code for data analysis.
Requirements
A computer with Internet access to view course materials and exercises.
An eagerness to learn and a curiosity-driven mindset!
Basic understanding of Python. If you’re a beginner, consider my other python course first.
Description
In the next few years 100 million+ terabytes of data will need to be managed and processed and companies such as Apple, Amazon, Facebook, Google and several AI firms are willing to pay top dollar to get skilled workers that are able to analyze and get insights from this data. Immerse yourself in the world of data analytics with our comprehensive course, “Python for Data Mastery” which will help you become a data unlocking wizard using the most popular and in-demand python data analytics and visualization libraries used in most companies such as Numpy, Pandas, Matplotlib, Seaborn, Scipy and Plotly.Designed to help both beginners and seasoned professionals, this course bridges the gap between theory and practice, teaching students to manipulate, analyze, and visualize data using Python’s most powerful libraries.Over hours of meticulously crafted content, the course offers a myriad of practical assignments closely resembling real-world scenarios, ensuring students acquire not just knowledge, but valuable and employable skills. Our graduates have gone on to advance their careers in data analysis, machine learning, data engineering, and more. Empower your staff today, and unlock the true potential of your business data.Mastery in Python’s popular data analytics libraries: Numpy, Pandas, Matplotlib, Seaborn, and Plotly.Practical skills in data manipulation, analysis, and visualization techniques.Application of statistical concepts in data analysis.Efficient handling of large datasets and identifying patterns and trends to forecast trends.Creation of powerful data visualizations and dashboards for reporting that speak volumes in the boardroom.Writing scalable Python code for data analysis that stands the test of data volume and complexity.Translating complex business challenges into analytical solutions that drive decision-making.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Data and Information
Lecture 3 Environment Setup
Section 2: Numpy
Lecture 4 NumPy Part 1
Lecture 5 NumPy Part 2
Lecture 6 NumPy Part 3
Lecture 7 NumPy Exercise
Lecture 8 NumPy Exercise Solution
Section 3: Pandas
Lecture 9 Pandas Part 1
Lecture 10 Pandas Part 2a
Lecture 11 Pandas Part 2b
Lecture 12 Pandas Part 3a
Lecture 13 Pandas Part 3b
Lecture 14 Pandas Part 4
Lecture 15 Pandas Exercise 1
Lecture 16 Pandas Exercise 1 Solution
Lecture 17 Pandas Exercise 2
Lecture 18 Pandas Exercise 2 Solution
Section 4: Matplotlib
Lecture 19 Matplotlib Part 1
Lecture 20 Matplotlib Part 2
Lecture 21 Matplotlib Part 3
Lecture 22 Matplotlib Exercise
Lecture 23 Matplotlib Exercise Solution
Section 5: Seaborn
Lecture 24 Seaborn Part 1
Lecture 25 Seaborn Part 2
Lecture 26 Seaborn Part 3
Lecture 27 Seaborn Part 4
Lecture 28 Seaborn Part 5
Lecture 29 Seaborn Part 6
Lecture 30 Seaborn Exercise
Lecture 31 Seaborn Exercise Solution
Section 6: Plotly
Lecture 32 Plotly Part 1
Lecture 33 Plotly Part 2
Lecture 34 Plotly Exercise
Lecture 35 Plotly Exercise Solution
Beginners seeking a solid foundation in Python and data analytics. This includes students and professionals wanting to pivot into a data-focused career.,Mid-level data analysts or scientists looking to enhance their Python programming skills and learn new techniques for data analysis and visualization.,Seasoned professionals in the field of data science who want to master advanced concepts and stay updated with the latest tools and libraries.,IT professionals from other fields seeking to diversify their skills into data analysis.,Business professionals, managers, and consultants who want to leverage data analytics for better decision-making.,Companies looking to train their workforce in data analytics to maximize the insights gained from their business data.
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