Python for Data Mastery: Data Analytics with Top Libraries

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Job Ready Programmer

6:44:51

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1 - Introduction

1 - Introduction.mp4

03:07

2 - Data and Information.mp4

06:33

3 - Environment Setup.mp4

05:42

2 - Numpy

4 - NumPy Part 1.mp4

15:18

5 - NumPy Part 2.mp4

21:26

6 - NumPy Part 3.mp4

19:48

7 - NumPy Exercise.mp4

03:10

7 - numpy-exercise.zip

8 - NumPy Exercise Solution.mp4

08:14

8 - numpy-exercise-solutions.zip

3 - Pandas

9 - Pandas Part 1.mp4

22:02

9 - test.csv

10 - Pandas Part 2a.mp4

18:52

11 - Pandas Part 2b.mp4

11:39

12 - Pandas Part 3a.mp4

17:26

13 - Pandas Part 3b.mp4

16:14

14 - Pandas Part 4.mp4

17:48

15 - AAPL-data.csv

15 - Pandas Exercise 1.mp4

03:06

15 - pandas-exercise-1.zip

16 - Pandas Exercise 1 Solution.mp4

08:42

16 - pandas-exercise1-solutions.zip

17 - Pandas Exercise 2.mp4

03:22

17 - WHO-COVID-19-Canada.csv

17 - pandas-exercise2.zip

18 - Pandas Exercise 2 Solution.mp4

12:46

18 - pandas-exercise2-solutions.zip

4 - Matplotlib

19 - Matplotlib Part 1.mp4

22:57

20 - Matplotlib Part 2.mp4

09:45

21 - Matplotlib Part 3.mp4

12:25

22 - Matplotlib Exercise.mp4

05:13

22 - WHO-COVID-19-SEAsia.csv

22 - matplotlib-exercise.zip

23 - Matplotlib Exercise Solution.mp4

14:28

5 - Seaborn

24 - Seaborn Part 1.mp4

14:03

25 - Seaborn Part 2.mp4

17:48

26 - Seaborn Part 3.mp4

12:01

27 - Seaborn Part 4.mp4

12:15

28 - Seaborn Part 5.mp4

07:46

29 - Seaborn Part 6.mp4

09:39

30 - Seaborn Exercise.mp4

04:02

30 - cereal.csv

30 - seaborn-exercise.zip

31 - Seaborn Exercise Solution.mp4

13:41

6 - Plotly

32 - Plotly Part 1.mp4

13:17

33 - Plotly Part 2.mp4

09:36

34 - Plotly Exercise.mp4

02:49

34 - marathon-results-2017.csv

34 - plotly-exercise.zip

35 - Plotly Exercise Solution.mp4

07:51

Description

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.

Who is this for?

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.

What You Need to Know?

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.

More details

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 AIÂ 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-demandÂ 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.

Who this course is for:
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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You can’t learn programming from reading books or online fill-in-the-blank type tutorials. Especially the online tutorials with browser based exercises where you code directly in your browser. The problem with that approach is that it doesn’t provide practical experience. It provides an illusion of learning something as it tugs you along to complete an assignment that’s more of a fill-in-the-blank type problem. A student feels like they’ve learned something but that knowledge does not stick. Unfortunately that experience will not help in an interview nor an actual project. Valuable time ends up going to complete waste. At JRP (JobReadyProgrammer), we don’t follow hype. We do what works! We take a traditional route to teaching how to code advancing slowly & patiently in the lectures often repeating key concepts in multiple different ways to help students really solidify the knowledge and mold their foundation to discover how to code properly and then boy do we test the skills! Students are put right in the middle of a practical real-world programming assignment to apply everything they've learned. So enough of those “key in the next few commands to fill in the puzzle and we’ll advance you” kind of tutorials. Here you’ll need to roll up your sleeves and get to work on solving practical programming assignments.

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