Master Data Science Prerequisites - Numpy - Pandas- Seaborn

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7:53:27

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1. Introduction

1. Introduction.mp4

04:56

2.1 Numpy_Resources.rar

2.2 oops.rar

2.3 Pandas Resources.rar

2.4 Python.rar

2.5 seaborn.rar

2. Resources.html

3. Hello.mp4

00:53

4. Congratulations.mp4

02:52

2. Python (Optional)

1. Installation - Python.mp4

03:57

2. Installation - VS Code.mp4

01:39

3. Setup and Hello World.mp4

08:02

4. Drawing a Shape.mp4

02:33

5. DataTypes.mp4

04:24

6. Variables.mp4

07:28

7. Strings.mp4

20:34

8. Numbers.mp4

12:57

9. Taking an input.mp4

03:31

10. Lists.mp4

06:04

11. List_functions.mp4

09:27

12. Tuples.mp4

03:52

13. Dictionaries.mp4

04:12

14. Functions.mp4

08:48

15. Return Statement.mp4

05:18

16. Operators.mp4

02:23

17. If Statement.mp4

12:51

18. If Statements & Comparisons.mp4

03:44

19. While Loop.mp4

04:23

20. For Loops.mp4

08:05

21. Exponent Function.mp4

03:41

22. 2D Lists & Nested Loops.mp4

04:01

23. Comments.mp4

01:38

24. Try & Except.mp4

03:45

25. Reading Files.mp4

03:54

26. Writing Files.mp4

03:04

3. Oops (Optional)

1. Introduction.mp4

03:00

2. What is oop.mp4

10:37

3. Encapsulation.mp4

07:34

4. Abstraction.mp4

06:05

5. Inheritance.mp4

05:27

6. Polymorphism.mp4

06:11

4. Review

1. Review.mp4

00:25

5. NumPy and it's Applications

1. NumPy and it's Applications.mp4

03:42

6. Initializing an Array

1. Initializing an Array.mp4

02:38

2. NumPy Datatypes.mp4

06:19

7. AccessingChanging Specific Elements, Rows, Columns, etc

1. AccessingChanging Specific Elements.mp4

11:06

8. Initializing Different Arrays (1s, 0s, full, random, etc)

1. Initializing Different Arrays (1s, 0s, full, random, etc).mp4

09:55

9. Basic Mathematics (arithmetic, trigonometry, etc.)

1. Basic Mathematics (arithmetic, trigonometry, etc.).mp4

04:49

10. Linear Algebra and Statistics

1. Linear Algebra and Statistics.mp4

07:56

11. Reorganizing Arrays

1. Reorganizing Arrays.mp4

05:00

12. Load data in from a file

1. Load data using NumPy.mp4

02:27

13. Advanced Indexing and Boolean Masking

1. Advanced Indexing and Boolean Masking.mp4

03:38

14. Introduction of Pandas

1. Introduction of Pandas.html

2. Data Description.mp4

04:12

3. Importing the data.mp4

15:29

15. Creating a DataFrame

1. Series.mp4

03:42

2. Creating a DataFrame.mp4

07:43

3. Homework.mp4

03:42

16. Data Cleaning

1. Data Cleaning.mp4

03:02

17. Dealing with Empty cells

1. Mean Median Mode.mp4

06:10

2. Empty cells.mp4

08:33

18. Dealing with wrong data

1. Wrong Data.mp4

12:51

19. Dealing with wrong data type

1. Wrong Datatype.mp4

06:10

20. Dealing with duplicate data

1. Duplicates.mp4

05:30

21. Correlation

1. Correlation Introduction.mp4

01:53

2. Correlation.mp4

02:01

22. Seaborn

1. Importing the data.mp4

06:29

23. Distribution Plot

1. DISPLOT.mp4

07:08

2. Uses of distribution plot.mp4

02:25

24. KDE PLOT

1. KDE introduction.mp4

07:45

2. KDE Plot Part 1.mp4

07:03

3. KDE Plot Part 2.mp4

15:04

25. Scatter Plot

1. Scatter Plot.mp4

11:40

26. Rug Plot

1. Rug Plot.mp4

03:45

27. Joint Plot

1. Joint Plot.mp4

07:55

28. Pair Plot

1. Pair Plot.mp4

05:55

29. Bar Plot

1. Bar Plot.mp4

12:04

30. Count Plot

1. Count Plot.mp4

02:00

31. Box Plot

1. Box Plot.mp4

06:33

32. Violin Plot

1. Violin Plot.mp4

04:50

33. Strip Plot

1. Strip Plot.mp4

04:16

34. Swarm Plot

1. Swarm Plot.mp4

03:37

35. Heat Map

1. Heat Map.mp4

09:25

36. Pair Grid

1. Pair Grid.mp4

08:45

37. Sub Plots

1. Sub Plots.mp4

05:16

38. Certificate

1. Certificate.mp4

00:49

Description

Analyze and Visualization data quickly and easily with Python's powerful Numpy, Pandas, Seaborn Library

What You'll Learn?

You will be able to program in Python professionally
Be able to use Python for data science and machine learning
You will be able to use Python for your own work problems or personal projects.
Importing and creating data frame in python
Learn everything there is to know about pandas - from absolute scratch!
Perform a multitude of data operations in Python's popular pandas library including grouping, pivoting, joining and more!
Create beautiful visualizations with Seaborn
Distribution Plot, Histograms, KDE Plots, Scatter Plot, Rug Plot, Joint Plot, Pair Plot, Bar Plot, Count Plot, Box Plot, Violin Plot, Strip Plot, Swarm Plot
Heat Map, Pair Plot, Sub Plot

Who is this for?

Data Scientists, Data Analysts.

Students and professionals who wants to do data analysis using python.

Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code

Python developer who wants to do analysis of tabular data.

Who wants to start their data visualization journey

Who Is curious about how to build effective and impactful graphs

Who Is looking to break into the business intelligence, analytics or data visualization field

Anyone who want to express thoughts, findings, insights from any kind of data using data Visualization.

More details

Description
*** Enrol now in the most comprehensive and up-to-date (Nov 2022) course available for the Data Science Prerequisites Concepts! *

About the Instructor.
This course is led by Aditya Dhandi - an international trainer, consultant, and data analyst with over 100 000 enrollments on Udemy. Aditya specializes in teaching data analysis techniques, Excel Pivot Tables, Power Pivot, Microsoft Power BI, & Google Data Studio & his courses average 4+ stars out of 5.

He's also the founder of the popular website, Jobshie.

Why Learn Data Science Prerequisites?
If you've spent time in a spreadsheet software like MS Excel or Google Sheets and want to take your data analysis skills to the next level, this course is for you!
Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with â€œrelationalâ€ or â€œlabeledâ€ data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

Pandas is the most powerful and flexible open source data analysis/manipulation tool available in any language.
pandas is well suited for many different kinds of data:
Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
Ordered and unordered (not necessarily fixed-frequency) time series data.
Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure

Why Learn Numpy?
The central object in Numpy is the Numpy array, on which you can do various operations.
The key is that a Numpy array isnâ€™t just a regular array youâ€™d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.
That means you can do vector and matrix operations like addition, subtraction, and multiplication.
The most important aspect of Numpy arrays is that they are optimized for speed. So weâ€™re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.
Then weâ€™ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.

Why Learn Seaborn Library?
"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.

This course is designed to teach analysts, students interested in data science, statisticians, and data scientists how to analyze real-world data by creating professional-looking charts and using numerical descriptive statistics techniques in Python 3.
We'll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project.

Key Features:

Video Lectures: Learn the best ways to learn Data Science Prerequisites Course Certificate: Complete the course and show off your skills with the course certificate.
Learn from experts: Your expert instructor will teach you Data Science Prerequisites skills you can apply immediately.
Full Excess: There is no time limit, so take the course at your own pace and retake lessons as you need.
Use any Device: Join the course using any modern browser on your phone, tablet and computer.
Ask Question: Ask questions and share ideas with other students in the course community.

What's included?
High quality video lessons to build your knowledge and skills.
Guided walk-throughs with techniques and tips.
14 pre-built dashboards you can use and customize.
Practical exercises to apply your skills.
Quizzes to reinforce learnings and test your knowledge.
Private discussion area where you can ask questions.
Course certificate for completing the lessons and assignment.
Full access lets you review lessons whenever you need.
Updates when lessons in the course are refreshed.

Jobshie Academy Reviews
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We help learners across the globe develop new skills and achieve their personal and professional goals. Browse learner reviews below to discover how people enjoy the online learning experience at Jobshie Academy.

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FAQs
When does the Data Science Prerequisites course start and finish?
The course starts as soon as you join! It is a completely self-paced online course, so you decide when you start and when you finish.
How long does it take to complete the course?
We recommend taking the course over two to three weeks, so you have time to apply the lessons to your account (or your clientâ€™s account). That being said we've seen people complete the course in a week and others that spread the lessons over a couple of months.
How long do I have access to the Data Science Prerequisites course?
You receive full access to the course so that you can take the lessons at any time. You can rewatch lessons whenever you like and any lesson updates are included.
Will I receive a certificate?
Yes, once you complete all of the lessons, exercises, quizzes, and course assignment, you'll receive your course certificate.

What are you waiting for?
Thereâ€™s never been a better time to add a skill like programming to your toolbox and Data Science Prerequisites to get started. So check out the free preview and get enrolled! You've got nothing to lose and everything to gain!

Who this course is for:
Data Scientists, Data Analysts.
Students and professionals who wants to do data analysis using python.
Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code
Python developer who wants to do analysis of tabular data.
Who wants to start their data visualization journey
Who Is curious about how to build effective and impactful graphs
Who Is looking to break into the business intelligence, analytics or data visualization field
Anyone who want to express thoughts, findings, insights from any kind of data using data Visualization.

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