Python Course for Data Analysis :Numpy + Pandas + Matplotlib

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Python Course for Data Analysis Numpy + Pandas + Matplotlib [TutsNode.com] - Python Course for Data Analysis Numpy + Pandas + Matplotlib 7. Merging Dataframes in Pandas
  • 5. Joins using Pandas.mp4 (159.5 MB)
  • 7. Quiz.html (0.2 KB)
  • 6. Interview Question 90% candidates get this wrong.mp4 (51.7 MB)
  • 1. Concat dataframes along rows.mp4 (49.7 MB)
  • 2. Concat dataframes along columns.mp4 (37.5 MB)
  • 3. Append.mp4 (20.8 MB)
  • 4. Introduction to Joins.mp4 (19.0 MB)
4. Data Types in Python
  • 1.1 Pandas.ipynb (298.0 KB)
  • 7. Quiz.html (0.2 KB)
  • 2. Access and Modify List.mp4 (89.2 MB)
  • 3. Operations on a List.mp4 (81.8 MB)
  • 4. Introduction to Dictionary.mp4 (65.5 MB)
  • 1. Introduction to list.mp4 (54.4 MB)
  • 6. Dictionary of Dictionaries.mp4 (42.6 MB)
  • 5. Operations on Dictionary.mp4 (6.4 MB)
5. Creating Dataframes using Pandas
  • 3.1 countries_data.xlsx (24.9 KB)
  • 2.1 countries_data.csv (13.8 KB)
  • 4.1 countries_data.txt (13.8 KB)
  • 5. Quiz.html (0.2 KB)
  • 1. Introduction to Pandas Creating the first Dataframe.mp4 (155.9 MB)
  • 2. Create Dataframe from csv.mp4 (87.6 MB)
  • 3. Creating Dataframes from excel (xlsx).mp4 (52.4 MB)
  • 4. Creating dataframe from text file.mp4 (30.1 MB)
3. Numpy
  • 1.1 Numpy.ipynb (13.8 KB)
  • 6. Quiz.html (0.2 KB)
  • 4. Access elements of an array.mp4 (110.0 MB)
  • 1. Create numpy array.mp4 (108.1 MB)
  • 5. Mathematical Operations on an array.mp4 (74.6 MB)
  • 3. Multi Dimention array.mp4 (57.9 MB)
  • 2. Sort, Add and Remove elements.mp4 (50.2 MB)
2. Introduction to Python
  • 1.1 Introduction to Python .ipynb (6.8 KB)
  • 3. Quiz.html (0.2 KB)
  • 2. Data types and type conversion.mp4 (29.8 MB)
  • 1. Variables in Python.mp4 (14.5 MB)
6. Data Selection and Manipulation
  • 4. Quiz.html (0.2 KB)
  • 1. Using loc.mp4 (99.9 MB)
  • 3. Modify and filter rows using loc.mp4 (83.6 MB)
  • 2. Using iloc.mp4 (7.5 MB)
8. Aggregations
  • 4. Quiz.html (0.2 KB)
  • 2. Aggregation Part 2.mp4 (114.6 MB)
  • 1. Aggregation Part 1.mp4 (85.7 MB)
  • 3. Rank Function.mp4 (78.0 MB)
9. Working with Datetime in pandas
  • 4. Quiz.html (0.2 KB)
  • 2. Weight loss per day.mp4 (143.2 MB)
  • 3. Age of the people.mp4 (89.9 MB)
  • 1. Getting Started.mp4 (89.6 MB)
10. Advance Data Manipulation
  • 6. Quiz.html (0.2 KB)
  • 1. Pivot Table Part 1.mp4 (102.9 MB)
  • 2. Pivot Table Part 2.mp4 (88.9 MB)
  • 4. Stack Unstack Part 2.mp4 (84.4 MB)
  • 5. Melting a Dataframe.mp4 (70.7 MB)
  • 3. Stack Unstack Part 1.mp4 (9.3 MB)
11. Iterating over Pandas Dataframe
  • 6. Quiz.html (0.2 KB)
  • 1. Loops in Python.mp4 (136.3 MB)
  • 5. Iterating 100x faster over Pandas Dataframe Method 3.mp4 (101.4 MB)
  • 3. Iterating over Pandas Dataframe Method 1.mp4 (79.4 MB)
  • 4. Iterating 50x faster over Pandas Dataframe Method 2.mp4 (74.2 MB)
  • 2. Range Function.mp4 (21.8 MB)
12. User defined functions
  • 3. Quiz.html (0.2 KB)
  • 1. Using def.mp4 (146.7 MB)
  • 2. Lambda and Apply.mp4 (139.9 MB)
13. Working with String Columns
  • 5. Quiz.html (0.2 KB)
  • 4. Replace and split a string.mp4 (142.8 MB)
  • 3. Removing extra spaces.mp4 (101.2 MB)
  • 2. String cases.mp4 (60.2 MB)
  • 1. String Datatypes.mp4 (48.2 MB)
15. Data Visualization using Matplotlib
  • 11. Quiz.html (0.2 KB)
  • 4. Histogram Part 1.mp4 (66.9 MB)
  • 1. Line Chart.mp4 (56.4 MB)
  • 10. Name of the Countries and Grids.mp4 (55.8 MB)
  • 9. Color of the Countries.mp4 (51.9 MB)
  • 7. Scaling axis.mp4 (48.6 MB)
  • 2. Scatter Plot.mp4 (42.5 MB)
  • 6. Adding axis labels and titles.mp4 (39.9 MB)
  • 3. Bar Chart.mp4 (36.1 MB)
  • 5. Histogram Part 2.mp4 (32.4 MB)
  • 8. Size of the Countries.mp4 (30.0 MB)
1. Getting Started
  • 2. Setting up the environment.mp4 (95.7 MB)
  • 3. How to access the resources.mp4 (32.8 MB)
  • 1. Introduction to the course.mp4 (9.7 MB)
14. Coding Assignment
  • 2. Solution.mp4 (8.9 MB)
  • 1. Pandas Assignment.mp4 (7.2 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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    • Description


      Description

      Hello and welcome to the course !

      I hope you are exited to start this journey of learning python for data analysis.

      By the end of this course you be able to comfortably manipulate and visualize your data.

      I just want a commitment from you that you will attempt all the 13 quizzes that are distributed across each of the modules and also solve the Final assignment honestly. Why ? Because the more you practice the better you code.

      Why you should take this course :

      It’s Memorable: You’ll learn the “why” behind everything you do, so you remember the concepts and can use them on your own later.

      It’s the Perfect Length: The course is just 6.5 hours long, so you’ll actually be able to finish it and get your certificate.

      It Goes at the Perfect Pace: You will learn the Python fundamentals at a pace tailored to beginners. This means you won’t get left behind, and won’t waste time on irrelevant filler.

      It’s Practical: You actually use Pandas to manipulate data. It’s not just dry theory. You can see you’ve understood by solving Quizes at the end of each section. There is a mega coding assignment to give you a hands on flavour and make you more confident in this skill. Over time i will keep on adding more coding assignmet for your practice.

      Now, let’s have a look at the course outline.

      We will start by laying some foundation with the below lectures :

      Basic Introduction to Python.
      Numpy Package, which forms the foundation of Pandas Package.

      Then, we will learn about :

      Data Types in Python to store collections of data.

      Then we will start with the following :

      Create Dataframe in Pandas from different file formats.
      Data Selection and Filtering.
      Merging and aggregations which forms the back bone of the dataframe analysis.
      Working with Datetime using pandas.
      Advance Data Manipulation.
      Loops and Functions.
      Data Visualization.
      Assignment – Work Fitbit User Activity data.

      Try out the course for a full 30 days, with a Udemy-approved Zero Risk, 30 Day 100% Money Back Guarantee! You have absolutely nothing to lose and everything to gain!

      Enroll in the Python for Data Analysis course Now! — You’ll be glad that you did!
      Who this course is for:

      Anyone who is looking to learn python for data analysis

      Requirements

      No programming experience needed. You will learn everything you need to know.

      Last Updated 11/2021



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Python Course for Data Analysis :Numpy + Pandas + Matplotlib

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4.2 GB
seeders:17
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Python Course for Data Analysis :Numpy + Pandas + Matplotlib

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