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Data Science with Numpy, Pandas, Matplotlib & Seaborn

(26 Ratings) 1793 Students Enrolled
Created By Kavin kumar Last Updated Mon, 16-Mar-2020 English
  • Course Duration
    3 Hours
  • Mode of Training
    Self-Paced
  • Lessons
    31 Lessons
  • Placement Assistance
    Guaranteed
12k+ satisfied learners Read Reviews
What Will I Learn?
  • Have a good hands-on skills in Data Visualization Insights
  • Learn Python basics such as Identifiers, Operators, Decision Control and Loops
  • Collections concepts such as List, Tuples, Set and Dictionary
  • Numpy Arrays to Multi-dimensional array and operations
  • Pandas DataFrames - Introduction and Operation
  • Learn Matplotlib and Seaborn for basic visualizations to retrieve meaningful insights
  • Installing Anaconda and launching Jupyter

Requirements
  • Basic Python knowledge
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Description

This course is an excellent choice for beginners, working professionals looking to expand their knowledge on Data Science and learn most popular Python libraries such as collections, numpy, matplotlib, seaborn and pandas data frames.

If you are working with Microsoft Excel, Google sheets or any other tabular data such as database tables, csv files and eager to take your Data Analysis skills to next level in your career, then this course is for you!

NumPy is a powerful library which efficiently performs matrix operations faster and exceed the python capabilities of data processing.

Pandas allows you to anything and everything with tabular or column data sets such as analyzing, organizing, sorting, filtering, aggregating, cleaning, calculating and more!

Matplotlib is a visualization library in Python for 2D plots of arrays. Matplotlib is a multi platform data visualization library built on Numpy arrays. Matplotlib consists of several plots like line, bar, scatter, histogram, etc.

Seaborn is a library for making statistical graphs in Python which is built on top of Matplotlib and closely integrated with pandas data structures.

Curriculum For This Course
31 Lessons 3 Hours
  • Course Outline 00:00:28 Preview
  • Anaconda or Python Installation 00:05:02 Preview
  • Creating variables 00:03:23
  • Variable Value Assignment 00:01:29
  • Data Types and Memory Address 00:07:48
  • Type Conversions 00:03:52
  • Indentation, Comments and Doc Strings 00:04:08
  • Input Output 00:02:53
  • Operators 00:10:08
  • Decision Control 00:04:41
  • While Loop 00:13:10
  • For Loop 00:05:41
  • Collections 00:05:55 Preview
  • Numpy Array - Topic 1 00:02:08 Preview
  • Numpy Array - Topic 2 00:09:45
  • Numpy Array - Topic 3 00:02:45
  • Numpy Array - Topic 4 00:08:22
  • Numpy Array - Topic 5 00:04:29
  • Numpy Array - Topic 6 00:17:12
  • Pandas DataFrame - Topic 1 00:13:06 Preview
  • Pandas DataFrame - Topic 2 00:18:17
  • Matplotlib - Topic 1 00:00:18
  • Matplotlib - Topic 2 00:10:29
  • Seaborn Library - Topic 1 00:03:47
  • Seaborn Library - Topic 2 00:02:02
  • Seaborn Library - Topic 3 00:00:13
  • Seaborn Library - Topic 4 00:01:43
  • Seaborn Library - Topic 5 00:02:32
  • Dataset and Goal of Analysis 00:02:30
  • Exploratory Data Analysis 00:12:04
  • Dataset and Goal of Exploratory Data Analysis 00:01:40

Data Science with Numpy, Pandas, Matplotlib & Seaborn