Data Analysis and Data Science Essential Course

Python Data analysis with Pandas, NumPy, Random, Seaborn, Matplotlib

Created by : Rated :4 544 views

Last Updated: 2022-11-13 05:11:39 English Course Type: Self Learning

Grouping, Sorting, analysing, organizing, sorting, filtering, pivoting, aggregating and more!

Hi? Welcome to Python Data Science Essential Course for Data Analysis and Visualization. This course is essential for Python Programming Intermediate students with adequate Knowledge of Python. The learning content for this course is for Beginners in Data Science with a desire to further their knowledge by dive in into Data Science and Machine Learning, this is the right course for you. This course is easy to follow and teaches new concepts in Data Analysis and Science.

In this course, we are going to cover, Data Structures and Binary Files followed by Pandas, Numpy, Random, Seaborn, SciPy and Matplotlib. With this course, you can start your journey in Data Science and Machine Learning. all that’s needed is your creativity and problem-solving skills. With this course, you can add confidence and programming skills competence to your resume. Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

Do not miss out on technology, these days everyone is a Data collector so, take your time with this course and learn how to manipulate data after collection. In Addition, if you are an expert with full and enough knowledge about Data Science this course is not for you and if you are in an advanced class already this course is not for you again. But if you are already a programmer and wish to learn Python Programming Language while specifically focusing on Data Science, then this course is definitely for you.

In this course, we will be using PyCharm and Anaconda .

Computer

Python Beginner level

Section 1 : Course Introduction 4 Lectures 00:17:34
Lecture 1 :
What you will learn in this course Preview
Lecture 2 :
Installing PyCharm
Lecture 3 :
Anaconda Installation
Lecture 4 :
Anaconda Navigator Interface
Section 2 : PANDAS DATA ANALYSIS WITH PYTHON 8 Lectures 00:34:55
Lecture 1 :
Introduction lecture
Lecture 2 :
Pandas Data in series
Lecture 3 :
Dictionaries
Lecture 4 :
Dataframes
Lecture 5 :
Pandas dataframe
Lecture 6 :
Read CSV
Lecture 7 :
Analyzing Data
Lecture 8 :
Plotting with Pandas
Section 3 : Numeric Python Concept (NumPy) 14 Lectures 00:55:54
Lecture 1 :
Introduction Lecture
Lecture 2 :
Dimensional Arrays
Lecture 3 :
Indexing
Lecture 4 :
Slicing
Lecture 5 :
DataType
Lecture 6 :
Views
Lecture 7 :
Array Shape
Lecture 8 :
Array Convention
Lecture 9 :
Iteration
Lecture 10 :
Enumerated
Lecture 11 :
Joining
Lecture 12 :
Search
Lecture 13 :
Sort
Lecture 14 :
Filter
Section 4 : Random Python Concept 6 Lectures 00:25:05
Lecture 1 :
Introduction lecture
Lecture 2 :
Data Distribution
Lecture 3 :
Elements Permutation
Lecture 4 :
Visual Distribution
Lecture 5 :
Normal Graph
Lecture 6 :
Binomial Graph
Section 5 : Python Seaborn in Data Analysis 4 Lectures 00:26:46
Lecture 1 :
Data Structure
Lecture 2 :
Dataset
Lecture 3 :
Visualizing Data
Lecture 4 :
Color Concept
Section 6 : Python Matplotlib Tutorial 9 Lectures 00:35:43
Lecture 1 :
Introduction lecture
Lecture 2 :
Plotting
Lecture 3 :
Multiple Points Plotting
Lecture 4 :
Markers
Lecture 5 :
Line Construction Part 1
Lecture 6 :
Line Construction Part 2
Lecture 7 :
Label and Titles
Lecture 8 :
Subplots
Lecture 9 :
Pie Chart
Section 7 : Machine Learning Fundamentals 4 Lectures 00:25:03
Lecture 1 :
Section Overview
Lecture 2 :
Machine Learning Algorithm
Lecture 3 :
Getting Data for Machine Learning
Lecture 4 :
Statistical Attributes for Machine Learning

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Fanuel Mapuwei,

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My name is Fanuel Mapuwei. I am a professional Fullstack Python developer. I have more than 3+ years of experience in software development. I have developed several applications for savannah network company which helped them to simplify tasks. Fanuel Mapuwei is a Computer Science Graduate under the Guidance of the University of Zimbabwe.