Python Data Analysis and Data Science Essentials.

Python Data Analysis and Data Science Essentials., available at $54.99, has an average rating of 4.6, with 69 lectures, based on 51 reviews, and has 1145 subscribers.

You will learn about Understand the fundamental of plotting Acquire adequate hands-on skills and confidence to use Python for Machine learning and Data Science Straight forward Programming Methodology Able to design well organized and problem-solving programs Use Statistical analysis to make predictions Working with different types of data Data Distributions This course is ideal for individuals who are Intermediate learners who wish to go further with Python Programming Language or Beginner Python learners who are curious with Machine learning and Data Science It is particularly useful for Intermediate learners who wish to go further with Python Programming Language or Beginner Python learners who are curious with Machine learning and Data Science.

Enroll now: Python Data Analysis and Data Science Essentials.

Summary

Title: Python Data Analysis and Data Science Essentials.

Price: $54.99

Average Rating: 4.6

Number of Lectures: 69

Number of Published Lectures: 69

Number of Curriculum Items: 69

Number of Published Curriculum Objects: 69

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the fundamental of plotting
Acquire adequate hands-on skills and confidence to use Python for Machine learning and Data Science
Straight forward Programming Methodology
Able to design well organized and problem-solving programs
Use Statistical analysis to make predictions
Working with different types of data
Data Distributions

Who Should Attend

Intermediate learners who wish to go further with Python Programming Language
Beginner Python learners who are curious with Machine learning and Data Science

Target Audiences

Intermediate learners who wish to go further with Python Programming Language
Beginner Python learners who are curious with Machine learning and Data Science

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.

PLEASE NOTE THAT: the course comes with UDEMY’S 30-DAY unconditional MONEY-BACK GUARANTEE. So what are you waiting for?

In this course, we will be using PyCharm and Anaconda (Jupyter Notebook) as our IDE.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Curriculumn

Lecture 2: PyCharm installation

Lecture 3: Anaconda Installation

Lecture 4: Opening Anaconda Navigator and Jupyter Notebook

Chapter 2: Python Data structures and Binary Files

Lecture 1: Why lists

Lecture 2: Dictionaries

Lecture 3: Sets in Python

Lecture 4: Boolean Logic concept in Python

Lecture 5: Binary files-I/O in Python

Chapter 3: PYTHON PANDAS TUTORIAL

Lecture 1: Introduction to Python Pandas

Lecture 2: Pandas Series

Lecture 3: Dictionaries with Pandas Series

Lecture 4: DataFrames

Lecture 5: Pandas DataFrame

Lecture 6: Panda Read CSV

Lecture 7: Pandas Analyzing DataFrame

Lecture 8: Pandas Plotting

Chapter 4: PYTHON NUMPY TUTORIAL

Lecture 1: Getting started

Lecture 2: Creating Dimensional Arrays

Lecture 3: Array Indexing

Lecture 4: Array Slicing

Lecture 5: Data Type

Lecture 6: View and Copy

Lecture 7: Shape and Reshaping

Lecture 8: 1D to 3D Array Conversion

Lecture 9: Array Iteration

Lecture 10: Enumerated Iteration

Lecture 11: Joining Array

Lecture 12: Array Search

Lecture 13: Array Sorting

Lecture 14: Array Filtering

Chapter 5: RANDOM TUTORIAL

Lecture 1: Getting started

Lecture 2: Random Data Distribution

Lecture 3: Permutation of Elements

Lecture 4: Visualizing Distributions

Lecture 5: Normal Graph Plotting

Lecture 6: Binomial Graph Plotting

Chapter 6: UNIVERSAL FUNCTIONS TUTORIAL

Lecture 1: Introduction

Lecture 2: Universal Function Add Operator Concept

Lecture 3: Creating a Universal Function

Lecture 4: Array Arithmetic Conditional

Lecture 5: Universal Functions Array Arithmetics

Lecture 6: Cumulative Summation

Lecture 7: Least Common Multiple

Chapter 7: PYTHON SEABORN TUTORIAL

Lecture 1: Data Structure

Lecture 2: Datasets

Lecture 3: Visualizing Data

Lecture 4: Color Concept

Chapter 8: SciPy TUTORIAL

Lecture 1: Introduction to Scientific Python

Lecture 2: Numeric Python Essentials

Lecture 3: SciPy Constants

Lecture 4: SciPy Integrate

Lecture 5: SciPy Sparse Data

Lecture 6: SciPy Image Processing

Lecture 7: SciPy Spatial Data

Lecture 8: KDTREES

Chapter 9: PYTHON MATPLOTLIB TUTORIAL

Lecture 1: Introduction

Lecture 2: Matplotlib Plotting

Lecture 3: Multiple Points plotting

Lecture 4: Matplotlib Markers

Lecture 5: line Construction Part 1

Lecture 6: line Construction Part 2

Lecture 7: Labels and Titles

Lecture 8: Subplots

Lecture 9: Pie Chart

Chapter 10: MACHINE LEARNING ESSENTIALS TUTORIAL

Lecture 1: Tutorial Overview

Lecture 2: Machine Learning Algorithm

Lecture 3: Getting Data For Machine Learning Projects

Lecture 4: Machine Learning: Statistical Attributes

Instructors

Fanuel Mapuwei
Computer Scientist | Data Scientist | Analyst in Finance
Protious Ngoro
Stocks and Financial market Analyst

Rating Distribution

1 stars: 1 votes
2 stars: 2 votes
3 stars: 4 votes
4 stars: 4 votes
5 stars: 40 votes

Frequently Asked Questions

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