Data Science Mastery with Python- Comprehensive course

Data Science Mastery with Python: Comprehensive course, available at $54.99, has an average rating of 4.85, with 83 lectures, based on 549 reviews, and has 54087 subscribers.

You will learn about Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions. Apply knowledge and actionable insights from data across a broad range of application domains. This course is ideal for individuals who are For Complete Beginners to Data Sciecne, which will make you Hero in the Data Science Field. It is particularly useful for For Complete Beginners to Data Sciecne, which will make you Hero in the Data Science Field.

Enroll now: Data Science Mastery with Python: Comprehensive course

Summary

Title: Data Science Mastery with Python: Comprehensive course

Price: $54.99

Average Rating: 4.85

Number of Lectures: 83

Number of Published Lectures: 82

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 82

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package

Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing

Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions.

Apply knowledge and actionable insights from data across a broad range of application domains.

Who Should Attend

For Complete Beginners to Data Sciecne, which will make you Hero in the Data Science Field.

Target Audiences

For Complete Beginners to Data Sciecne, which will make you Hero in the Data Science Field.

Today Data Science and Machine Learning are used in almost every industry, including automobiles, banks, health, telecommunications, telecommunications, and more.

As the manager of Data Science and Machine Learning, you will have to research and look beyond common problems, you may need to do a lot of data processing. test data using advanced tools and build amazing business solutions. However, where and how will you learn these skills required in Data Science and Machine Learning?

DATA SCIENCE COURSE-OVERVIEW

Getting Started with Data Science

Define Data

Why Data Science?

Who is a Data Scientist?

What does a Data Scientist do?

The lifecycle of Data Science with the help of a use case

Job trends

Data Science Components

Data Science Job Roles

Math Basics

Multivariable Calculus

Functions of several variables

Derivatives and gradients

Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function

Cost function

Plotting of functions

Minimum and Maximum values of a function

Linear Algebra

Vectors

Matrices

Transpose of a matrix

The inverse of a matrix

The determinant of a matrix

Dot product

Eigenvalues

Eigenvectors

Optimization Methods

Cost function/Objective function

Likelihood function

Error function

Gradient Descent Algorithm and its variants (e.g., Stochastic Gradient Descent Algorithm)

Programming Basics

R Programming for Data Science

History of R

Why R?

R Installation

Installation of R Studio

Install R Packages.

R for business

Features of R

Basic R syntax

R programming fundamentals

Foundational R programming concepts such as data types, vectors arithmetic, indexing, and data frames

How to perform operations in R including sorting, data wrangling using dplyr, and data visualization with ggplot2

Understand and use the various graphics in R for data visualization.

Gain a basic understanding of various statistical concepts.

Understand and use hypothesis testing method to drive business

decisions.

Understand and use linear, non-linear regression models, and

classification techniques for data analysis.

Working with data in R

Master R programming and understand how various statements are executed in R.

Python for Data Science

Introduction to Python for Data Science

Introduction to Python

Python Installation

Python Environment Setup

Python Packages Installation

Variables and Datatypes

Operators

Python Pandas-Intro

Python Numpy-Intro

Python SciPy-Intro

Python Matplotlib-Intro

Python Basics

Python Data Structures

Programming Fundamentals

Working with data in Python

Object-oriented programming aspects of Python

Jupyter notebooks

Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions

Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions

Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave

Perform data analysis and manipulation using data structures and tools provided in the Pandas package

Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline

Use the matplotlib library of Python for data visualization

Extract useful data from websites by performing web scraping using

Python

Integrate Python with MapReduce

Data Basics

Learn how to manipulate data in various formats, for example, CSV file, pdf file, text file, etc.

Learn how to clean data, impute data, scale data, import and export data, and scrape data from the internet.

Learn data transformation and dimensionality reduction techniques such as covariance matrix plot, principal component analysis (PCA), and linear discriminant analysis (LDA).

Probability and Statistics Basics

Important statistical concepts used in data science

Difference between population and sample

Types of variables

Measures of central tendency

Measures of variability

Coefficient of variance

Skewness and Kurtosis

Inferential Statistics

Regression and ANOVA

Exploratory Data Analysis

Data visualization

Missing value analysis

Introduction to Big Data

Introduction to Hadoop

Introduction to Tableau

Introduction to Business Analytics

Introduction to Machine Learning Basics

Supervised vs Unsupervised

Time Series Analysis

Text Mining

Data Science Capstone Project

Science and Mechanical Data require in-depth knowledge on a variety of topics. Scientific data is not limited to knowing specific packages/libraries and learning how to use them. Science and Mechanical Data requires an accurate understanding of the following skills,

Understand the complete structure of Science and Mechanical Data

Different Types of Data Analytics, Data Design, Scientific Data Transfer Features and Machine Learning Projects

Python Programming Skills which is the most popular language in Science and Mechanical Data

Machine Learning Mathematics including Linear Algebra, Calculus and how to apply it to Machine Learning Algorithms and Science Data

Mathematics and Mathematical Analysis of Data Science

Data Science Data Recognition

Data processing and deception before installing Learning Machines

Machine learning

Ridge (L2), Lasso (L1), and Elasticnet Regression / Regularization for Machine Learning

Selection and Minimization Feature for Machine Learning Models

Selection of Machine Learning Model using Cross Verification and Hyperparameter Tuning

Analysis of Machine Learning Materials Groups

In-depth learning uses the most popular tools and technologies of today.

This Data Science and Machine Learning course is designed to consider all of the above, True Data Science and Machine Learning A-Z Course. In most Data Science and Machine Learning courses, algorithms are taught without teaching Python or this programming language. However, it is very important to understand language structure in order to apply any discipline including Data Science and Mechanical Learning.

Also, without understanding Mathematics and Statistics it is impossible to understand how other Data Science and Machine Learning algorithms and techniques work.

Science and Mechanical Data is a set of complex linked topics. However, we strongly believe in what Einstein once said,

“If you can’t explain it easily, you didn’t understand it well enough.”

As a teacher, I constantly strive to reach my goal. This is one comprehensive course in Science and Mechanical Data that teaches you everything you need to learn Science and Mechanical Data using simple examples with great depth.

As you will see from the preview talks, some of the more complex topics are explained in simple language.

Some important skills you will learn,

Python Programming

Python is listed as the # 1 language for Data Science and Mechanical Data. It is easy to use and rich with various libraries and functions required to perform various Data Science and Machine Learning activities. In addition, it is the most widely used and automated language for the use of many Deep Learning frameworks including Tensorflow and Keras.

Advanced Mathematics Learning Machine

Mathematics is the foundation of Data Science in general and Learning Machines in particular. Without understanding the meanings of Vectors, Matrices, their operations and understanding Calculus, it is impossible to understand the basics of Data Science and Machine Learning. The Gradient Declaration of Basic Neural Network and Mechanical Learning is built on the foundations of Calculus and Derivatives.

Previous Statistics for Data Science

It is not enough to know only what you are saying, in the middle, the mode, etc. Advanced Techniques for Science and Mechanical Data such as feature selection, size reduction using PCA are all based on previous Distribution and Statistical Significance calculations. It also helps us to understand the operation of the data and use the appropriate machine learning process to get the best results from various Data Science and Mechanical Learning techniques.

Data recognition

As they say, the picture costs a thousand words. Data identification is one of the most important methods of Data Science and Mechanical Data and is used for Analytical Data Analysis. In that, we analyze the data visually to identify patterns and styles. We will learn how to create different sites and charts and how to analyze them for all practical purposes. Feature Selection plays an important role in Machine Learning and Visualization Data is its key.

Data processing

Scientific Data requires extensive data processing. Data Science and Machine Learning specialists spend more than 2/3 of their time analyzing and analyzing data. Data can be noisy and never in good condition. Data processing is one of the most important ways for Data Science and Mechanics to learn to get the best results. We will be using Pandas which is a well-known Python data processing library and various other libraries for reading, analyzing, processing and cleaning data.

Machine learning

Heart and Soul Data Science is a guessing skill provided by algorithms from the Deep Learning and Learning Machines. Machine learning takes the complete discipline of Data Science ahead of others. We will integrate everything we have learned in previous sections and build learning models for various machines. The key features of Machine Learning are not only ingenuity but also understanding of the various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values ??affect the outcome in order to build the best machine learning models.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Getting Started with Data Science

Lecture 2: Udemy Review Update

Chapter 2: Basic Maths Required for Data Science

Lecture 1: Lets Start with Statistics

Lecture 2: Data Quality Issues

Lecture 3: Types of Statistics

Lecture 4: Measures of Spread

Lecture 5: Measures of Shapes

Lecture 6: Plots Visualisation

Lecture 7: Inferential Statistics

Lecture 8: Probability

Lecture 9: Conditional Probability

Lecture 10: Random Variables

Lecture 11: Normal Probability Distribution

Lecture 12: Central Limit Theorem

Lecture 13: Hypothesis Testing for Decision Making

Chapter 3: Python for Data Science

Lecture 1: Python for Data Science

Lecture 2: Python Installation – Google Collab

Lecture 3: Python Basics

Lecture 4: Identifiers in Python

Lecture 5: Comments in Python

Lecture 6: Python Indentation

Lecture 7: Python Statements

Lecture 8: Variables in Python

Lecture 9: Data Types & Related Stuffs in Python

Lecture 10: Conversion of Data Types in Python

Lecture 11: Python I/O functions

Lecture 12: Output Formatting

Lecture 13: User Input in Python

Lecture 14: Operators in Python

Lecture 15: Control Flow in Python

Lecture 16: Functions in Python

Lecture 17: Types of Functions in Python

Lecture 18: Recursive Functions in Python

Lecture 19: Argument in a Function

Lecture 20: Lambda or Anonymous Functions in Python

Chapter 4: Advance Python

Lecture 1: Advance Programming in Python

Lecture 2: Advance Programming in Python: Part 2

Lecture 3: Data Visualisations

Lecture 4: Bivariate Plotting

Lecture 5: Multivariate Plotting

Chapter 5: Lets dig deeper

Lecture 1: EDA

Lecture 2: EDA on Mcdonalds Data Set

Lecture 3: Exploratory Data Analysis

Chapter 6: Lets Explore in to Machine Learning

Lecture 1: Introduction: Machine Learning

Lecture 2: Unsupervised Learning

Lecture 3: Reinforement Learning

Chapter 7: Module Seven

Lecture 1: Linear Regression

Lecture 2: How to use Linear Regression

Lecture 3: Logistic Regression

Lecture 4: Logistic Regression on Titanic Data Set

Lecture 5: Decision Tree

Lecture 6: Algorithms used in Decision Treee

Lecture 7: Gini Index

Lecture 8: Issues with Decision Tree

Lecture 9: Applications of Decision Tree

Lecture 10: Working on Titanic Data Set

Lecture 11: Random Forest

Lecture 12: Types of Random Forest

Lecture 13: Why Random Forest

Lecture 14: Application of Random Forest

Lecture 15: Random Forest Implementation on Titanic Data Set

Lecture 16: Model Evaluation Technique

Lecture 17: Concept of R-Squared

Lecture 18: Linear Regression

Lecture 19: Classification

Lecture 20: Confusion Matrix

Lecture 21: Recall / Sensitivity / True Rate of Positive

Lecture 22: FB score

Lecture 23: AUC/ ROC curve

Lecture 24: Model Evaluation recall Curve

Chapter 8: Module Eight

Lecture 1: Data Analysis using R

Lecture 2: Data Analysis using R: part 2

Lecture 3: All about R Language

Chapter 9: Featured Topics in Java

Lecture 1: Big Data

Lecture 2: Intro to Hadoop

Lecture 3: Intro to Tableu

Lecture 4: Intro to Business Analytics

Chapter 10: Project: Telecom Churn Production

Lecture 1: Project: Part 1: Lets get our system ready

Lecture 2: Project: part 2

Lecture 3: Project: Part 3

Lecture 4: Project: part 4

Lecture 5: Project: Lets Finalise it

Instructors

Selfcode Academy
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Rating Distribution

1 stars: 12 votes

2 stars: 22 votes

3 stars: 64 votes

4 stars: 157 votes

5 stars: 294 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!