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Data Science 2021 : Complete Data Science

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.


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?

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,

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“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.



Getting Started with Data Science
Basic Maths Required for Data Science
Let’s Start with Statistics
Data Quality Issues
Types of Statistics
Measures of Spread
Measures of Shapes
Plots Visualisation
Inferential Statistics
Conditional Probability
Random Variables
Normal Probability Distribution
Central Limit Theorem
Hypothesis Testing for Decision Making
Python for Data Science
Python for Data Science
Python Installation – Google Collab
Python Basics
Identifiers in Python
Comments in Python
Python Indentation
Python Statements
Variables in Python
Data Types & Related Stuffs in Python
Conversion of Data Types in Python
Python I/O functions
Output Formatting
User Input in Python
Operators in Python
Control Flow in Python
Functions in Python
Types of Functions in Python
Argument in a Function
Recursive Functions in Python
Lambda or Anonymous Functions in Python
Advance Python
Advance Programming in Python
Advance Programming in Python: Part 2
Data Visualisations
Bivariate Plotting
Multivariate Plotting
Let’s dig deeper
EDA on Mc’donalds Data Set
Exploratory Data Analysis
Let’s Explore in to Machine Learning
Introduction: Machine Learning
Unsupervised Learning
Reinforement Learning
Module Seven
Linear Regression
How to use Linear Regression
Logistic Regression
Logistic Regression on Titanic Data Set
Decision Tree
Algorithms used in Decision Treee
Gini Index
Issues with Decision Tree
Applications of Decision Tree
Working on Titanic Data Set
Random Forest
Types of Random Forest
Why Random Forest
Application of Random Forest
Random Forest Implementation on Titanic Data Set
Model Evaluation Technique
Concept of R-Squared
Linear Regression
Confusion Matrix
Recall / Sensitivity / True Rate of Positive
FB score
AUC/ ROC curve
Model Evaluation recall Curve
Module Eight
Data Analysis using R
Data Analysis using R: part 2
All about R Language
Featured Topics in Java
Big Data
Intro to Hadoop
Intro to Tableu
Intro to Business Analytics
Project: Telecom Churn Production
Project: Part 1: Let’s get our system ready
Project: part 2
Project: Part 3
Project: part 4
Project: Let’s Finalise it