**Data Science** is a process to extract insight from the data using Feature Engineering, Feature Selection, Machine Learning, etc. to solve the real-world business problem.

Data science has an intersection with artificial intelligence but is not a subset of artificial intelligence.

Data Science is the Art and Science of drawing actionable insights from the data.

**Applications:**

Retail, Bank, E-Commerce, Healthcare, and Telecom, etc.

## Python Library for Data Science

To solve the business problem using **Data Science** for that data gathering, cleaning and visualization must be done. For that bellow python library, you should learn first.

### NumPy

### Matplotlib

**Python Matplotlib Tutorial****Matplotlib Line Plot****Matplotlib Histogram****Matplotlib Bar Chart****Matplotlib Pie Chart****Matplotlib Scatter Plot****Matplotlib Subplot****Matplotlib Save Figure****Matplotlib Image Show**- ……
- Course in progress …

### Feature Engineering

**Best 6 Methods to Handling Missing Values/Data Smartly – Data Cleaning****One Hot Encoding & Dummy Variables | Categorical Variable Encoding****Feature Scaling – Standardization vs Normalization****Label Encoding vs Ordinal Encoding | Categorical Variable Encoding**

### Machine Learning Project

**Machine Learning Project End to End: Student Mark Prediction****Directing Customers to Subscription Through Financial App Behavior Analysis****Breast Cancer Detection Using Machine Learning Classifier****House Prices Prediction Advanced Regression Techniques | Kaggle Competition****Bangalore House Price Prediction Machine Learning Project till Deployment**

### Seaborn

**Python Seaborn Tutorial****Seaborn Line Plot****Seaborn Histogram****Seaborn Barplot****Seaborn Scatter Plot****Seaborn Heatmap****Seaborn Pairplot**- ……
- Course in progress …

### Statistics for Machine Learning

### Machine Learning Algorithms

**Linear Regression****Root Mean Square Error****Ridge and Lasso Regression****Polynomial Linear Regression****Support Vector Regression****Support Vector Machine (SVM) Classification****Decision Tree Classification****Decision Tree Regression****Random Forest Classification****Random Forest Regression****K Nearest Neighbor Classification****K Nearest Neighbor Regression****Naïve Bayes Classifier****Save & Load Machine Learning Model**

It would be great if you could build a blog section for users like, so that they can ask their questions and problems.

I am a beginner so this will be very helpful for me as you teaching style is very different from others.

Also, I would like to know some interview questions with practical.

Nice tutorial, it is very usefull for beginner…

Thanks Faiz

Very good! simple and understandable..It would be great if you could build with completeness.

Hi sir Thank you for making just amazing YouTube channel and website . I have a question after getting knowledge of Numpy ,Pandas , matplotlib, seaborn, i am become a data Analyst. ?

how can join the tutorial

visite our youtube channel https://www.youtube.com/indianaiproduction

Great sir!

it’s good effort …. keep it up.

it’s really help full for me thanks.

can i got certificate from your institute?

i am from pakistan.

We are not running institute