Data Science Tutorials:

Take this course and master machine learning algorithms and data science.

Tutorial #0 | Getting started with data science

Learn how to start your career in data science by installing important tools. click here

Python Crash Course:

Tutorial #1.1 | Basics of Python

In this tutorial basics of the python related to the data science are explained in brief. Read More…

Tutorial #1.2 | Lists in Python

This tutorial is an overview of data structure ( list ) in Read more

Tutorial #1.3 | Dictionaries and tuples

Read about Dictionaries and tuples.

Tutorial #1.4 | Booleans and sets

Boolean are the expression such as True, False. These are used to test the data whether it is correct or wrong. Read more >>

Tutorial #1.5 | Iterations

To know all about loops in python click here >>>

Tutorial #1.6 | Functions

Function defining in python and more about functions. read more>>

Numpy:

Tutorial #2.0 | Introduction to numpy

It is a library in python that holds linear algebra. Read more

Tutorial #2.1 | Indexing and slicing

Indexing and slicing in numpy is similar to indexing and slicing in lists. Read more

Pandas:

Tutorial #3.0 | Introduction to pandas

This is a very powerful tool for data scientist , this tool is used to manipulate data like filling the missing values , filtering , sorting and many other operations on CSV , EXCEL , HTML and other files. Read more

Tutorial #3.1 |operations using pandas

This would be our data frame for the operations that will be doing in this tutorial, until unless we define some other data frame. Read more

Tutorial #3.2 | Operations using pandas

In jupyter notebook we can read and edit many types of files like excel files, csv files, html files and many more files. Read more

Matplot library:

Tutorial #4.0 | Matplotlib

This is a plotting library for python. If you are familiar with plotting the figures in MatLab then these Matplotlib tutorials would be very easy for us. Let us understand how to import this library in the jupyter notebook.Read more

Seaborn:

Tutorial #5.0 | Introduction to seaborn

This is a library for statistical plotting , built upon the matplot library ( which means matplot is the base to learn seaborn ) . Read more

Tutorial #5.1 | Pair plot

This plot function plots with all possible columns present in the data set. In this tutorial also we would be working with the same data set . Read more

Tutorial #5.2 | Categorical plots

Like numerical values which possess numbers, categorical values possess categories such as gender ( contains two categorical values ) , year ( contains 12 categorical values ). Read more

Tutorial #5.3 | Categorical distribution plots

In this tutorial we would learn all three types of categorical distribution plots. Read more

Tutorial #5.4 | Linear model plots

Datasets contains many quantitative analysis, and relating those quantities is one of the most important part to analyze the data, and these linear model’s plot can be analyzed by using two types of plots.Read more

Tutorial #5.5 | Estimation and heat plots

This plot takes the central tendency of occurrence of that plot of particular category, This plot is better than barplot for comparing central occurrence tendency. Read more

Pandas builtin visualizations:

Tutorial #6.0 | Pandas builtin data visualizations

Without importing matplot or seaborn library, we can plot using pandas builtin functions. Read more

Plotly:

Tutorial #7.0 | Plotly and cufflinks

Plotly is a very powerful library tool to visualize data plots in a very interactive way, we can plot 3d surfaces and zoom in and out those plots using plotly . Read more

Machine learning:

Tutorial #8.0 | Introduction to ML

Basically we train data to predict future unknown values using various methods. Read more

Tutorial #8.1 | Linear regression

Linear regression is building a model that predicts one variable dependent on other variables that are dependent on each other. . Read more

Tutorial #8.2 | Logistic regression | Part-1

This regression is used to solve classification problems, It predicts discrete values. We cannot use linear regression to build a model on classification problems. Read more

Tutorial #8.3 | Logistic regression | Part-2

Building a classification model. Read more

Tutorial #8.4 | Knn

K Nearest Neighbors is an algorithm that solves classification problems in a very simple and easy process. Read more

Tutorial #8.5 | Knn | model building

In this tutorial we would built our Knn machine learning model. Read more

Tutorial #8.6 | Decision trees and random forests | Part-1

This is a type of decision making algorithm that builds a tree like model.Read more

Tutorial #8.7 | Decision trees and random forests | Part-2

Building a machine learning model based on decision trees and random forests: Read more

Tutorial #8.8 | SVM

This is a kind of supervised machine learning model with associated learning algorithms that analyzes the data and then predicts the data pattern. Read more

Tutorial #8.9 | K means clustering

This is an unsupervised machine learning algorithm ( which means it has no labeled data ). This will attempt to group similar clusters together. Read more

Tutorial #8.10 | Recommendar systems

The best example of the recommender system is “ amazon recommends us products based on other customer ratings ” Read more

SQL

#1.0 | Introduction to Database Management System

In this tutorial Introduction to Database Management is explained. Read More…

#1.1 | Data Abstraction, Users, Models and Languages

In this tutorial different data abstraction, models, users and languages are discussed. Read More…

#1.2 | Types of Keys

There are many keys associated with database management. Read More…

#1.3 | Maping Cardinalities, Relationship, ER-Diagram

In this tutorial about the maping cardinalities, relationships and ER- Diagram is discussed. Read More…