Introduction to Data Science in Python

What is Data Science?

Today the universe has entered the time of massive data and the storage for it also increasing rapidly. Data Science is the field of study for predicting the future. Hence, it’s important to grasp the concept behind Data Science and the way the industries are implementing the solutions for their business problems with the assistance of Data Science.

Before that we must know the data i.e. information and the way to extract the meaningful insights out of the raw data, using different statistical methods, algorithms, and the process. These methods will pull the hidden patterns from the data. The information can be structured or unstructured.

Why Data Science?

The Data science job is the sexiest in the 21st century - at Harvard University. With the assistance of Python, data scientists use scientific methods, processes, and algorithms to extract knowledge, hidden patterns, and insights from many structural and unstructured datasets.

Structured Data Vs Unstructured Data

• Structured data is information that may be recorded easily and might be added to the easy-to-read files without any further exploitation.
• Unstructured data is defined because the data does not exhibit any particular pattern.

Let's inspect some significance of Data Science,

• Using advanced Machine learning algorithms, we can predict the longer term.
• It helps to forestall business losses.
• It helps to identify fraudulent cases.
• It helps to build Artificial Intelligence computing systems.
• Perform sentiment analysis over the customers to measure the loyalty of their organization.
• It builds models to do precise decisions better and faster.
• It helps in recommendations.
• It helps to deliver the right product to the right customer to boost the business

Python for Data Science

Data Science is expounded to Machine Learning, Data Analytics, Data Processing, and Big Data. Nowadays industries are implementing the solutions for their business problems with Data Science. Python programming language is the most popular and powerful language. Around 90 to 95 percent of the industries are using python as a tool to perform the data analysis, because of its high-level features.

Let’s look into the foremost essential features of Python:

• Python language is on the market for Free and Open Source. We can access it from the official website www.python.org. Across the planet, it has a wide community where researchers are working dedicatedly toward new modules and functions.
• Python in comparison to other programming languages is extremely Easy to Understand and Learn as all the syntax and commands are in basic English language.
• It is an Expressive Language, where we can do tasks with few lines of code. As an example, to print a word simply use print("Hello World").
• It is an Interpreted Language, which implies the whole program is executed line by line, one at a time. This makes debugging very easy.
• It's a Cross-Platform Language, python programming is supported on all operating systems like Windows, Linux, UNIX, etc. It is a portable language.
• It’s an Object-Oriented Language, which deals with classes and objects concepts.
• It’s a Functional-Oriented Language, which helps the programmer to write down the code which is reusable and to develop the algorithms and applications in less code.
• It has a Large Standard Library, various machine learning libraries, like Pandas, Numpy, Matplotlib, Seaborn, Sklearn, etc., and deep learning libraries like Tensor Flow, Keras, Pytorch, etc.
• It’s an Embedded Language, where we can use python coding into another programming language and other programming languages can be embedded into python as well.
• It has Dynamic Memory Allocation, where python allocates the memory to the assigned variable at the run time automatically.
• It’s Multithreading, which implies the execution of the program is faster.

How to Install Python?

Python is sometimes not installed on Windows. However, you will be able to check if it’s installed into your system by running one line of command on the command prompt:
python--version

To download Python, you need to check python org, - the official Python website.

Python Libraries for Data Science:

As we discussed, python has a wide range of high-level libraries which is readily available for the ease of Data Scientist. Here we are going to deep dive and understand about top 8 libraries.

1. PANDAS

• Pandas library is a flexible, fast, and powerful library, used for data manipulation.
• It is built on top of the python language.
• It is an open-source library that offers high performance in manipulating and analyzing information.
• Different objects of data are loaded.
• If the data is having missing values [ represented as NaN], it’s easy to handle them with the help of the Pandas library.
• It is mutable, columns can be added and deleted from the data.
• It is flexible in reshaping the data.

The syntax for importing the library;
import pandas as PD

Pandas provide two kinds of data structures,

• Pandas series
• Pandas DataFrame

Pandas Series:

It is a one-dimensional array of objects which holds any kind of data [integer, float, string, etc.]. It is also defined as a column within the dataset. It is accessed with the assistance of indexes.

Pandas DataFrame:

It is a two-dimensional structure of data that is represented in tabular format with labeled rows and columns. In other words, data with a definition is called SCHEMA. DataFrame consists of information, rows, and columns.

2. NumPy

NumPy library is an open source library in python to perform numerical computations on the data. It increases the performance and therefore the execution time is fast on the dataset.

• It performs basic math operations like addition, multiplication, subtraction, reshaping, flattening, and index arrays.
• It performs some advanced computations like stacking arrays, splitting, and broadcasting.
• It works with date, time, or linear algebra.
• It helps in indexing and slicing arrays.

The syntax for importing the library;
import numPy as np

3. SciPy

It is an open-source library that’s built on top of NumPy library. This implies that no need to import NumPy library if SciPy is imported. SciPy library contains packages and modules which is efficient for scientific computations, engineering, and technical computations. It performs tasks like linear algebra, calculus, integration, differential equations, and signal processing.

The syntax for importing the library;
import scipy

4. Matplotlib

It is a low-level Python library which is used to present the insights visually. It generates 2D graphs and plots to represent the data. It creates static, animated, and interactive graphs in python. Let’s scrutinize into different types of plots,

• Bar plot
• Histogram plot
• Scatter plot
• Box plot
• Area plot
• Pie chart
• Pair plot

The syntax for importing the library;
import matplotlib.pyplot as plt

5. Seaborn

Seaborn is a high-level library, that is build on top of matplotlib for advanced visualization of data. It is generally used for statistical plotting. The visualizations are created in a beautiful way for easy interpretation and exploration of information. It offers beautiful styles and color palettes to form the plots more attractive and presentable.

The syntax for importing the library;
import seaborn as sns

6. Scikit Learn(Sklearn)

It is a machine learning library that is built on top of Scipy, NumPy, Matplotlib. It is very useful for data analysis, data processing, and data mining. It is used to implement machine learning models in Python such as Linear Regression, Logistic Regression, Classification, Clustering, Dimension Reduction, Decision Tree, Support Vector Machines, Random Forest and Bayesian Models, Gradient Boosting, etc.

The syntax for importing the library;
import sklearn

7. TensorFlow

It’s an open-source Artificial Intelligence library which helps to develop neural networks with hidden layers. It’s used for numerical calculations which has nodes and edges.

Nodes represent mathematical operations.

Edges represent communication between the multidimensional arrays called as tensors.

In this picture, add represent a node which performs addition operation, a and b are input sensor and c is the output tensor.

It is often used to perform speech recognition, sentiment analysis, face recognition, time series, and forecasting. TensorFlow is categorized into 2 types,

• Low-level API
• High-level API

The syntax for importing the library;
import sklearn

8.Keras

It’s a high level python library used for building the deep neural network. While using keras, statistical analysis and exploring the text data and image data is extremely easy.

It is easy to learn and understand , compact and build on top of TensorFlow framework. It also supports convolutional neural network and recurrent neural network.

The syntax for installing the library;
pip install keras