Data Science Tutorial – Excellence Technology

What is Data Science?

Data Science is a combination of mathematics, statistics, machine learning, and computer science. Data Science is collecting, analyzing and interpreting data to gather insights into the data that can help decision-makers make informed decisions.

Data Science is used in almost every industry today that can predict customer behavior and trends and identify new opportunities. Businesses can use it to make informed decisions about product development and marketing. It is used as a tool to detect fraud and optimize processes. Governments also use Data Science to improve efficiency in the delivery of public services.

In simple terms, Data Science helps to analyze data and extract meaningful insights from it by combining statistics & mathematics, programming skills, and subject expertise.

Importance of Data Science

Nowadays, organizations are overwhelmed with data. Data Science will help in extracting meaningful insights from that by combining various methods, technology, and tools. In the fields of e-commerce, finance, medicine, human resources, etc, businesses come across huge amounts of data. Data Science tools and technologies help them process all of them.

History of Data Science

Early in the 1960s, the term “Data Science†was coined to help comprehend and analyze the massive volumes of data being gathered at the time. Data science is a discipline that is constantly developing, employing computer science and statistical methods to acquire insights and generate valuable predictions in a variety of industries.

Data Science – Prerequisites

  • Statistics

Data science relies on statistics to capture and transform data patterns into usable evidence through the use of complex machine-learning techniques.

Check out statistics for Data Science to learn key concepts of Statistics in Data Science, Machine Learning, and Business Intelligence.

  • Programming

Python, R, and SQL are the most common programming languages. To successfully execute a data science project, it is important to instill some level of programming knowledge.

  • Machine Learning

Making accurate forecasts and estimates is made possible by Machine Learning, which is a crucial component of data science. You must have a firm understanding of machine learning if you want to succeed in the field of data science.

  • Databases

A clear understanding of the functioning of Databases, and skills to manage and extract data is a must in this domain. 

  • Modeling

You may quickly calculate and predict using mathematical models based on the data you already know. Modeling helps in determining which algorithm is best suited to handle a certain issue and how to train these models.

What is Data Science used for?

  • Descriptive Analysis

It helps in accurately displaying data points for patterns that may appear that satisfy all of the data’s requirements. In other words, it involves organizing, ordering, and manipulating data to produce information that is insightful about the supplied data. It also involves converting raw data into a form that will make it simple to grasp and interpret.

  • Predictive Analysis

It is the process of using historical data along with various techniques like data mining, statistical modeling, and machine learning to forecast future results. Utilizing trends in this data, businesses use predictive analytics to spot dangers and opportunities.

  • Diagnostic Analysis

It is an in-depth examination to understand why something happened. Techniques like drill-down, data discovery, data mining, and correlations are used to describe it. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques. 

  • Prescriptive Analysis

Prescriptive analysis advances the use of predictive data. It foresees what is most likely to occur and offers the best course of action for dealing with that result. It can assess the probable effects of various decisions and suggest the optimal course of action. It makes use of machine learning recommendation engines, complicated event processing, neural networks, simulation, graph analysis, and simulation.

What is the Data Science process?

  • Obtaining the data

The first step is to identify what type of data needs to be analyzed, and this data needs to be exported to an excel or a CSV file.

  • Scrubbing the data

It is essential because before you can read the data, you must ensure it is in a perfectly readable state, without any mistakes, with no missing or wrong values.

  • Exploratory Analysis

Analyzing the data is done by visualizing the data in various ways and identifying patterns to spot anything out of the ordinary. To analyze the data, you must have excellent attention to detail to identify if anything is out of place.

  • Modeling or Machine Learning

A data engineer or scientist writes down instructions for the Machine Learning algorithm to follow based on the Data that has to be analyzed. The algorithm iteratively uses these instructions to come up with the correct output.

  • Interpreting the data

In this step, you uncover your findings and present them to the organization. The most critical skill in this would be your ability to explain your results.

What are different Data Science tools?

Here are a few examples of tools that will assist Data Scientists in making their job easier.

  1. Data Analysis – Informatica PowerCenter, Rapidminer, Excel, SAS
  2. Data Visualization – Tableau, Qlikview, RAW, Jupyter
  3. Data Warehousing – Apache Hadoop, Informatica/Talend, Microsoft HD insights
  4. Data Modelling – H2O.ai, Datarobot, Azure ML Studio, Mahout

Applications of Data Science

  • Product Recommendation

The product recommendation technique can influence customers to buy similar products. For example, a salesperson of Big Bazaar is trying to increase the store’s sales by bundling the products together and giving discounts. So he bundled shampoo and conditioner together and gave a discount on them. Furthermore, customers will buy them together for a discounted price.

  • Future Forecasting

It is one of the widely applied techniques in Data Science. On the basis of various types of data that are collected from various sources weather forecasting and future forecasting are done. 

  • Fraud and Risk Detection

It is one of the most logical applications of Data Science. Since online transactions are booming, losing your data is possible. For example, Credit card fraud detection depends on the amount, merchant, location, time, and other variables. If any of them looks unnatural, the transaction will be automatically canceled, and it will block your card for 24 hours or more.

  • Self-Driving Car

The self-driving car is one of the most successful inventions in today’s world. We train our car to make decisions independently based on the previous data. In this process, we can penalize our model if it does not perform well. The car becomes more intelligent with time when it starts learning through all the real-time experiences.

  • Image Recognition

When you want to recognize some images, data science can detect the object and classify it. The most famous example of image recognition is face recognition – If you tell your smartphone to unblock it, it will scan your face. So first, the system will detect the face, then classify your face as a human face, and after that, it will decide if the phone belongs to the actual owner or not.

  • Speech to text Convert

Speech recognition is a process of understanding natural language by the computer. We are quite familiar with virtual assistants like Siri, Alexa, and Google Assistant. 

  • Healthcare

Data Science helps in various branches of healthcare such as Medical Image Analysis, Development of new drugs, Genetics and Genomics, and providing virtual assistance to patients. 

  • Search Engines

Google, Yahoo, Bing, Ask, etc. provides us with a lot of results within a fraction of a second. It is made possible using various data science algorithms.

Data Science With Python

This data science with Python tutorial will help you learn the basics of Python along with different steps of data science according to the need of 2023 such as data preprocessing, data visualization, statistics, making machine learning models, and much more with the help of detailed and well-explained examples. This tutorial will help both beginners as well as some trained professionals in mastering data science with Python.

Why Python is Important For Data Science?

Python has been in demand for the past few years and the recent survey also suggested the same, Python leads the chart among the top programming languages in both the TIOBE index & PYPL Index. However, to support this, there are 5 concrete reasons behind this,

  1. Easy To Learn: Being an open-source platform, Python has a simple and intuitive syntax that is easy to learn and read. This makes it a great language for beginners to learn data science.
  2. Cross-Platform: Being a developer, you don’t need to worry about the data types. The reason is, Python allows developers to run the code on Windows, Mac OS X, UNIX, and Linux.
  3. Portable: Being an easy & beginner’s friendly programming language, Python is highly portable in nature which means that a developer can run their code on different machines without making any further changes.
  4. Extensive Library: Python has several powerful libraries that make data analysis and visualization easy. Pandas is a library for data manipulation and analysis, NumPy is a library for numerical computation, and Matplotlib is a library for data visualization.
  5. Community Support: Python has a large and active community that supports and contributes to the development of various libraries and tools for data science. This community has created many useful libraries, including Pandas, NumPy, matplotlib, and SciPy, which are widely used in data science.

However, there are a lot more reasons to opt for Python for Data Science such as OOP, expressive language, the ability to allocate memory dynamically, etc. and that’s the reason for using Python Programming Language for Data Science applications.

R Programming for Data Science

Data Science has emerged as the most popular field of the 21st century. This is because there is a pressing need to analyze and construct insights from the data. Industries transform raw data into furnished data products. In order to do so, it requires several important tools to churn the raw data. R is one of the programming languages that provide an intensive environment for you to research, process, transform, and visualize information. 

Features of R – Data Science

Some of the important features of R for data science applications are: 

  • R provides extensive support for statistical modeling.
  • R is a suitable tool for various data science applications because it provides aesthetic visualization tools.
  • R is heavily utilized in data science applications for ETL (Extract, Transform, Load). It provides an interface for many databases like SQL and even spreadsheets.
  • R also provides various important packages for data wrangling.
  • With R, data scientists can apply machine learning algorithms to gain insights about future events.
  • One of the important features of R is to interface with NoSQL databases and analyze unstructured data.

Most common R Libraries in Data Science

  • Dplyr: For performing data wrangling and data analysis, we use the dplyr package. We use this package for facilitating various functions for the Data frame in R. Dplyr is actually built around these 5 functions. You can work with local data frames as well as with remote database tables. You might need to: 
    Select certain columns of data. 
    Filter your data to select specific rows. 
    Arrange the rows of your data in order. 
    Mutate your data frame to contain new columns. 
    Summarize chunks of your data in some way.
  • Ggplot2: R is most famous for its visualization library ggplot2. It provides an aesthetic set of graphics that are also interactive. The ggplot2 library implements a “grammar of graphics†(Wilkinson, 2005). This approach gives us a coherent way to produce visualizations by expressing relationships between the attributes of data and their graphical representation.
  • Esquisse: This package has brought the most important feature of Tableau to R. Just drag and drop, and get your visualization done in minutes. This is actually an enhancement to ggplot2. It allows us to draw bar graphs, curves, scatter plots, and histograms, then export the graph or retrieve the code generating the graph.
  • Tidyr: Tidyr is a package that we use for tidying or cleaning the data. We consider this data to be tidy when each variable represents a column and each row represents an observation.
  • Shiny: This is a very well-known package in R. When you want to share your stuff with people around you and make it easier for them to know and explore it visually, you can use Shiny. It’s a Data Scientist’s best friend.
  • Caret: Caret stands for classification and regression training. Using this function, you can model complex regression and classification problems.
  • E1071: The E1071 package has wide use for implementing clustering, Fourier Transform, Naive Bayes, SVM, and other types of miscellaneous functions.
  • Mlr: This package is absolutely incredible in performing machine learning tasks. It almost has all the important and useful algorithms for performing machine learning tasks. It can also be termed as the extensible framework for classification, regression, clustering, multi-classification, and survival analysis.

Other worth mentioning R libraries: 

  1. Lubridate
  2. Knitr
  3. DT(DataTables)
  4. RCrawler
  5. Leaflet
  6. Janitor
  7. Plotly

Applications of R for Data Science

Top Companies that Use R for Data Science: 

  • Google: At Google, R is a popular choice for performing many analytical operations. The Google Flu Trends project makes use of R to analyze trends and patterns in searches associated with flu.
  • Facebook makes heavy use of R for social network analytics. It uses R for gaining insights about the behavior of the users and establishes relationships between them.
  • IBM: IBM is one of the major investors in R. It recently joined the R consortium. IBM also utilizes R for developing various analytical solutions. It has used R in IBM Watson – an open computing platform.
  • Uber: Uber makes use of the R package shiny for accessing its charting components. Shiny is an interactive web application that’s built with R for embedding interactive visual graphics.