Learn the basics Data analytics using R Programming

By Dibu

Ph.D. student at IITM - Loves data based projects and tutoring the R programming language
India
Contact Seller
$129.31 Cost
2 days Delivery

Description

After completing this short but crisp course, you will be able to:
  1. Use R programming language with much ease
  2. Easily move on to advanced topics in R
  3. Get a better understanding of data science and machine learning because you can now apply all your theoretical knowledge into real world examples using R!

What seller need from the Buyer to get started?

2 days of your time, 8 hours each. Preferably weekends. And you become an intermediate in R!

You'll find all feedbacks here

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Learn the basics Data analytics using R Programming
by Dibu
Learn the basics Data analytics using R Programmin...

After completing this short but crisp course, you will be able to: Use R programming language with much ease Easily move on...

$129.31

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Python is one of the fastest growing programming languages. It has undergone more than 28 years of the successful span. Python itself reveals its success story and a promising future ahead. Python programming language is presently being used by a number of high traffic websites including Google, Yahoo Groups, Yahoo Maps, Shopzilla, Web Therapy, Facebook, NASA, Nokia, IBM, SGI Inc, Quora, Dropbox, Instagram and Youtube. Similarly, Python also discovers a countless use for creating gaming, financial, scientific and instructive applications.

 

Python is a fast, flexible, and powerful programing language that's freely available and used in many application domains. Python is known for its clear syntax, concise code, fast process, and cross-platform compatibility.

 

Python is considered to be in the first place in the list of all AI and machine learning development languages due to the simplicity. The syntaxes belonging to python are terribly easy and can be easily learn. Therefore, several AI algorithms will be easily implemented in it. Python takes short development time as compared to different languages like Java, C++ or Ruby. Python supports object oriented, functional as well as procedure oriented styles of programming. There are lots of libraries in python that make our tasks easier.

 

Some technologies relying on python:

Python has become the core language as far as the success of following technologies is concerned. Let’s dive into the technologies which use python as a core element for research, production and further developments.

 

  1. Networking: Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.
  2. Big Data: The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.
  3. Artificial Intelligence (AI): There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes. It is only the Artificial Intelligence that has made it possible to develop speech recognition system, interpreting data like images, videos etc.

 

Why Choose Python for Artificial Intelligence and Machine Learning?

Whether a startup or associate MNC, Python provides a large list of benefits to all. The usage of Python is specified it cannot be restricted to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complicated processes like artificial intelligence (AI), Machine Learning (ML), natural language process, data science etc. The question is why Python is gaining such momentum in AI? And therefore the answer lies below:

 

Flexibility: Flexibility is one of the core advantages of Python. With the option to choose between OOPs approach and scripting, Python is suitable for every purpose. It works as a perfect backend and it also suitable for linking different data structures together.

 

Platform agnostic: Python provides developer with the flexibility to provide an API from the existing programming language. Python is also platform independent, with just minor changes in the source codes, you can get your project or application up and running on different operating systems.

 

Support: Python is a completely open source with a great community. There is a host of resources available which can get any developer up to speed in no time. Not to forget, there is a huge community of active coders willing to help programmers in every stage of developing cycle.

 

Prebuilt Libraries: Python has a lot of libraries for every need of your AI project. Few names include Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning.

 

Less Code: Python provides ease of testing - one of the best among competitors. Python helps in easy writing and execution of codes. Python can implement the same logic with as much as 1/5th code as compared to other OOPs languages.

 

Applications of Python:

There are so many applications of Python in the real world. But over time we’ve seen that there are three main applications for Python

Web Development: Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

Data Science (including Machine Learning): Machine Learning with Python has made it possible to recognize images, videos, speech recognition and much more.

Data Analysis/Visualization: Python is also better for data manipulation and repeated tasks. Python helps in the analysis of a large amount of data through its high-performance libraries and tools. One of the most popular Python libraries for the data visualization is Matplotlib.

 

Welcome to Python programming world! We presume you are trying to find information concerning why and how to get started with Python. Fortunately, an experienced coder in any programing language (whatever it's going to be) will pick up Python very quickly. It is also easy for beginners to learn and use.

 

Why you should learn Python

Python is one of the most popular general-purpose programming languages used for both large and small-scale applications. With Python, you can discover how to bridge web development and data analytics. Python’s widespread adoption is due to its large standard library, easy readability, and support of multiple paradigms such as functional, procedural and object-oriented programming styles. Python modules interact with a variety of databases, making it an excellent choice for large-scale data analysis. The Python programming language is often the best choice for introductory courses in data science and machine learning. If you've been wondering how to learn python online to advance your career, you've come to the right destination.

 

A popular Python slogan “life is happier without braces”.

 

Install Python

Installing Python is generally easy, and today several Linux and UNIX system distributions include a recent Python. Even some Windows computers currently go along with Python already installed. If you do need to install Python download from Python official website.

 

Learning Python

Before getting started, you may want to find out which IDEs and text editor are best, IDE usually has plenty of useful features such as autocomplete, debugger and refactoring tools. Some will even check your Python code for little mistakes and encourage best practices through warnings. IDE will help you to find bugs and develop code faster. Learn basics of Python programming and syntax from online Python tutorials.

 

What you need to learn

  1. Python Syntax
  2. String and Console output
  3. Conditionals and control flow
  4. Function
  5. List and Dictionaries
  6. Loops and array
  7. Classes
  8. File input and output
  9. Advanced topic in python

 

Here are some tips to help you make the new concepts you are learning as a beginner programmer:

  1. Code Everyday: Consistency is very important when you are learning a new language. We recommend making a commitment to code every day.
  2. Write it out: As you progress on your journey as a new programmer, you may wonder if you should be taking notes. This will be especially beneficial for those working towards the goal of becoming a full-time developer, as many interviews will involve writing code on a whiteboard.
  3. Go Interactive: Whether you are learning about basic Python data structures (strings, lists, dictionaries, etc.) for the first time, or you are debugging an application, the interactive Python shell will be one of your best learning tools.
  4. Become bug hunter: Once you begin writing complicated programs that you just can run into bugs in your code. It happens to all or any of us! Don’t let bugs frustrate you. Instead, embrace these moments proudly and consider yourself as a bug bounty hunter.
  5. Surround yourself with others: It is extremely important when you are learning to code in Python that you simply surround yourself with others who are learning additionally. This may allow you to share the information and tricks you learn on the approach.
  6. Teach: It is said that the most effective way to learn something is to teach it. This is often true once you are learning Python. There are many ways to try to do this: white boarding with other Python lovers, writing blog posts explaining recently learned ideas, recording videos during which you explain something you learned, or simply talking to yourself at your computer.
  7. Pair program: Pair programming is a technique that involves two developers working to complete a task. The two developers switch between them. One developer writes the code, while other helps guide the problem solving and reviews the code as it is written. Switch frequently to get the benefit of both sides.
  8. Build something: For beginners, there are many small exercises that will really help you become confident with Python.

The global demand for data Science professionals is extremely high because of increasing relevance across various sectors. Data Science has become the most-sought skill because the data is piling along with a surge in different tech fields like Artificial Intelligence, machine learning and data Analytics. Hiring data scientist is being carried across numerous domains like e-commerce, education, retail, telecommunication and much more.

 

In the past years, analysts used excel tools to analyze data. Things are changing now! In this modern world, data-driven decision making is sparkling and technology is advanced in the data industry. The tools and technologies that modern Data Scientists employ are a combination of statistical and Machine Learning algorithms. They are used to discover patterns using predictive models. The future of Data Science is bright and the options for its implementation are extensive.

 

Data Scientists must consistently evolve at the edge of innovation and creativity. They must be aware of the types of models they create. These innovations will allow them to spend time discovering new things that may be of value. Subsequently, the advances in Data Science tools will help leverage existing Data Science talent to a greater extent.

 

So what does a Data Scientist do?

Data Scientists influence a pile of data in an innovative way to discover valuable trends and insights. This approach helps to identify opportunities by implementing research and management tools to optimize business processes by reducing the risks. Data Scientists are also responsible for designing and implementing processes for data mining, research and modeling purposes.

 

Data scientist performs research and analyses data and help companies flourish by predicting growth, trends and business insights based on a large amount of data. Basically, data scientists are massive data wranglers. They take a vast data and use their skills in mathematics, statistics and programming to scrub and organize the information. All their analysis combined with industrial knowledge helps to uncover hidden solutions to business challenges.

 

Generally, a data scientist needs to know what could be the output of the big data he/she is analyzing. He/she also needs to have a clearly defined plan on how the output can be achieved with the available resources and time. Most of all the data scientists must know the reason behind his attempt to analyze the big data.

 

To achieve all of the above, a data scientist may be required to:

 

Every organization has unique data problems with its own complexities. Solving different Data Science problems requires different skill sets. Data Science teams are groups of professionals with varied skill sets. They, as a team, solve some of the hardest data problems an organization might face. Each member contributes distinctive skill set required to complete a Data Science project from start to finish.

 

The Career Opportunities:

The careers associated with data science are generally categorized into five.

 

  1. Statisticians: Statisticians work usually for national governments, marketing research firms and research institutes. Extracting information from massive databases through numerous statistical procedures is what they do.
  2. Data Analyst: Telecommunication companies, manufacturing companies, financial companies etc. hire data scientists to analyze their data. A data analyst keeps track of various factors affecting company operation and make visual graphics.
  3. Big Data and Data Mining Engineer: Tech companies, retail companies and recreation companies use data scientists as data mining engineers. They have to gather and analyze huge amounts of data, typically from unstructured information.
  4. Business Intelligence Reporting Professional: They work for tech companies, financial companies, and consulting companies etc. Market research is the primary objective of this job. They also generate various reports from the structured data to improve the business.
  5. Project Manager: A project manager evaluates data and insights fetched from the operational departments and influences the business decisions. They have to plan the work and make sure everything goes in accordance with the plan.

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