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Applied Data Science with R in collaboration with IBM

  1. 4.5
  2. (535 ratings)
  • 300+ Learners
  • English

Applied Data Science with R in collaboration with IBM

  1. $428
    • Best online HR Management course
    • Best online HR Management course
    • Best online HR Management course
    • Best online HR Management course
    300+ Learners
Features
  • 100+ hours of learning
  • Practice Test Included
  • Certificate of completion
  • Skill level

What you'll learn

You will learn to make use of the R language to access databases, clean, analyze, and visualize data with R. Through our guided lectures and access to labs, you will get hands-on experience tackling fascinating data issues. This's an action-packed learning path for data science enthusiasts who wish to work on real-life problems with R.

Universally Recognized Certificates

From IBM and DataTrained

Capstone and Real Life Projects

Access to 15 real life projects and a capstone project

Analytics Jobs Placement Assistance

Access to analyticsjobs.in curated jobs

Access to in-demand Tools

IBM Watson labs and $1200 equivalent Cloud Credits

Programming Languages and Tools Covered

Detailed Syllabus of Applied Data Science with R Course

R is actually an effective language for data analysis, data visualization, machine learning, stats. Initially created for statistical programming, it's currently one of the most favored languages in data science.

With this program, you will be to learn about the fundamentals of R, and you will end with the confidence to begin writing your very own R scripts. But this is not your normal textbook launch to R. You are not only understanding about R fundamentals, but you will also be using R to resolve problems related to films data. A concrete case can make learning painless. You are going to learn about the basic principles of R syntax, which includes assigning variables and doing small activities with R's most crucial details structures -- vectors! From vectors, you will then learn about data frames, arrays, matrix, and lists. After that, you will leap into conditional statements, functions, debugging and classes. As soon as you have covered the fundamentals - you will find out about reading and writing data in R, whether it is a table format(CSV, Excel) or maybe a text file (.txt). Lastly, you will end with a few crucial functions for character strings as well as dates in R.

Course Content
Module 1 - R basics
  • Math, Variables, and Strings
  • Vectors and Factors
  • Vector operations
Module 3 - R programming fundamentals
  • Conditions and loops
  • Functions in R
  • Objects and Classes
  • Debugging
Module 4 - Working with data in R
  • Reading CSV and Excel Files
  • Reading text files
  • Writing and saving data objects to file in R
Module 2 - Data structures in R
  • Arrays & Matrices
  • Lists
  • Dataframes
Module 5 - Strings and Dates in R
  • String operations in R
  • Regular Expressions
  • Dates in R

Data is among the most crucial assets of a company. Data must have a database to store and procedure information quickly. SQL is actually a language for a database to query information.

In this introductory program, you will master the fundamentals of the SQL language and also relational databases. You will start by understanding the relational style as well as relational model principles and constraints. By the end of this particular program, you'll have mastered and used the 5 fundamental SQL statements, several innovative SQL syntaxes, as well as join statements. This is not your normal textbook introduction. You are not only learning through lectures. At the conclusion of each module, you will find assignments, hands-on exercises, review concerns, and additionally a final examination. Successfully completing this program earns you a certificate. Why don't we get started!

Course Content
Module 1 -SQL and Relational Databases 101
  • Introduction to SQL and Relational Databases
  • Information and Data Models
  • Types of Relationships
  • Mapping Entities to Tables
  • Relational Model Concepts
Module 3 - Data Definition Language (DDL) and Data
  • Manipulation Language (DML)
  • CREATE TABLE statement
  • INSERT statement
  • SELECT statement
  • UPDATE and DELETE statements
Module 5 - Working with multiple tables
  • Join Overview
  • Inner Join
  • Left Outer Join
  • Right Outer Join
  • Full Join
Module 2 - Relational Model Constraints and Data Objects
  • Relational Model Constraints Introduction
  • Relational Model Constraints Advanced
Module 4 - Advanced SQL
  • String Patterns, Ranges, and Sets
  • Sorting Result Sets
  • Grouping Result Sets

It is going to introduce you to the profits of utilizing R with databases. Teach you exactly how to link directories from R. Teach you exactly how to make database items, populate the data source, as well as issue SQL queries to access and alter the data of yours from R. The program will even delve into complex subjects of using saved methods and utilizing in-database analytics with R

Course Content
Module 1 - R and Relational Databases Module 3 - Database Design and Querying Data Module 5 - In-Database Analytics with R
Module 2 - Connecting to Relational Databases using RJDBC and RODBC Module 4 - Modifying Data and Using Stored Procedures
Course Content
Module 1 - Basic Visualization Tools
  • Bar Charts
  • Histograms
  • Pie Charts
Module 3 - Specialized Visualization Tools
  • Word Clouds
  • Radar Charts
  • Waffle Charts
  • Box Plots
Module 5 - How to build interactive web pages
  • Introduction to Shiny
  • Creating and Customizing Shiny Apps
  • Additional Shiny Features
Module 2 - Basic Visualization Tools Continued
  • Scatter Plots
  • Line Plots and Regression
Module 4 - How to create Maps
  • Creating Maps in R

Comprehensive Curriculum

The curriculum has been designed by faculty from IITs, IBM and Expert Industry Professionals.

100+ Hours of Content--- best institute for data science lucknow
100+

Hours of Content

80+ Live Sessions--  data science training lucknow
80+

Live Sessions

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Instructors

Learn from India’s leading Software Engineering faculty and Industry leaders

Dr. Deepika Sharma - Training Head, DataTrained

Dr. Deepika Sharma

Training Head, DataTrained

Research Scientist with a PhD in computer science and 14 years of hands-on experience.

Hima Vasudevan - Business Analyst, IBM

Hima Vasudevan

Business Analyst, IBM

Hima Vasudevan is a Data Scientist based out of the Chicago office. She is part of the Emerging Technologies team and focuses on developing course content.

Rav Ahuja - Senior Manager, IBM

Rav Ahuja

Senior Manager, IBM

Rav Ahuja is a Senior Manager with IBM Canada Lab specializing in AI, Data Science and Big Data analytics. He is part of the Emerging Technologies team and is involved in incubating solutions for Data Scientists and Analytics Professionals.

Raul F. Chong- Senior Program Manager, IBM

Raul F. Chong

Senior Program Manager, IBM

Raul F. Chong is a Senior Program Manager based at the IBM Toronto Laboratory. Raul joined IBM in 1997 and has held numerous positions in the company. Raul has taught many DB2 workshops, has published numerous articles, and has contributed to the DB2 Certification exam tutorials.

Grant Hutchison - Senior Engineer, IBM

Grant Hutchison

Senior Engineer, IBM

Grant Hutchison had worked with IBM for 18 years as Senior Engineer and Manager at IBM Canada (1991-2009). He held various roles including: Software Development, Support, Quality Assurance, Marketing, Sales, Training, and Product Management.

Frequently Ask Questions

Data Science is an approach to gain knowledge and deep insights from raw data through the application of statistics, mathematics, and computer science. Statistics is paramount to data science. That’s where R comes into play.

R is an open-source language and environment for statistical analysis and graphics. R is specifically designed for statistical analysis purposes. If you are interested in learning data science then R is for you. Few important things to know about R:

  • R is an open-source program, it is both free and customizable. R's open interfaces allow it to work with a variety of different programs and systems. it has a high-quality standard.
  • R is a programming language that allows users to explore, model, and visualize data.
  • R is a programming language that is used to analyze data. R is a programming language that is used in data science to manage, store, and analyze data. It can be used for statistical modeling and data analysis.

Python and R are both regarded to be quite simple to learn. Python was developed for software development. Python may come more readily to you than R if you have prior familiarity with Java or C++. R, on the other hand, may be a little easier only if you have a strong foundation in statistics.

Generally, Python's easy-to-understand syntax makes it easier to learn. R has a steeper learning curve at first, but it becomes substantially easier as users learn to use its capabilities.

We provide the best Certificate Course On Applied Data Science With R, therefore, you don’t have to think about whether R is hard to learn. Our curriculum and faculty make all the concepts digestible. If you have any questions, contact us, we would be happy to guide you.

We have witnessed how data is revolutionizing the world. Everyone should adapt to new technologies, otherwise, it will result in the fall of that entity. For instance, Nokia failed to innovate.

Applied data science certification opens the door for future opportunities. It not only helps you in your career but also upskills you for the future. Here is the list of a few careers in data science where you can apply data science:

  • Data Scientist
  • Data Analyst
  • Statistician
  • Data Engineering
  • Machine learning Engineering

Our IBM data science professional certificate will significantly add weight to your resume. It will help you to upskill and make you ready for the future of data science.

For more information on the importance of data science, click here.

R with data science is an approach to extracting knowledge from data using the R programming language. R is a language made purely for statistical analysis. R environment is an integration of objects for data manipulation and data analysis.

R environment includes:

  • Data handling and storing
  • Many operators for calculations
  • A large collection of modules and tools for data analysis
  • Tools for graphical analysis

Data science and applied data science are not similar. Although it’s easy to use these words interchangeably. Let’s understand the difference in brief

Data science
  • Data mining is a focused part of data science
  • Data Visualization is an integral element of data science
  • Database management is another important aspect of data science that includes data cleaning, data transformation, data manipulation, etc.
Applied data science
  • Research for new algorithms
  • Research of ways to apply data science into multiple disciplines
  • Optimization of predictive modeling to make it more accurate and efficient

R is an open-source programming language and python is too. Both languages are significant in the world of data science. It’s important to know what programming languages will be effective to use in Data Science.

Python is a high-level programming language and it is the most popular programming language. There are a lot of libraries and modules in python. It is used across multiple disciplines :

  • Web development
  • Designing
  • Machine learning and AI
  • Finance
  • Data Analytics & Visualization
  • Game Development, so on

R is also an open-source language like python, However, python is more specific to building statistical models. It depends on your goals and objectives. If you want to build a predictive model, in that case, Python is better. If you want to do data manipulation and exploration, then R would be your best choice.

No, programming is not hard, although, it has a steeper learning curve that means, at first, many people find it difficult to learn to code. However, after learning the basics of coding, they also find it very fun and interesting. Coding and programming are like learning a new language. Since code is a language too.

IBM data science certification course has been designed by faculty from IITs, IBM, and Expert Industry Professionals. Therefore, you can go ahead and add remarkable credentials with these certifications.

For more information on coding, click here

An applied data scientist responsible for researching the application of data science into the different multiples. Not only he needs to find answers to where he can apply data science but also how it can be applied. Data science is the future and its increasing rapidly. According to the Forbes report, we have generated 90% of data in the last two years.

Data is fuel for industries and data science. Let's see the applications of data science:

  • Banking
  • Business
  • Transport
  • Healthcare
  • Manufacturing
  • E-commerce

For more detailed information, click here.

Yes, you can become an applied data scientist with an online course. Data science is easy to learn online since all you need is a computer. Other benefits of the online course include:

  • Flexible Scheduling
  • Affordability
  • Ability to progress in a profession
  • Being able to study at your own speed is a valuable skill
  • No commute

You’ll learn multiple tools and software with the programming language R. You will learn to make use of the R language to access databases, clean, analyze, and visualize data with R.

Programming and tools covered in this course will be

  • NLP
  • PERL
  • SQL
  • Excel
  • Python
  • MySql

Our IBM data science program curriculum has been designed by faculty from IITs, IBM, and Expert Industry Professionals. Our mission is to provide quality education at an affordable cost with the best counseling sessions and industry interface.

Pathway of learning IBM data science program:

  • R for Data science
  • SQL and Relational database
  • Using R with Database
  • Data Visualization with R