R Programming Training in Pune

Features of R Programming:

The programming language and environment R is strong designed for statistical computing and data analysis. Some of the key features of R programming include:

• Open Source: R is an open-source language, which means it is freely available and accessible to everyone. Users can modify and distribute the code without any cost.
• Statistical Computing: R is specifically designed for statistical analysis, data visualization, and manipulation. It provides a variety of statistical operations, packages for various analytical tasks.
• Rich Ecosystem: R has a vast dynamic user and development community that contributes to a robust ecosystem of packages and extensions. These packages offer specialized tools for various data analysis tasks.
• Data Handling: R provides powerful data structures like vectors, matrices, data frames, and lists, which make data manipulation and handling convenient and efficient.
• Data Visualization: R offers extensive data visualization capabilities through packages like ggplot2, lattice, and plotly. A variety of visualisations can be produced by users for exploratory analysis and presentation.
• Interactivity: R provides interactive features with Shiny, enabling users to build web applications and dashboards with R code.
• Compatibility and Integration: R can interface with other programming languages like C, Python, and Java, allowing for seamless integration with existing systems.
• Community and Support: R has a large and active community, providing abundant resources, documentation, and user support.
• Cross-Platform: R runs on various operating systems, including Windows, macOS, and Linux, ensuring platform independence.
• Free and Open Source: R is free to use and distribute under the GNU General Public License (GPL), making it accessible to users worldwide without any cost barriers.

Current Trends of R programming:

The following are some of the current trends in R programming:

• Data Science and Machine Learning: R remains a Preferred option for machine learning and data science tasks. Its extensive library ecosystem, including caret, tidymodels, and mlr, supports various machine learning algorithms and pipelines.
• Big Data and Parallel Computing: R is adapting to handle big data challenges. Packages like dplyr and data.table offer efficient data manipulation, while libraries like Sparklyr and multidplyr enable distributed computing with Apache Spark.
• Interactivity and Dashboards: R's interactive visualization libraries, like Shiny and plotly, continue to gain traction for creating interactive web applications and dashboards.
• Deep Learning: While R is primarily associated with traditional statistical analysis, the integration of deep learning libraries like Keras and TensorFlow has brought deep learning capabilities to R users.
• Reproducibility and Data Science Notebooks: The adoption of R Markdown and Jupyter notebooks facilitates reproducible research and data storytelling.
• Integration with Python: R and Python integration through reticulate and other tools allows users to leverage the strengths of both languages within a single workflow.
• Geospatial Data Analysis: With libraries such as sf and leaflet, R is becoming a preferred language for geospatial data analysis and visualization.
• Automated Machine Learning (AutoML): The emergence of R packages like automl and H2O AutoML simplifies the process of automating the machine learning model selection and tuning.
• Bioinformatics and Genomics: R is commonly used in bioinformatics and genomics research, with specialized packages supporting tasks like DNA sequence analysis and gene expression profiling.
• Application in Academia and Industry: R continues to be widely used in academia and industries like finance, healthcare, marketing, and social sciences for data analysis, modeling, and decision-making.

Becoming a Data Scientist following R Programming Certification :

Data Scientists ranked first amongst the most promising jobs in the United States in 2019 as per LinkedIn’s report. According to the very report, Data Scientists averaged US$130,000 in the basic salary this year. Moreover, the job vacancies grew by 56 percent from the last year. Currently, there are more than 4,000 Data Science openings across the country. No wonder: Data Scientists are headed to the top as the best job in the United States, thanks to its high demand, high salaries, and high job satisfaction. Also, Analytics India Magazine foretells that the demand for Data Science professionals in India will increase sevenfold in the next seven years and the market will reach US$20 billion.

• Thus, it is a high time you made a change into Data Science by enrolling yourself in a definitive Data Scientist course.
• But first, let’s take a look at the skills and qualifications you need to have in place before jumping the gun.
• Before probing into what the roles and responsibilities are of a Data Scientist, let us take a brief overlook of what companies require from a Data Scientist.

Current Data Scientist Job Requirements :

The following is a rundown of the Data Scientist roles and responsibilities from major job portals, including Indeed and Glassdoor. As a Data Scientist, you should have to :

• Work with stakeholders to learn how to use business data for valuable business solutions
• Search for ways to get distinct data sources and assess their accuracy
• Browse and analyze enterprise databases to clarify and improve product development, marketing techniques, and business processes
• Design custom data models and algorithms
• Use predictive models to enhance customer experience, ad targeting, revenue generation, and more
• Elaborate the organization’s test model quality and A/B testing framework
• Correlate with various technical/functional teams to implement models and monitor results
• Develop processes, techniques, and tools to analyze and observe model performance while ensuring data accuracy

What Data Scientist Qualifications Companies Watch For in a Candidate :

• Data Scientist roles and responsibilities involve identifying business trends and changes through advanced Big Data Analytics and using a variety of techniques to interpret results from multiple data sources through statistical analysis, data aggregation, and data mining.
• This is a very significant role, and most companies expect some or all of the following Data Scientist skills in candidates before hiring them.
• A general inclination toward solving complex problems
• Knowledge/experience on/with statistical programming languages, including R, Python, SLQ, etc., to process data and obtain insights from it
• Experience using and improving data architectures
• Understanding of Machine Learning techniques, including decision tree learning, clustering, artificial neural networks, etc., and their pros and cons
• Knowledge and application expertise in advanced statistical techniques and concepts, including, regression, distribution properties, statistical testing, etc.
• Good communication skills to support cross-team collaboration
• The impulse to learn and study new technologies
• Multilingual coding knowledge/knowledge: Java, JavaScript, C, C++, etc.
• Background/knowledge in statistics and data mining techniques, including, random forest, GLM/regression, social network analysis, text mining, etc.
• Experience with influential web services, including S3, Spark, Redshift, etc.
• Experience/information in distributed data and computing tools, including, MapReduce, MySQL, Hadoop, Spark, Hive, etc.
• Capability to use data visualization tools to showcase data for stakeholders using D3, ggplot, Periscope, and more

What are some Data Scientist roles and responsibilities?

As a Data Scientist, you want to design, develop, and deploy the most relevant solutions for your business and share your results with stakeholders. This requires serving Big Data, implementing relevant data models, and creating databases to support your business solution.

Analytics : Investigating Ways to Infer Relevancy in Data

Leveraging fast-growing data sources that can be utilized for capture and analysis is one of the biggest business challenges that market leaders face today. The amount of data getting generated every day is around 2.5 quintillion bytes and the possibility of relevant data is also huge. Thus, regardless of whether the data is from a machine or other sources, relevant analytics allows you to discover important information that would otherwise be hidden.

According to Indeed, the five most important skills for a Data Scientist posted in their job portal are :

• Machine learning
• Python
• R
• SQL
• Hadoop

Nine out of ten jobs require at least Python, R, and/or SQL as important abilities of a Data Scientist. According to the very report, these skills are closely related to the roles and responsibilities that aspiring Data scientists should learn. Thus, these Data Scientist skills will get you eligible for almost 70% of all online positions in the Data Scientist’s role. Extending your Data Scientist roles and responsibilities exceeding these ​​will allow you to earn higher salaries.

In a data-driven organization, your stakeholders depend on you to help them make decisions. To do this, you need to know what, when, and how to practice which machine algorithms. Your choice will make or break the solution to a business problem. Statistics give the means/tools necessary to drill down into the data and gain valuable insights. The higher statistics you know.

Features of R- Programming :

• R Programming can help you land a high-paying job.
• R is a Statistical Analysis and Data Science lingua franca.
• R is widely utilized by Several Organizations.
• R is a programming language that may be used to create interactive web applications.
• R is a programming language that may be used to create stunning visualizations.
• A Comprehensive Library is provided by R.
• R has a big user base.