Data Science with R Programming Training

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Data Science with R Programming Course Details

Zx Academy's Data Science with R programming certification training covers data visualization, data exploration, descriptive analytics techniques, and predictive analytics techniques with the R language. The candidate will learn how to import and export data in R, R packages, various statical concepts, data structures in R, forecasting, and cluster analysis.

The Data Science with R certification training at Zx Academy lets the candidate attain expertise in Machine Learning Algorithms like Decision Trees, K-Means Clustering, Naive Bayes using R, and Random Forest. This course encompasses an understanding of Time Series, Statistics, Introduction to Deep Learning, and Text Mining. During this training, you can learn how to implement real-life scenarios on healthcare, media, HR, aviation, and social media.

Highlights of Zx Academy Training:

  • 24/7 Support throughout the training period
  • Live online classes
  • Training under industry experts
  • Free study material
  • Flexible timings to all our students

What will you learn in Data Science with R Programming Certification training?

After completion of the Data Science with R Programming certification training, you will learn:

  • Introduction to Business Analytics and R Programming
  • Data Visualization
  • Data Structures
  • Statistics for Data Science
  • Clustering
  • Classification
  • Regression Analysis

Who should take this Data Science with R Programming Certification training?

The Data Science with R Programming Certification training course is suited for:

  • Developers aspiring to be a 'Data Scientist'
  • Business Analysts
  • 'R' professionals who wish to work Big Data
  • Analytics Managers
  • Information Architects

What are the prerequisites for taking Data Science with R Programming Certification training?

The prerequisite for taking Data Science with R Programming Certification training is the basic understanding of R programming.

Why should you go for Data Science with R Programming Certification training?

Data Science plays a key role in fields like business analysis that is used to incorporate modeling, computer science, analytics, and statistics. Hence, a candidate must need structured training to take complete benefit from Data Science with R Programming certification. Besides, you have to work on various real-time projects using many from multiple disciplines. The industry experts design the Data Science with R programming certification at Zx Academy.

Salary Trends:

According to Glassdoor, the average salary of an R Systems Data Scientist is Rs.19,00,000 per year.

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Data Science with R Programming Curriculum

What is Data Science?
What does Data Science involve?
Era of Data Science
Business Intelligence vs Data Science
Life cycle of Data Science
Tools of Data Science
Introduction to Big Data and Hadoop
Introduction to R
Introduction to Spark
Introduction to Machine Learning

What is Statistical Inference?
Terminologies of Statistics
Measures of Centers
Measures of Spread
Probability
Normal Distribution
Binary Distribution

Data Analysis Pipeline
What is Data Extraction
Types of Data
Raw and Processed Data
Data Wrangling
Exploratory Data Analysis
Visualization of Data

What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Supervised Learning algorithm: Linear Regression and Logistic Regression

What are classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?
What is Naive Bayes?
Support Vector Machine: Classification

What is Clustering & its use cases
What is K-means Clustering?
What is C-means Clustering?
What is Canopy Clustering?
What is Hierarchical Clustering?

What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Types of Recommendations
User-Based Recommendation
Item-Based Recommendation
Difference: User-Based and Item-Based Recommendation
Recommendation use cases

The concepts of text-mining
Use cases
Text Mining Algorithms
Quantifying text
TF-IDF
Beyond TF-IDF

What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied
Implement respective ETS model for forecasting

Reinforced Learning
Reinforcement learning Process Flow
Reinforced Learning Use cases
Deep Learning
Biological Neural Networks
Understand Artificial Neural Networks
Building an Artificial Neural Network
How ANN works
Important Terminologies of ANN’s

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Projects on Data Science with R Programming

Exploratory Data Analysis (EDA) on a Dataset:

Project Description: For this project, you'll perform Exploratory Data Analysis (EDA) using R. EDA is a crucial part of data science process, helping to unlock understanding about a dataset's structure and relationships among variables.

Steps for Selecting Datasets

Choose Your Dataset (e.g., Housing Prices, Customer Reviews or Climate Data).

Data Import: Utilizing R, you can import the dataset into your working environment.

Data Cleaning: Clean data by managing missing values, eliminating duplicates and changing data types as necessary.

Data Visualization: Create different kinds of plots and visualizations (scatter plots, histograms, bar charts) to explore the characteristics of your data.

Statistical Analysis: Compute summary statistics and conduct basic statistical tests on your data in order to gain new insight.

Narrative and Report: Communicate your findings and insights through a report, including text explanations, visualizations and recommendations derived from your analysis.

Predictive Modeling with Machine Learning:

Project Description: For this project, you'll use R to build a simple predictive model which makes predictions based on an available dataset. Starting out simple regression models may prove effective as starting points for creating this predictive model.

Steps: Select an Appropriate Dataset: Select an appropriate dataset that suits predictive modeling needs - this could include housing prices, sales figures or any other relevant subject area.

Data Preprocessing: Before preprocessing data, handle missing values, encode categorical variables, and divide your dataset into training and testing sets.

Model Selection: Select an appropriate machine learning algorithm (e.g. linear regression for regression tasks), and train it using training data to produce your model.

Model Evaluation: Evaluate the performance of your model on testing data using appropriate metrics (e.g. Mean Absolute Error for regression tasks).

Visualization: Display predictions and performance metrics using scatter plots that compare actual with predicted values.

Model Deployment: Deploy the model (optionally) so it can make predictions on new data.

Report and Interpretation: Provide a summary and interpretation of your modeling process, results, and any insights gained through its exploration.

Project Resources

Data Science with R Programming Certification

A Data Science with R Programming certification formally recognizes your skill in using R for data analysis, visualization, and statistical modeling - something it demonstrates by being used to solve real-world data science problems.

Pursuing a Data Science with R Programming certification can bring many advantages:
Validation of Skills: R validates your data science skills, making you more attractive to employers. Career Advancement: It opens doors for data science roles and potential advancement opportunities.
Expertise: Earning certification demonstrates a firm understanding of Data Science with R, an invaluable skill.

Some well-recognized certifications related to data science and R programming include:
Microsoft Certified: Data Analyst Associate: This certification recognizes your expertise in data analysis, including using R for visualization and statistical analyses.
Machine Learning for Data Science with R: This certification covers machine learning with R as a key aspect of data science.
RStudio Certifications: RStudio offers several certifications focused on R programming for data science, such as tidyverse and Shiny programming environments.

Preparing for a Data Science with R Programming certification typically includes taking these steps:
Study Resources: Take advantage of official study materials, practice exams, and recommended textbooks provided by certifying bodies or training providers when making your studies.
Engage in Hands-On Practice: Strengthen your R skills through real-life data analysis projects using R.
Training Courses: Consider enrolling in either an online or in-person training course designed specifically to prepare you for certification.
Take Practice Exams: Conduct practice exams to gauge your readiness and identify areas requiring further investigation.
Communities and Forums: Join R programming and data science communities and forums in order to learn from others and get assistance when needed.

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Faq’s for Data Science with R Programming

ZX Academy's Data Science with R Programming course equips students with the skills and knowledge required for data analysis, visualization, modeling using R programming language. It covers various aspects of data science from data manipulation to machine learning.

Typically, having some knowledge of statistics and programming experience can be advantageous; however, not always necessary. ZX Academy may offer courses tailored towards both novices and those with prior experience.

Typical curriculum topics of interest for data science courses typically include data preprocessing, visualization, statistical analysis, machine learning using R and data science projects in real life settings. Please check course outline to identify any particular areas that might interest you.

The length of a course depends on several factors, including its format, the number of instructional hours provided and your pace of study. In general, courses usually last several weeks to months to complete.

With the skills acquired through this course, you can pursue careers in data analysis, data science, machine learning and related fields. Roles may include data scientist, data analyst or machine learning engineer among others.

Many Data Science with R Programming courses are designed with beginners in mind, with step-by-step instruction and hands-on exercises designed for effective learning. To be safe, check with ZX Academy about prerequisites and suitable courses before enrolling.

Data science courses often feature practical exercises and projects to enable students to apply their knowledge, such as working with real-world datasets or solving practical issues.

Typically, ZX Academy courses provide access to R programming environments, datasets and recommended books or resources. Please check with them directly regarding what tools and resources are offered.

Yes. ZX Academy and many other training providers provide online data science with R programming courses for global access by students worldwide.

The cost of taking a course varies significantly based on factors like its training provider, course format and any additional materials or support provided. For up-to-date pricing information please contact ZX Academy directly.

Reviews

Data Science with R Programming rated (5.0 / 5) based on 2 reviews.

Anusha

5
It was a very good experience to learn the Data Analytics with R Training from Zx Academy. Faculty members are very knowledgeable and cooperative. Thank you Zx Acadamy

Anusha

5
l'm successfully completed the Data Science with Python Training at Zx Academy. I'm really happy to have found the Zx Academy training course.

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