Data analytics using R Course

|| Data Analytics Using R Certification from BIT

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|| What will I learn?

Learn how to manipulate, clean, and preprocess data using R.
Develop practical skills in data analytics through hands-on exercises and projects.
Understand best practices and tips for effective data analysis using R.

Explore data visualization principles and techniques in R.
Participants will gain exposure to advanced topics in R, including machine learning concepts and techniques, working with big data, and optimizing R code and workflows for efficiency.

|| What will I learn?

Learn how to manipulate, clean, and preprocess data using R.
Develop practical skills in data analytics through hands-on exercises and projects.
Understand best practices and tips for effective data analysis using R.
Explore data visualization principles and techniques in R.
Participants will gain exposure to advanced topics in R, including machine learning concepts and techniques, working with big data, and optimizing R code and workflows for efficiency.

|| Requirements

Basic understanding of statistics and data analysis concepts.

No prior experience with R is necessary.

|| Requirements

Basic understanding of statistics and data analysis concepts.
No prior experience with R is necessary.

|| Significant Scope of Data Analytics

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The "Data Analytics using R" course offers a comprehensive introduction to data analytics with a focus on leveraging the R programming language. Starting with the basics of R and the RStudio environment, participants will learn essential programming skills, including writing scripts and understanding data types. The course then delves into data manipulation using the powerful dplyr package, enabling students to efficiently import, clean, and transform data. Visualization skills are developed using ggplot2, teaching learners to create and customize a variety of plots to effectively communicate data insights. Statistical analysis is a key component, covering both descriptive and inferential statistics, such as hypothesis testing and ANOVA. The course also emphasizes exploratory data analysis (EDA) techniques to uncover patterns and anomalies in data. Predictive modeling is introduced with a focus on regression and classification models, while time series analysis covers forecasting methods. Advanced topics include clustering and dimensionality reduction techniques. Throughout the course, students engage in hands-on projects that simulate real-world data challenges, culminating in the ability to create reproducible reports using RMarkdown and effectively present their findings. By the end of the course, participants will be well-equipped to perform robust data analysis and contribute to data-driven decision-making in various professional contexts.

Introduction to R
What is R?,
Installing R,
R environment

Understanding R data structure
Variables , Scalars
Vectors, Matrices, List
Data frames, functions, Factors

Importing data
Reading Tabular Data files
Loading and storing data with a clipboard
Accessing database, Writing data to file
Writing text & output from analyses to file

Manipulating Data
Selecting rows/observations
Rounding Number
Merging data
Relabeling the column names
Data sorting
Data aggregation

Using functions in R
Commonly used Mathematical Functions
Commonly used Summary Functions
Commonly used String Functions
User-defined functions
local and global variable
Working with dates

Looping
While loop ,
If loop

Charts and Plots
Box plot, Histogram,
Pie graph ,Line chart
Scatterplot, Developing graphs

Introduction to R Programming
Overview of R and RStudio IDE
Basic syntax, data types, and variables in R

Data Import and Manipulation
Importing data from various sources (e.g., CSV files, Excel spreadsheets, databases)
Cleaning and preprocessing data using dplyr and tidyr packages

Exploratory Data Analysis (EDA)
Summarizing and visualizing data distributions, correlations, and patterns
Identifying outliers, missing values, and data inconsistencies

Data Visualization with ggplot2
Creating static and interactive plots: scatter plots, histograms, bar charts, etc.
Customizing plot aesthetics and themes for effective storytelling

Statistical Analysis with R
Descriptive statistics: mean, median, standard deviation, etc.
Inferential statistics: hypothesis testing, confidence intervals, p-values

Advanced Data Analytics Techniques
Predictive modeling: linear regression, logistic regression, decision trees
Cluster analysis: k-means clustering, hierarchical clustering
Time series analysis: forecasting, seasonality, trend detection

Reporting and Deployment
Generating dynamic reports and presentations using RMarkdown
Building interactive web applications with Shiny for data visualization and analysis

Get in touch

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|| Skills to Master

Data Collection:

Data Cleaning and Preprocessing:

Database Management:

Programming Languages:

Statistical Analysis:

Data Visualization:

Machine Learning:

Big Data Technologies:

Data Warehousing:

Version Control:

Cloud Computing:

Data Governance and Security:

Data Storytelling:

Automation and Scalability:

API Integration:

Predictive Analytics and Forecasting:

Data Mining and Exploration:

Collaboration and Teamwork:

Industry-specific Knowledge:

Critical Thinking and Problem Solving:

Networking and Professional Development:

Reporting Tools:

Operational Analytics:

Regulatory Compliance:

|| Tools to Master

Python

TensorFlow

PyTorch

Keras

Seaborn

numpy

Jupyter

VS Code

Pandas

GitHub

Docker

SQL

AWS

OpenAI's GPT

Scikit-learn

Power BI

Google Colab

PyCharm

Tableau

Plotly

Azure

Matplotlib

SciPy

QlikView

Alteryx

Scrum

Excel

VBA

Snowflake

|| Data Analytics Using R offers a variety of Career Options and Job Opportunities

Here are some potential career paths and roles you can pursue:

Data Analyst:
Analyse and interpret complex datasets. Generate reports and visualizations to support decision-making. Identify trends and patterns to provide actionable insights.
Business Intelligence Analyst:
Develop and maintain business intelligence dashboards and reports. Work with stakeholders to understand business requirements. Use R to analyse business performance and market trends.
Data Scientist:
Design and implement predictive models and machine learning algorithms. Conduct exploratory data analysis (EDA) to uncover insights. Use statistical methods to inform business strategies and decisions.
Statistical Analyst:
Perform advanced statistical analysis on large datasets. Develop statistical models to solve business problems. Communicate findings to non-technical stakeholders through reports and visualizations.
Market Research Analyst:
Conduct research to understand market trends and consumer behaviour. Use R to analyse survey data and generate insights. Help businesses develop marketing strategies based on data findings.
Healthcare Data Analyst:
Analyse healthcare data to improve patient outcomes and operational efficiency. Develop models to predict patient readmissions and treatment effectiveness. Work with healthcare professionals to inform policy and practice changes.
Financial Analyst:
Use R to analyze financial data and create forecasts. Assess financial risks and opportunities through data-driven analysis. Support investment decisions with quantitative models.
Operations Analyst:
Optimize business processes and improve operational efficiency through data analysis. Develop models to predict operational outcomes and reduce costs. Collaborate with various departments to implement data-driven improvements.
Data Visualization Specialist:
Create compelling visualizations to communicate data insights. Develop dashboards and interactive reports using R and visualization tools. Work with teams to present complex data in an accessible format.
Research Scientist:
Conduct research using statistical and analytical methods. Analyse experimental data and publish findings in scientific journals. Collaborate with academic and industry researchers on data-driven projects.
Big Data Analyst:
Work with large datasets using big data technologies like Hadoop and Spark. Use R for data preprocessing, analysis, and visualization. Extract meaningful insights from massive amounts of data to inform strategic decisions.
Consultant:
Provide expert advice on data analytics and statistical methods. Help organizations implement data-driven solutions. Conduct training sessions and workshops on data analytics using R.

|| Job Role & Salary

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|| Companies Which use Data Analytics Using R in India

Tata Consultancy Services (TCS):
Uses R for big data analytics and machine learning projects. Employs R for customer analytics and market research.
HDFC Bank:
Uses R for credit risk modelling and fraud detection. Employs R for customer segmentation and targeted marketing.
Sun Pharmaceutical Industries:
Utilizes R for epidemiological studies and health data analysis. Analyses pharmaceutical data to support research and development.
Amazon India:
Utilizes R for market analysis and demand prediction. Analyses customer data to enhance shopping experiences and optimize logistics.
Deloitte India:
Uses R for data analytics and visualization in consulting projects. Employs R for risk assessment and financial analysis.
Indian Institutes of Technology (IITs):
Use R for data science and analytics courses. Employ R for research data analysis in various academic disciplines.
Google:
Uses R for data analysis and visualization in various projects. Employs R for statistical modelling and data mining.

Many other small, medium and giant companies use Data Analytics using R for their tasks, it is almost used in each and every sector we can possibly think of in today's world.

|| Top Hiring Companies

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|| Placement Opportunities after Data Analytics Using R

After completing a course in data analysis using R, graduates have robust placement opportunities across various sectors. Companies in technology, finance, healthcare, e-commerce, and consulting actively seek professionals skilled in R for roles such as Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst. Prestigious organizations like Infosys, TCS, HDFC Bank, ICICI Bank, Amazon, Flipkart, and healthcare giants like Dr.Reddy’s Laboratories and Sun Pharmaceutical Industries often hire data professionals proficient in R. Additionally, startups and government agencies such as the National Informatics Centre (NIC) and the Reserve Bank of India (RBI) offer promising roles. There are many opportunities available in Abroad as well in countries like US, UK, Germany and Australia so on. These opportunities are particularly abundant in major Indian cities like Bangalore, Mumbai, Delhi NCR, and Hyderabad, where the demand for data-driven decision-making continues to grow.

|| Empowering Your Career Transition From Learning To Leading

Rajvi Suthar

Rajvi Suthar, excelling as a Data Analyst at Tata Consultancy Services (TCS), leverages unique tools such as Python for scripting, R for statistical analysis, and Alteryx for data blending. Her adept use of these cutting-edge tools contributes to efficient and advanced data analysis solutions.

Sarthak Gupta

Sarthak Gupta, demonstrating mastery as a Business Data Analyst at Accenture, leverages unique tools such as Power BI for visual analytics, Python for data scripting, and Alteryx for data blending. His adept use of these cutting-edge tools contributes to efficient and advanced business data analysis solutions.

Megha Bhatt

Megha Bhatt, demonstrating prowess as a ML Engineer at Cognizant, leverages unique tools such as Alteryx for advanced data blending, Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

Rishabhjit Saini

Rishabhjit Saini, demonstrating mastery as a Senior Data Processing professional at NielsenIQ, leverages unique tools such as Talend for data integration, Apache Spark for big data processing, and Trifacta for advanced data wrangling. His adept use of these cutting-edge tools contributes to efficient and innovative data handling.

Darshna Dave

Darshna Dave, excelling as a Data Engineer at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

|| Get Data Analytics using R Certification

Three easy steps will unlock your Data Analytics Using R Certification:

Finish the online / offline course of Data Analytics Using R Course and the Assignment
Take on and successfully complete a number of industry-based Projects
Pass the Data Analytics Using R Certification exam

The certificate for this Data Analytics Using R Certification will be sent to you through our learning management system, where you can also download it. Add a link to your certificate to your CV or LinkedIn profile.