Data Science and Machine Learning Bootcamp with R
February 18, 2025 6:26 pmData Science and Machine Learning Bootcamp with R
Online Course
Data Science and Machine Learning Bootcamp with R
The Data Science and Machine Learning Bootcamp with R is designed to equip you with the essential skills needed to succeed in the fast-growing fields of data science and machine learning. Over the course of 6 months, you will gain hands-on experience using R, one of the most powerful programming languages for statistical analysis and machine learning. You will learn how to manipulate data, perform exploratory data analysis, visualize data, and apply machine learning algorithms to solve complex problems. This bootcamp covers a broad range of topics including statistical analysis, machine learning models, data visualization, deep learning, and natural language processing. By the end of the course, you will be capable of developing and deploying data-driven solutions to real-world challenges.
Data science and machine learning are among the most in-demand skills in today’s job market. Businesses across sectors such as healthcare, finance, e-commerce, and technology rely on data-driven insights to make informed decisions. As a graduate of this bootcamp, you will be qualified for roles like Data Scientist, Machine Learning Engineer, Data Analyst, and AI Specialist. The demand for skilled professionals in these fields is expected to continue rising, with job opportunities offering competitive salaries and long-term career growth. Whether you’re looking to work in tech, finance, healthcare, or any other data-driven industry, this bootcamp will provide you with the knowledge and tools needed to excel.
Course Fee
₹ 30,000/-
Qualification
Any Degree
Duration
3 Months
Course Type
Certification
Our Recognitions Speaks
Creative Mentors was honored for its excellency in animation education industry
WHAT WE TEACH
Here’s a suggested 6-Month Course Curriculum for a Data Science and Machine Learning Bootcamp with R, designed to cover essential concepts and practical skills in data science and machine learning using R:
Month 1: Introduction to Data Science & R Programming
- Overview of Data Science and its importance
- Understanding the data science workflow: data collection, analysis, modeling, and deployment
- Introduction to R programming language
- Installing R and RStudio, R packages, and managing libraries
- Basic R syntax and data structures (vectors, data frames, lists)
- Data cleaning: handling missing data, outliers, and duplicates
- Data transformation: reshaping, merging, and aggregating data
- Exploratory Data Analysis (EDA): Descriptive statistics, data visualization
- Introduction to ggplot2 for data visualization
- Handling categorical and numerical variables
Month 2: Statistical Analysis and Data Visualization
- Descriptive statistics: mean, median, variance, standard deviation
- Probability distributions (Normal, Poisson, Binomial)
- Hypothesis testing: t-tests, chi-square tests, ANOVA
- Correlation and covariance analysis
- Confidence intervals and p-values
- Visualizing data using ggplot2: histograms, bar plots, scatter plots
- Advanced data visualizations: heatmaps, box plots, violin plots
- Customizing plots: themes, legends, and color palettes
- Plotting time series and geographical data
- Effective storytelling with visualizations
Month 3: Introduction to Machine Learning and R
- Overview of machine learning: Supervised vs. Unsupervised learning
- Steps in the machine learning process: Data preprocessing, training, testing, model evaluation
- Overview of key algorithms: Linear regression, Logistic regression, Decision Trees
- Model evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC curves
- Linear regression: implementation and evaluation
- Logistic regression: binary classification problems
- K-Nearest Neighbors (KNN): theory and implementation
- Decision trees and Random Forests
- Model performance improvement: Hyperparameter tuning, cross-validation
Month 4: Advanced Machine Learning Techniques
- Introduction to clustering: K-means, Hierarchical clustering
- Principal Component Analysis (PCA): Dimensionality reduction
- Association rule learning: Apriori algorithm
- Exploring unsupervised learning in real-world applications
- Visualizing clustering results
- Model validation techniques: Cross-validation, confusion matrix, ROC curve
- Overfitting and underfitting: Bias-variance tradeoff
- Deploying machine learning models in R
- Introduction to Shiny for building interactive web apps
- Packaging and sharing models for deployment
Month 5: Deep Learning and Advanced Topics
- Overview of deep learning and neural networks
- Building neural networks with R using keras and tensorflow
- Activation functions, loss functions, and backpropagation
- Training and evaluating deep learning models
- Image classification and regression tasks
- Introduction to NLP: Text preprocessing (tokenization, stop-word removal)
- Text vectorization: TF-IDF, Bag of Words
- Sentiment analysis and text classification
- Word embeddings (Word2Vec, GloVe)
- Applications of NLP in business and industry
Month 6: Capstone Project & Real-World Applications
- Identifying a real-world data science problem
- Collecting, cleaning, and exploring data
- Model selection, training, and evaluation
- Building visualizations and communicating results effectively
- Applying all skills learned in the course to a comprehensive data science project
- Writing up the results, insights, and conclusions
- Presenting findings with effective visualizations
- Final assessments and project feedback
- Weekly Assignments: Reinforce key concepts through hands-on tasks and problem sets.
- Project Work: Apply concepts learned in modules to real-world datasets.
- Quizzes: Test knowledge on theoretical concepts after every module.
- Final Capstone: A comprehensive data science and machine learning project with a focus on real-world problem-solving.
This bootcamp-style curriculum provides a strong foundation in data science and machine learning using R, integrating theory with practical application. By the end of the program, students will be able to tackle data science challenges and apply machine learning algorithms to real-world datasets, making them ready for industry roles like Data Scientist, Machine Learning Engineer, Data Analyst, and more.
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FROM THE STUDENTS
Direct testimonials from the students who completed the course
ADMISSION PROCESS
Creative Mentors Animation, Gaming and VFX School is looking for dedicated students who want to become tomorrow’s art and design leaders. We seek innovators, storytellers, collaborators, problem solvers, dreamers, leaders—all are welcome here.
