Data science with R training in bangalore kammanahalli
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Data science with R training in Bangalore
we offer the Best Data Science with R Training in Bangalore, which is designed to teach candidates essential skills like Data Analytics, Machine Learning Algorithms, Data Modeling, Business Analytics, k-means clustering, and R programming, all while being trained by highly certified Data Science experts. We’ve created the Best Data Science Training in Bangalore for you, complete with Data Science real-world projects and Data Science interview preparation.
- Diploma Course in Data Science with Python & R
- Data Science with Python
- Data Science with R
- Data Science with Azure
- Data Science with SAS
On choosing any of the recognized certificate Data Science Training Modules at Cambridge InfoTech, you will become an expert with all the essential skills and tools that are required for a professional Data Scientist in your career.
Why Data Science Course in Bangalore?
Industry specialists with extensive experience in the sector teach the course.
You can enroll in the course at any of our Indian locations.
The most up-to-date equipment and software versions are available.
Classroom Ratio 5.1 students in each batch, where each learner will receive individual attention.
Case studies and real-life projects.
There is no limit to how much time you can spend in the lab or how much you can use it.
Recognized certification course.
We have trained over 32,000 candidates to become IT professionals, demonstrating our training proficiency in India and UAE.
Courses are scheduled to accommodate working professionals and students.
Contact sessions with visiting industry experts on a regular basis.
With over 1500+ IT companies in our network, we’re sure to help you with 100% Job Placement assistance.
Data Science With R training
Cambridge InfoTech offers the aspirants the Best Data Science with R Training in Bangalore. The benefit of this plan is constant supervision by an additional restored educational program by the master workforce. Our Training experts are in the scope of Data Science with R for a significant sustained period. They have additionally been chipping away at comparable progressions in top MNC’s. We are incredibly conscious of what the market requires from us. Our Data with R Training institute in Bangalore provides the most effective approach for preparing in all the more viably determined ways. Get the troublesome preparation from a standout amongst other Data Science with R training establishment.
Best Data Science with R Training in Bangalore
We provide the best Fast-Track Data science training institutes in Bangalore along with One-to-One Data Science with R Training. The added benefit that you would be receiving after you finish our training is that the topics will all be comprised in the most effective way likely while you practice our Data Science with R Certification Training in Bangalore. We always want our students to experience what they learn and what they experience in data science training. That’s why we are concentrating on making learning more practice-oriented.
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Advantages of Data Science with R Training course in Bangalore
Cambridge InfoTech is as of now at some places in Bangalore. We are known amongst our aspirants and industry to give accreditation fueled Data Science with R Training in Bangalore. Our members will consistently be able to clear each round of interviews after they complete our training sessions. The strength of us is we are helping every student, assemble a data science group with R mentors alongside members who can provide future guidance on the whole of the subjects. We concentrate on giving help in position moreover. We do have a solid HR group profession to think about every single interview necessities.
data science with r course syllabus
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & its types
- What is R?
- Why R?
- Installing R
- R environment
- How to get help in R
- R Studio Overview
- Environment setup
- Data Types
- Variables Vectors
- Lists
- Matrix
- Array
- Factors
- Data Frames
- Loops
- Packages
- Functions
- In-Built Data sets
- DMwR
- Reply/plyr
- Caret
- Lubridate
- E1071
- Cluster/FPC
- Data.table
- Stats/utils
- ggplot/ggplot2
- Glmnet
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to the CSV file
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Probability Basics
- What does mean by probability?
- Types of Probability
- ODDS Ratio?
- Standard Deviation
- Data deviation & distribution
- Variance
- Bias variance Tradeoff
- Underfitting
- Overfitting
- Distance metrics
- Euclidean Distance
- Manhattan Distance
- Outlier analysis
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- Scatter plot
- Cook’s Distance
- Missing Value treatments
- What is a NA?
- Central Imputation
- KNN imputation
- Mummification
- Correlation
- Pearson correlation
- Positive & Negative correlation
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
- Missing Value treatments
- What is a NA?
- Central Imputation
- KNN imputation
- Mummification
- Correlation
- Pearson correlation
- Positive & Negative correlation
Machine Learning Algorithms
- Popular Machine Learning Algorithms
- Clustering, Classification, and Regression
- Supervised vs Unsupervised Learning
- Application of Supervised Learning Algorithms
- Application of Unsupervised Learning Algorithms
- Overview of modeling Machine Learning Algorithm: Train, Evaluation, and Testing.
- How to choose Machine Learning Algorithm?
- Linear Regression
- Linear Equation
- Slope
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure
- ROC curve
- Bias Variance Tradeoff
- K-Means
- K-Means ++
- Hierarchical Clustering
- Linear Regression
- Logistic Regression
- K-Means
- K-Means++
- Hierarchical Clustering – Agglomerative
- CART
- 5.0
- Random forest
- Naïve Bayes
