Data Science with R
Boost your career in Data Science with R Program. An industry leader offers world-class Data Science training on the most in-demand Data Science skills. Gain hands-on experience with key technologies such as R and Machine Learning. Become an expert in Data Science today.
The program covers a wide range of topics, from R to Exploratory Data Analysis to Machine Learning and Deep Learning and more. Our instructors and assistants supervise you while you complete the coursework, using practical labs to bring these ideas to life.
What’s in it for me?
Upon completion of this program, you will:
1) Be familiar with analytics tools and technologies such as R
2) Apply industry-relevant machine learning techniques such as regression, predictive modeling, clustering, time series forecasting, classification, etc.
3) Use statistics and data modeling to build an analytics framework for a business problem
4) Perform using data cleaning and data transformation operations several tools and techniques
5) Be well versed in Deep learning, Natural Language Processing (NLP).
6) Present yourself to leading analytics companies as an ideal candidate for analyst, data engineer, and data scientist roles
Role Offered
1) Data Scientist
2) Manager Analytics
3) ML Engineer
4) AI Engineer
5) Reporting Analyst
6) Research Executive
This course offers Data Science with R Certification Validation Tool for Employers
Employers, clients, and other stakeholders can use your Data Science Certification Validation Tool to check the authenticity of your certification.
The Data Science with R @ The Learnify academy:
Why The Learnify academy
The Data Science with R program @ The Learnify academy:
1) A focus on learning, understanding, and implementing concepts rather than merely gaining theoretical knowledge
2) Interactive classes and small batch sizes
3) Facilitated by an industry expert with over ten years of experience
4) Instructor-led interactive virtual classroom sessions for 40+ hours on weekends
5) Support for six months following completion of training, i.e. monthly revision classes
6) Certificates are valid for a lifetime
7) Session recordings are available for life
8) 15:1 participant-faculty ratio
9) Four or more capstone projects
Course Curriculum:
| Course Curriculum - Data Science with R |
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| Data Science Overview | Data Manipulation with R | Machine Learning – Supervised & Unsupervised |
| # DS Spectrum | # File read and write support | # Supervised VS Unsupervised |
| # S use cases (Different sectors using) | # Understanding Data frame and Series | # Linear & Logistic Regression |
| # Difference b/w AI, ML, DL & DS | # Data Operations | # Decision Tree & Random Forest |
| Introduction to R and Rstudio (R & Rstudio setup) | # Group by & Aggregation | # Naive Bayes & Support Vector Machine |
| # Why R & Rstudio | # Join/Merge/Concatenation | # Boosting – Xgboost, Adaboost & others boosting algorithm |
| # R & Rstudio Installation | # Industry Use Cases | # KNN |
| R Basics | Visualization in R Using Ggplot2 and Ggmap | # Principal Component Analysis (PCA) |
| # R as a calculator | # Different Plots – Scatter, Histogram, Bar chart, Pie chart, Stacked, column chart, Pair plot, density plot, Line chart, and Violin Plot | # K- Means Clustering & DBSCAN Clustering |
| # Data types with R – Variables, Objects | # Outlier Analysis using Box Plot | NLP – Natural Language Processing |
| # Operators – Comparison, Logical & Arithmetic | # Industry Use Cases | # Text Preprocessing, Wordcloud |
| # Conditional Statement – If , elif & else | Statistics essential for Data Science | # Sentiment Analysis |
| # Loops – While, for | # Introduction | # Text Classification use case |
| # Functions – R inbuilt & User-defined function | # Sampling and Population | Deep Learning – Basics |
| Advance Data Structure in R | # Measures of Central tendency, Dispersion, Skewness, and Kurtosis | # Introduction to Artificial Neural Network (ANN) |
| # Understanding Vector | # Karl Pearson and Spearman Correlation | Four End to End ML Industry based Project |
| # Understanding Data Frame | # Discrete and Continuous Distributions | # Supervised Classification Project |
| #Understanding List | # Inferential Statistics and Confidence Interval | # Supervised Regression Project |
| # Hypothesis Testing – Large and Small sample tests | # Unsupervised Clustering Project | |
| # Non-Parametric Tests | # NLP – Movie review Project | |
