Data Science Certification with R

Colleagues, Glassdoor estimate the average salary of US-based Data Scientists at $118,088. This Data Science with R certification training lets you gain expertise in Machine Learning Algorithms such as K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. This Data Science with R  Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout the R Programming Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. The curriculum addresses: 1) Introduction to Data Science with R - see how Data Science helps to analyze large and unstructured data with different tools, 2) Statistical Inference - learn statistical techniques and terminologies used in data analysis, 3) Statistical Inference - statistical techniques and terminologies used in data analysis, 4) Data Extraction, Wrangling and Exploration - sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format, 4) Introduction to Machine Learning - categories of Machine Learning and implement Supervised Learning Algorithms, 5)  Classification Techniques - Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, 6) Unsupervised Learning - various types of clustering that can be used to analyze the data, 6) Recommender Engines - association rules and different types of Recommender Engines, 7) Text Mining - Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity, 8) Time Series - data, different component of Time Series data, Time Series modeling - Exponential Smoothing models and ARIMA model for Time Series Forecasting, and 9)  Deep Learning - learn the oncepts of Reinforcement learning and Deep learning in this module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for Artificial Neural Networks, and Artificial Neural Network terminologies. 

Enroll today (teams & execs welcome): https://fxo.co/EFzP 

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