Intellipaat Data Science Architect Master’s Course

1. Data Science with R –
Introduction to Data Science with R, Data Exploration, Data Manipulation, Data Visualization, Introduction to Statistics, Machine Learning, Logistic Regression, Decision Trees & Random Forest, Unsupervised learning, Association Rule Mining & Recommendation Engine, Introduction to Artificial Intelligence (self paced), Time Series Analysis (self paced), Support Vector Machine – (SVM) (self paced), Naïve Bayes (self paced), Text Mining (self paced), Case Study.

2. Python for Data Science –
Introduction to Data Science, Introduction to Python, Python basic constructs, OOPs in Python, NumPy for mathematical computing, SciPy for scientific computing, Data manipulation, Data visualization with Matplotlib, Machine Learning using Python, Supervised learning, Unsupervised Learning, Python integration with Spark- (selfpaced)

3. Machine Learning Course
Introduction to Machine Learning, Supervised Learning and Linear Regression, Classification and Logistic Regression, Decision Tree and Random Forest, Naïve Bayes and Support Vector Machine (self paced), Unsupervised Learning, Natural Language Processing and Text Mining (self paced), Introduction to Deep Learning, Time Series Analysis (Self-paced).

4. AI and Deep Learning
Introduction to Deep Learning & Neural Networks, Multi-layered Neural Networks, Artificial Neural Networks and Various Methods, Deep learning libraries, Keras API, TFLearn API for TensorFLow, DNN: Deep Neural Networks, CNN: Convolutional Neural Networks, RNN: Recurrent Neural Networks, GPU in Deep Learning, Autoencoders & Restricted Boltzmann
Machine (RBM), Deep Learning applications, Chatbots, Time Series Analysis (Self-Paced).

5. Tableau Desktop 10
Introduction to Data Visualization and Power of Tableau, Architecture of Tableau, Working with Metadata & Data Blending, Creation of sets, Working with Filters, Organizing Data and Visual Analytics, Working with Mapping, Working with Calculations & Expressions, Working with Parameters, Charts and Graphs, Dashboards and Stories, TableauPrep, Integration of Tableau with R and Hadoop.

6. Data Science with SAS
Introduction to SAS, SAS Enterprise Guide, SAS Operators & Functions, Compilation & Execution, Using Variables, Creation and Compilation of SAS
Data sets, SAS Procedures, Input statement and formatted input, SAS FORMAT, SAS Graphs, Interactive Data Processing, Data Transformation Function, Output Delivery System (ODS), SAS MACROS, PROC SQL, Advanced Base SAS, Summarization Reports.

7. Self-Paced Courses
Statistics & Probability, Advance Excel, MongoDB, MS SQL,Machine Learning, Hadoop Developer

8. Project Works