Course Details

This Post Graduate Program in Data Science and Machine Learning has a perfect blend of Technology, Data Science and Business cases and insights; it stands out to be among the best in the world.  It is imperative that career seekers grab this opportunity.This uniquely blended Program is brought to you by Praxis, a Top-ranked Analytics B-School in India. Post Graduate Program in Data Science is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity.Learners are required to complete 12-15 months of faculty led Online mode. The following are the modules covered"   Big Data 101 Big Data Characteristics Big Data and Business Data Relationships and Data Model Data Grouping Clustering Algorithms Getting ready for Clustering Algorithms Clustering Algorithms – UPGMA, single Link Clustering KPIs, Businesses & Data Elements Mapping for business outcomes Basic Query Advanced Query – Embedding Mathematics Modelling Introduction to key mathematical concepts Application of eigenvalues and eigenvectors Application of the graph Laplacian Application of PCA and SVD Coding in DB Environment Making Data Sets Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I Measures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R Hadoop Introduction to Big Data and Hadoop Introduction to DBMS systems using MySQL Big Data and Hadoop EcoSystem HDFS Unix & HDFS Hands-on Map-Reduce basics Map Reduce Advanced Topics and Hands on Pig introduction and Hands on Pig Scripting Hive Introduction, Metastore, Limitations of Hive Comparison with Traditional Database and HIVE scripting Hive Data Types, Partitioning and Bucketing Hive Tables (Managed and External) Hive Continued Scoop Introduction and Hands-on Introduction to NoSql and HBASE HBASE architecture and Hands-on Access Methods Big Data with Spark and Python Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples Data Mining 1 - Machine Learning with R & Python Introduction to NumPy Introduction to Pandas Slicing Data Exploratory Data Analysis Exploratory Data Analysis (Continue) Missing Value Imputation and Outlier Analysis Linear Regression Motivation Linear Regression optimization objective Linear Regression in Python Introduction to Regression Tree Introduction to Classification Tree Measures of Selecting the best Split Cluster Analysis – Hierarchical Clustering & k-Means Clustering Customer segmentation in Telecom Industry using Cluster Analysis k-Means clustering Association Rules mining Market Basket Analysis Data Mining 2 - Advanced Machine Learning with R & Python Sources of Error (Irreducible error, bias and variance) Formally defining the 3 Sources of Error Linear Regression – Multicollinearity (VIF) Qualitative Predictors – Use of Dummy Variables Observing overfitting in Polynomial Regression Regularized Regression (L2 – Regularization) – To avoid overfitting Regularized Regression (L1 – Regularization) – Feature selection using regularization Regularized Regression – How does regularized regression handles multicollinearity? Decision Tree – Pruning Bagging Models Designing your own Bagged Model Random Forest Boosting (Ada Boost) K Nearest Neighbour – Concept. kNN algorithm for k=1 and k>1 Writing a K Nearest Neighbour algorithm from scratch Comparison of kNN with Linear Regression; Difference between kNN and kMeans. Revision of basics of Linear Algebra The Theory of dimension reduction Practical – Compressing an image file [Practical using R Software] Practical – Compressing an image file [Practical using R Software] (Continue) RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals Artificial Intelligence & Deep Learning - Industry Practices

This program is brought to you by Praxis B schoolThe Program in Machine Learning is strategically designed to make the candidates competent authority in the enticing world of Machine Learning by introducing you to the fundamentals and dynamics of Machine Learning for coding, linear classification, research and development of algorithms and to predict to extract patterns. This program also comes with the benefit of Placement support from 361 Degree Minds though you would not have the need since opportunities galore when you do this program.