Course Details

Advanced Certificate 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 3-6 weeks of faculty led Online mode. The following are the subjects covered  

• 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
• Measuring Association

• 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

• 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)

This program is brought to you by Praxis B school

The course will make the candidates expert in Machine Learning and data automation without explicit programming. The candidates will be mastered in Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.