This business analytics training is for beginners who want to start from basics of Excel, SQL, Tableau moving to advanced tools like R, Python data science, including machine learning. Evolved from our most popular course Business Analytics training, this is the best business analytics course in India curated for candidates who are looking for job oriented business analytics certification but have no prior knowledge of any business intelligence or data analytics tools. Most extensive business analytics course in Bangalore and Delhi NCR, with flexibility of also attending the live online training and self-paced video based mode as well.
However, learning tools without techniques is half the job done in today's Analytics world. So to help candidates emerge as 'Industry Ready' professional, this Business Analytics course in India encompasses basic statistical concepts to advanced analytics and predictive modelling techniques, along with machine learning, using most widely used analytics tools, like Excel, Tableau, SQL, Python and R. This analytics certification course is for all those aspirants who want to switch into the field of data science and begin their career as business analyst.
One of the best Business Analytics online course in India and is also available in Delhi NCR and Banaglore, with aim to provide you job oriented Data Science and Business Analytics skills.
Business Analytics training 'Business Analytics 360' Duration: 450 hours (At least 150 hours live training + 48 hours video based module + around 10 hrs of weekly self-study and practice)
Who Should do this course?
Beginner candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Business Management who are looking for Business Analytics training to start their career in the field of Analytics and Data Science.
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Course Duration | 450 hours |
Classes | 50 |
Tools | Excel, VBA, Tableau, SQL,R, Python |
Learning Mode | Live/Video Based |
Next Batch | 15th December, 2019 (Gurgaon) 19th January, 2020 (Bangalore) |
Time Series Forecasting: Solving forecasting problems
Machine Learning -Predictive Modeling – Basics
•Introduction to Machine Learning & Predictive Modeling
•Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
•Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
•Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
•Overfitting (Bias-Variance Trade off) & Performance Metrics
•Feature engineering & dimension reduction
•Concept of optimization & cost function
•Overview of gradient descent algorithm
Machine Learning -Predictive Modeling – Basics
•Overview of Cross validation(Bootstrapping, K-Fold validation etc)
•Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
Segmentation: Solving segmentation problems
•Introduction to Segmentation & Role of ML
•Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
•Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
•Behavioral Segmentation Techniques (K-Means Cluster Analysis)
•Cluster evaluation and profiling - Identify cluster characteristics
•Interpretation of results - Implementation on new data
Unsupervised Learning: Segmentation
•Concept of Distance and related math background
•Expectation Maximization
•Hierarchical Clustering
•Spectral Clustering (DBSCAN)
•Principle component Analysis (PCA)
Supervised Learning: Decision Trees
•Decision Trees - Introduction - Applications
•Types of Decision Tree Algorithms
•Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
•Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
•Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
•Decision Trees - Validation
•Overfitting - Best Practices to avoid
Supervised Learning: Ensemble Learning
•Concept of Ensembling
•Manual Ensembling Vs. Automated Ensembling
•Methods of Ensembling (Stacking, Mixture of Experts)
•Bagging (Logic, Practical Applications)
•Random forest (Logic, Practical Applications)
•Boosting (Logic, Practical Applications)
•Ada Boost
•Gradient Boosting Machines (GBM)
•XGBoost
Supervised Learning: Artificial Neural Networks (ANN)
•Motivation for Neural Networks and Its Applications
•Perceptron and Single Layer Neural Network, and Hand Calculations
•Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
•Neural Networks for Regression
•Neural Networks for Classification
•Interpretation of Outputs and Fine tune the models with hyper parameters
•Validating ANN models
Supervised Learning: Support Vector Machines
•Motivation for Support Vector Machine & Applications
•Support Vector Regression
•Support vector classifier (Linear & Non-Linear)
Supervised Learning: Support Vector Machines
•Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
•Interpretation of Outputs and Fine tune the models with hyper parameters
•Validating SVM models
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Supervised Learning: KNN
•What is KNN & Applications?
•KNN for missing treatment
•KNN For solving regression problems
•KNN for solving classification problems
•Validating KNN model
•Model fine tuning with hyper parameters
Supervised Learning: Naïve Bayes
•Concept of Conditional Probability
•Bayes Theorem and Its Applications
•Naïve Bayes for classification
•Applications of Naïve Bayes in Classifications
Text Mining & Analytics
•Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Text Mining & Analytics
•Finding patterns in text: text mining, text as a graph
•Natural Language processing (NLP)
•Text Analytics – Sentiment Analysis using R
•Text Analytics – Word cloud analysis using R
•Text Analytics - Segmentation using K-Means/Hierarchical Clustering
•Text Analytics - Classification (Spam/Not spam)
•Applications of Social Media Analytics
•Metrics(Measures Actions) in social media analytics
•Examples & Actionable Insights using Social Media Analytics
•Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
•Fine tuning the models using Hyper parameters, grid search, piping etc.
Project - Consolidate Learnings:
Applying different algorithms to solve the business problems and bench mark the results
Stationary Time Series Methods
Trend Based Time Series
Seasonal Time Series
Advanced Techniques
I am from Advance Big Data Science course of Nov 2016 batch. The course was very structured and got real world problems to practice during the final case studies. It has also boosted my skills to start participating in the hackathons. Chandra sir was really helped me in preparing my resume right from very beginning of the course. Also, the placement team was very well organized and connected to industry leaders so that people get the right opportunity right after completing the course. I really recommend this institute, who are interested to transition into analytics field.
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