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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

News: Edvancer Eduventures start CBAP course in Mumbai

Kunal Jain
Kunal Jain Last Updated : 18 Apr, 2015
3 min read

In continuation to our previous articles (here and here), another training institute, Edvancer Eduventures has started offering a range of analytics courses in India. Currently they are running a “Certified Business Analytics Professional’ (CBAP) and will be launching 2 more courses R and Big Data in coming months.

cbap_edvancer

We had an opportunity to talk with the CEO, Aatash Shah and here are some excerpts from the talk:

AV: Who is the course aimed at?

Aatash: This course is aimed at both graduate freshers / final year students and working professionals. It is targeted at those looking to start a career in analytics either fresh from college or making a lateral shift.

AV: How does the course differentiate compared to other courses available? 

Aatash:  We do not believe in making our students learn everything under the sun in analytics if they are unlikely to implement those skills in their career for a long time. Instead we believe in training when it is required and hence our idea is to have modular courses which can be taken as the need arises.

Secondly, our course is much more application oriented, designed towards solving business problems using analytics. We teach with a hands-on approach from day one with over 15 real world case studies which will explain each and every concept. We also have a 20 hour guided simulation of an actual analytics project which will bring together all they have learnt in the course in an effort to make their learning as practical as possible.

We provide students life-time access to the course material on our learning management system and free access to the SAS language tool for 90 days.

The other differentiating aspect is the delivery mode. We have three modes of delivery, classroom (in Mumbai for now), online self-paced and soon we shall have the online live instructor led mode too. We also have a comprehensive learning management system with a discussion forum which can be accessed from any internet enabled device allowing any-time, any-where learning.

AV: Is there a way I can test the interface or content? 

Ans: Sure, you can visit www.edvancer.in/course/cbap/ where you can view the curriculum and the preview of the content which has snippets of the various modules.

AV: Is there any placement support which is provided or in case you have any work opportunities / live projects or cases / internships? 

Ans: We do offer placement support and also have an employer’s page on the website from where employers can access CVs of our students. Though having just started off there are no statistics regarding the same.

AV: What is the profile of trainers and promoters?

Ans: About the trainers- We have three trainers presently, all professionals from the analytics industry. One of them is currently the head of a reputed MNC analytics consulting firm in Mumbai and he loves teaching. Another trainer has 8 years of experience in analytics in the banking and retail industry while the third has over 3 years of experience in analytics.

About the promoters:

Aatash Shah, Co-Founder and CEO

Aatash has over 6 years of experience in the investment banking industry. He is a post-graduate in management from IIM Lucknow and is also a BE in Electronics Engineering from Mumbai University.

Lalit Sachan, Co-founder

Lalit has over 2.5 years of experience in business analytics and is extremely passionate about decision making through data analytics. He is a post-graduate from Indian Statistical Institute, Kolkata and a B.Tech from IIT Kanpur.

You can go through the details of the course content and can register for the same following this link. Alternately, if you have any questions for me or Aatash, please feel free to mention them here and we’ll try and answer them.

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page

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Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Mohammed Zaman
Mohammed Zaman

Hi, Do you provide CBAP training? If yes please email me details such as Content, Duration , Material given to Participants and schedule in August 2014.

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sachin
sachin

Hi Kunal, I am really confused which online learning institute is should join, as i am looking to move my carrier from BPO to Data Analytics. I have complited my BCA now have total 4 years of experience in email marketing and lead generation domain. I want to know which accadmy i can join which can provide me adequate knowladge about data analytics. I have recived call from somany e-institutes like, Digital Vidya, Jigsaw acadmy,edvancer, and others. my budget is arround 50to 60 K please please help me to make a right decision. I am already running behind in my carrier hope Analytics Vidya will help me.

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