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Fundamentals of Data Analytics

Get started with this applied course focusing on the building blocks of analytics and statistics. The multi dimensional course provides 12 hands on data cases across different domains and techniques. Beginner Level course which would take approx. 61 Hours to complete

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Machine Learning - Linear Regression

Linear Regression is one of the most widely leveraged techniques, for building future predictions and forecasts. The applied courses focuses on the different aspects of building and evaluating a robust linear regression model, on real world datasets. The course provides 6 hands on data cases with guided approach for building appropriate solutions. Advance Level, Approx. 23 Hours to complete

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Machine Learning - Logistic Regression

Logistic Regression is one of the most widely applied statistical techniques across businesses and verticals. The successful completion of the applied course, will equip the learner to build and evaluate a robust logistic model on a real world dataset to solve a business objective. Advance Level Approx. 20 Hours to complete (5 Hours per week)

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Concepts and Application of Objective and Subjective Segmentation

The applied course focuses on the different concepts of segmentation via some of the most commonly used supervised and unsupervised algorithms. The data cases discussed within the course are based on real world business problems with a guided approach for building appropriate solutions. Advance Level Approx. 20 Hours to complete (5 Hours per week)"

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Mining Text Data Cleansing, Treatment, Structural Representation & Visualization

This applied course focuses on the fundamentals of text data mining like cleansing, Treatment and Visualization of Text Data, which is a kind of must to know for text data analytics. With the help of Real-world datacases based on unstructured text data you get familiarized with the concepts through a guided approach.

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Fundamentals of Time Series Analysis

This applied course, first one in the series, focuses on the building blocks for any time series analysis, exposing the learner on application of different visual and statistical techniques applied on real world datasets. Upon successfully completion of the course the leaner would have a thorough understanding of the different concepts and experience on their application on data. Beginner Level Approx. 15 Hours to complete (5 Hours per week)

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Data Mining Concepts and Techniques

Building a deeper understanding about the data is a very crucial step of any Data Science project. Without understanding the data well you can never draw actionable and impact insights from the data or build Predictive Solutions.This course discusses the different techniques of data mining through which you can build a better understanding about the your data and use it effectively in problem solving.

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Sentiment Analysis - Using Unstructured Text Data

Today as many people utilize social media and electronic channels to convey their opinions , it has become crucial for businesses to assess the sentiment behind the opinions and act accordingly. So in recent times Sentiment Analysis as a technique has gained a lot of popularity due to its wide ranging application across domains. In this course alongwith other basic text analytics concepts , you will also be introduced to Sentiment Analysis and how one can draw interpretations through analysis of the identified sentiments of a text corpus

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Text Analytics - Classification and Clustering

NLP and Text Analytics are two of the new age domains sprouting from the advent of the different forms of data generated in the digital world. This applied course provides the learner a thorough understanding of Text Clustering and Classification techniques, with hands on application of those techniques on real world datasets. The structure of the course exposes the learner not only to identify underlying themes in a text dataset through clustering methods, though also enables the learner in building predictive text algorithms through classification techniques. Advance Level Approx. 25 Hours to complete (5 Hours per week)

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Application Of Analytics For Customer Acquisition In Banking & Financial Services

First among the 3 applied courses focused on Application of Analytics in Banking and Financial Services talks about the vital importance of Data Analytics in the Customer Acquisitions Functions. Through its 6 Datacases it will introduce you to application of Analytics to some of the key problems in Acquisitions.

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Application of Analytics for Customer Engagement in Banking and Financial Services

Second among the 3 applied courses focused on Application of Analytics in Banking and Financial Services talks about the vital importance of Data Analytics in the Customer Engagement Management Functions. Through its 5 Datacases it will introduce you to application of Analytics to some of the key problems in Customer Engagement function

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Application of Analytics for Customer Retention in Banking & Financial Services

Third among the 3 applied courses focused on Application of Analytics in Banking and Financial Services talks about the vital importance of Data Analytics in the Customer Retention Management Functions. Through its 5 Datacases it will introduce you to application of Analytics to some of the key problems in Customer Retentionfunction

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Basics of Hyperparameter Tuning - Linear & Logistic Regression

Hyperparameter tuning refers to the task of choosing the optimal combination of parameters that can only be learned before the training process begins. When the performance of the algorithm is not satisfactory, hyperparameter tuning is used to tweak certain parameters for a performance boost. In this course, we will focus on hyperparameter tuning for Linear and Logistic Regression through a hands-on approach and will deep-dive into the understanding of various hyperparameters associated with the algorithms.

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Fundamentals of Data Preprocessing

Get started with this applied course focusing on the introduction to data preprocessing. This quick course provides opportunities of learning through reading material, Business case and a quiz to test your understanding. Beginner Level Approx. 1 Hour to complete

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Introduction to Explainable AI (XAI) using LIME

This course will help you understand the need of explainable AI (XAI) and introduce you to LIME, which is one of techniques for XAI. Learn how LIME can be applied on a real world datacase and how the results can be presented in a human interpretable format. Take the quiz to validate your understanding and then solve a datacase based on the learnings.

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Basic Data Visualization Methods- I

This course helps you to understand different types of basics of summary and distribution charts which are majorly used to represent the data in a visible format to reveal the trends and patterns. Learn how and when to use different types of charts to present the actionable insights.

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Introduction to ANOVA

Analysis of Variance (ANOVA) is a statistical method to test difference between two or more means. This applied course focuses on the concepts of ANOVA , its types and applications by using business cases. Basic Level

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Introduction to Explainable AI (XAI) using SHAP

This course will help you understand the need of explainable AI (XAI) and introduce you to SHAP, which is one of techniques for XAI. Learn how SHAP can be applied on a real world datacase and how the results can be presented in a human interpretable format. Take the quiz to validate your understanding and then solve a datacase based on the learnings.

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Introduction to Gradient Boosting Classification

Gradient Boosting Classification is a machine learning technique, attracting attention for its prediction and accuracy. From Machine Learning competitions to data science businesses gradient boosting is providing best-in-class results. It uses an ensemble of weak prediction models for building a classification model. The model is built in a sequential process, where each new step tries to minimize the errors of the previous step. This course will provide you an in-depth understanding of the algorithm along with its practical use case.

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Introduction to Extreme Gradient Boosting Classifier

Extreme Gradient Boosting is an optimized machine learning algorithm designed for better speed and performance. It is an extension of gradient boosted decision trees. Since its inception in 2014, the algorithm has achieved high performances on structured data and dominated other algorithms because of its flexibility. This course covers the end-to-end walkthrough of the algorithm.

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Catboost Classifiers - An Introduction

CatBoost (short for Categorical Boosting) is a recently developed machine learning algorithm that helps data scientists and machine learning engineers in automating their column transformations and provides best-in-class accuracy. It can easily integrate with different deep learning frameworks and can work on diverse data types. It solves a variety of business problems that earlier required extensive data preparation.

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Introduction to Gain and Lift Charts

This course will give you a brief introduction to the concept and applications of one of the most important and commonly used model validation techniqes: Gain and Lift Charts.

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Hyperparameter Tuning in SVM

Proper Hyperparameter tuning is essential for the successful implementation of Support Vector Machines for both classification and regression tasks. SVM offers some predominant hyperparameters that should be fixed before the training process and have an immense effect on the performance of their predictive models. That being said, hyperparameter tuning for SVM is a trivial task. In this course, we will study the concepts of Hyperparameter tuning for SVM in detail along with extensive hands-on practice.

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Hyperparameter Tuning in Tree-Based Models

Tree-Based Models are considered as key algorithms in supervised machine learning. They are simple to train and easy to visualize. They provide high flexibility in training as there are many hyperparameters to tune. In this course, our focus will be to enhance the performance of tree-based models by the use of hyperparameter tuning. We will deep dive into the understanding of the hyperparameters and methodologies for hyperparameter tuning.

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Model Evaluation Techniques - Classification Models

One of the most essential phases of the classification machine learning algorithms is to understand the ways to evaluate the performance of the model built. This is one of the tasks that are often difficult to interpret and sometimes not paid attention to. In this course, we will see each of the performance measures that will help you to quantify the performance of the model and decide which algorithm will be most suited according to the problem at hand.

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Model Evaluation Techniques - Regression Models

Machine Learning algorithms have been proven extremely beneficial for regression tasks. However, there is one question that is often asked. Which algorithm is suited for the particular problem or Do you think the model built is performing well? In this course, we will learn different performance measures that will help you to answer this question. We will see each performance measure used to evaluate a regression model in detail which will help us to select the top-performing model for a particular problem.

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Introduction to Neural Networks

Neural Networks are a series of algorithms composed of artificial neurons, that are capable of recognizing the underlying principle from the set of data in a way similar to the human brain. In the past decade, neural networks have laid the foundation for a new domain called Deep Learning that has been used to develop some of the most intelligent machines capable of performing complex tasks such as object recognition, speech translation and recognition, robotics, and many more. This course is designed to give a complete understanding of the most fundamental aspects of neural networks.

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Introduction to Optimization Techniques

First among the 3 applied courses focused on Application of Optimization in Analytics talks about the vital importance of General Optimization Problems in the Business Functions. Through its 3 Datacases it will introduce you to application of three different optimization algorithms to some of the key problems in finding optimal parameters for business settings

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Optimization Techniques - Network Flow Problems

Second among the 3 applied courses focused on Application of Analytics by Optimization talks about the vital importance of Shortest Path Problem and Minimum Spanning Tree in the Network Flow Optimization Problems. Through its 2 Datacases it will introduce you to application of Analytics to some of the key problems in Network FLow Optimization

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Optimization Techniques - Specialized Optimization Problems

Third among the 3 applied courses focused on Application of Analytics by Optimization talks about the vital importance of several specialized optimization problems such as transportation problem, knapsack problem and travelling salesman problem. Through its 5 datacases it will introduce you to application of analytics to some of the key optimization problems.

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Understanding Decision Trees

Decision Trees are classic non-parametric supervised learning algorithms used for both classification and regression tasks. The decision tree splits the dataset multiple times based on a series of decisions efficiently representing the data and making predictions. They are one of the most widely used algorithms for data mining as it is easy to interpret the decisions made by the algorithm and this reason has made it one of the favourite algorithms of Data Science professionals as well.

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Getting Started with Naive Bayes Classifiers

Naive Bayes Classifiers are a family of probabilistic algorithms that are based on the Bayes Theorem with an assumption of conditional independence between the features of the dataset. Their robustness and fast execution are widely known. Despite its simplicity, they have wide-ranging applications such as Text classification, Spam Detection, medical diagnosis, and robot sensing to name a few.

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Understanding Principal Component Analysis (PCA)

Training a dataset with a large number of features can be sufficiently slower with the classical machine learning or deep learning algorithms. Principal Component Analysis or PCA is an unsupervised machine learning algorithm that is able to summarize information of large datasets into a smaller set that makes it easy to train and visualize data. This course provides an in-depth understanding of the hands-on application of PCA.

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Application of Explainable AI methods to decode NLP models

Webinar by Amrit Bhat

In this webinar, learn the domain of Explainable AI and its usage in NLP models on a practical business case.

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8th January 2021 (Finished)

Data Talk with Imaad Mohamed Khan

Webinar by Imaad Mohamed Khan

In this episode of the Data Talk series, we will be talking with Imaad Khan, one of India's top 10 Data Scientists, regarding his journey in the field of Data Science and his outlook regarding the future of the field.

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5th December 2020 (Finished)

Predict Customer Attrition Using Naïve Bayes Classification

Webinar by Aman Akshansh

Learn about the Naive Bayes algorithm and its application in determining the likelihood of a customer attriting. The webinar will focus on taking a use case and solving it using the Naive Bayes Algorithm

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25th September 2020 (Finished)

Explaining NLP Models Using LIME and SHAP

Webinar by Amrit Bhat

Learn the ropes of explainable AI using LIME and SHAP with a practical use case on NLP models and their explainability

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28th August 2020 (Finished)

Applications of statistical tests - Chi Square test, t-test and z-test

Webinar by Vishwanath Paramashetti

In this case, we will be applying techniques of ‘Hypothesis Testing’ to answer some of the hypothetical scenarios based on the data at hand. Learn how different statistical testing techniques are used to prove or disprove a hypothesis and make business decisions.

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14th August 2020 (Finished)

Application of Machine Learning Techniques and Explainable AI frameworks to Mitigate Risk

Webinar by Lakshmi E

In this hands-on webinar powered by ATH LEAPS, you will be introduced to how ML models and Explainable AI frameworks play a vital role in controlling the risk across domains like BFS and Healthcare. With the help of a real business case from the Finance domain and using open-source data on the current COVID-19 pandemic you will get an opportunity to be familiarised with applying ML models and Explainable AI frameworks for building better predictive models for effective risk mitigation.

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17th July 2020 (Finished)

Application of Decision Tree in Credit Card Attrition

Webinar by Manasa Moharaj

Learn how the Decision Tree algorithm is applied in the Credit Card Industry to identify customers with higher likelihood of attrition. In this webinar, we'll focus on C5.0 algorithm and its interpretation.

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3rd July 2020 (Finished)

Customer Segmentation in Retail Sector using K-Means Clustering

Webinar by Varun Shrivastava

Customer Segmentation allows an organization to follow a targeted approach to engage and retain their customers. In this webinar, we'll learn how a retail company segments its customers by applying K-Means algorithm.

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19th June 2020 (Finished)

Sentiment analysis on review data of movies

Webinar by Saurabh Singh

Can reviews of a movie be summarized to gauge the public sentiment about it? Understanding the dominant sentiment among the masses would help you finding an answer. Sentiment analysis is the key here, join the webinar to know how.

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5th June 2020 (Finished)

Analyze Credit Card Spend Data - Application Of Descriptive Analysis Techniques

Webinar by Nikhil Nene

Did you know that majority of the data driven solutions are backed on descriptive analysis. Here, in this case, we will tell you how to use descriptive statistics for the same with the help of an example from banking domain.

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22nd May 2020 (Finished)

Application of ARIMA to Forecast Day-Ahead Spot Prices of Electricity

Webinar by Vishwanath Paramashetti

Electricity is 10 times more volatile than other commodities with 50% fluctuation in daily price being very common. Advanced economies use energy market to hedge and plan against the risks of price volatility. Hence, probabilistic information about the Day-Ahead Spot Prices is very valuable for both, energy producers and buyers. This information is used in negotiating contracts.

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8th May 2020 (Finished)

Challenges in application of Common-sense Knowledge in NLP and AI solutions

Webinar by Madhav Kaushik

Contextualizing NLP and AI solutions in the healthcare industry, is of paramount importance, as they can have a direct impact on patent safety. The talk will focus on the business impact and successful application of such solutions in the healthcare industry via ATH Precision – ‘Powerful Ecosystem for Business Integrated Analytics’

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1st May 2020 (Finished)

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Fantasy Cricket & Linear Programming

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Understanding SHAP(XAI) through LEAPS

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Demystifying LIME (XAI) through Leaps

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Scope and Future of Business Analytics in India

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Data Science – Myth Vs Reality

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Why did we build Analyttica TreasureHunt?

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Network Engineer - Multinational Networking and Telecom Company

“Data Analytics is an art to evaluate the insights of data to make the productive decisions and manage risks in an organization. Recently completed my 'Fundamentals of Data Analytics' course. It is an amazing platform with ample cases to provide a hands-on experience. This course is great to learn about the fundamentals of data analytics with real-life data sets to practice with multiple quizzes. It helped me in gaining experience. It was helpful to understand the basics of Data analytics. It provided in-depth subject knowledge.
Concepts learned:
Ø Basic Numeric Descriptive Measures
Ø Probability Distribution and Statistical Inference #
Ø Frequently Applied Statistical Test
Ø Collection of Data: Population and Sampling
Ø Classification and Representation of Data
Ø Measures of Location and Variability
Ø Basic Bi-variate Analysis
Ø Probability Distribution
Ø Statistical Inference – Estimation and Hypothesis Testing
Thanks to Analyttica Datalab Inc. for such amazing content.”