Tutorials

Introduction to the common ethical concerns and dilemmas in the field of data science. Possible opportunities for data scientists to move past ethical evils and pave the way for a brighter future.

Tutorials

Introduction to various aspects of feature engineering for bot detection of automated web traffic. Developing an understanding of the impact on online business and types of web bots available.

Tutorials

Overview of the A/B testing technique along with the usefulness of this testing mechanism in an online business. The common issues and pitfalls in A/B testing when running online experiments.

Tutorials

Fundamentals of building robust machine learning models. Effective models require due diligence during data acquisition phase and an increased focus on data quality.

Tutorials

Introduction to data visualization with r and ggplot2 in terms of its sheer breadth and control over crafted data visualizations. Better visualization ensures better data analysis.

Tutorials

Data storytelling is the most effective communication tool in data science. Impactful storytelling with Power BI can be of great help in the world of data.

Tutorials

Introduction to the best practices of designing and evaluating A/B and multi-variate tests. Discussion on the choice of appropriate metrics, avoidance of errors, and interpretation of results.

Tutorials

Introduction to the use of Caret in implementing some of the most common tasks of a data science project. The significance of Caret in the daily tasks of a data scientist.

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Using the expertise in Microsoft Excel to quickly learn R programming. Application of R in data analysis and visualization with the help of prior knowledge of Excel.

Tutorials

An introduction to the use of R visualizations using Microsoft's free Power BI desktop software. A hands-on experience of Power BI can be of immense value in data science.

Tutorials

Proper analysis of business data is one of the fundamental skills for a data professional. Microsoft Excel is a strong tool that can ensure data analysis in a robust manner.

Tutorials

Event logs are everywhere and these represent a prime source of Big Data. Using the OSS tools of R and ProM, a data scientist can create mine event logs efficiently.

Tutorials

An introduction to the unstructured text and how to make sense of it with the help of text and sentiment analysis taken fro, some notable examples in the industry.

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Discussion on wrangling data out of an HDFS and building machine learning models from a large dataset that can then be deployed to an elastically scaled web service for insights.

Tutorials

An introduction to the organizational structure and science behind LIGO detectors, and the data processing techniques that enabled gravitational wave detection.

Tutorials

An introduction to building a real-time analytics pipeline from sentiment classification model in Azure ML and R. Real-time dashboarding with Power BI and C# using a Twitter dataset.

Tutorials

Kaggle is an online community for data scientists that offers machine learning competitions. Complete the Titanic model in Azure ML to get better insights into Kaggle problems and their solutions.

Tutorials

An introduction to Kaggle followed by a guide on how to create a Kaggle account and submit a model to the Kaggle competition. Discussion on typical problems in Kaggle and how to succeed.

Tutorials

A recommender system is an automated system that uses user preference to predict things. A discussion on the importance of recommender systems and how do they work.

Tutorials

Introduction to the technique of clustering and the fundamental concepts associated with it. Discussion on clustering methods along with their strengths and weaknesses.