Data Analytics for Practitioners

Why should you attend?

This Data Analytics for Practitioners course offers a deep dive into the multifaceted world of data analytics, with a focus on practical applications and real-world case studies. Participants will begin by grounding their knowledge in essential mathematical concepts such as Probability, Statistics, Linear Algebra, and Calculus, which are pivotal in understanding data patterns and algorithms. As the course progresses, learners will explore the business implications of text analytics, learning to categorize customer sentiments, prevent churn, and enhance scoring models that drive decision-making in lending, marketing, and credit sectors. The curriculum includes building robust text data pipelines using ElasticSearch (ES), enabling efficient data ingestion and processing. Students will be engaged in hands-on exercises that include customer segmentation, sentiment analysis, and predictive modeling using various machine learning techniques like decision trees, logistic regression, neural networks, and clustering methods like k-means. Advanced topics will cover sophisticated classification techniques using Word2Vec/Doc2Vec and Long Short-Term Memory (LSTM) networks for nuanced customer complaint segmentation. Additionally, the course delves into setting up comprehensive data architectures with tools like Nifi and Pentaho while tackling both SQL and NoSQL storage solutions. Finally, learners will sharpen their skills through practical exercises on advanced analytics and visualization using Python libraries such as seaborn, gleam, and plotly.