Analytics Expert Program

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Analytics Expert Program

Key Program Benefits:

  • Essential Math and Stat for Analytics – Unique Thinking in Analytics approach using innovative techniques & excel
  • Business Intelligence – Learn how to transform , reshape, prepare data for modeling using Power BI or Tableau
  • Programming for Data Analytics – Understand how to analyze data using R or Python
  • Predictive Modeling – Learn how to convert data to insights by applying fundamental statistical learning techniques
  • Advanced Machine Learning – Improve model accuracy and performance by using cutting edge ML algorithms & its applications
  • Other Data Analytics Aspects – Get an introduction to AI Applications, Big Data Ecosystem and Big Data Analytics.

*Conditions Apply

Program Goals

Provide a strong statistical thinking with a good understanding of the applications of probability, calculus and linear algebra in model building. Build a strong reasoning framework to convert any business problem into an inferential analytics framework.

  • Thinking in Analytics
  • Probability
  • Descriptive Statistics
  • Correlation Vs Regression
  • Central Limit Theorem
  • Hypothesis Testing
  • T-Test, Chi-Square Test and F-Test
  • Introduction to Calculus
  • Introduction to Linear Algebra
Essential Math and Stat for Analytics
Program Goals

Provide a solid foundation in programming concepts required for a successful data analyst and data scientist. We learn to use R to perform data analysis and visualization. Additionally we inculcate skills to investigate data issues interactively using R Studio.

  • Data Types and Data Structures
  • Loops, Functions and Programming Elements
  • Basics of Object Oriented Programming
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn
  • Handling time series data
  • Handling strings
  • Building Python dashboards using plotly
R for Data Science
Program Goals

Convert raw data into insights and help understand what factors drive a specific business outcome and to what extent. We help in building strategies to link these models to business actions.

  • Simple Linear Regression (Marketing)
  • Multiple Regression (Marketing)
  • Logistic Regression (Finance)
  • KNN and Naïve Bayes (HR)
  • Decision Trees (Finance)
  • Bagging, Boosting & Random Forests (Finance)
  • Lasso and Ridge Regression (Finance)
  • Clustering (Marketing)
  • PCA (Marketing)
  • Neural Networks (Stock Market)
  • Support Vector Machines (Stock Market)
  • Introduction to Time Series Analysis (Finance)
  • Time Series Forecasting (Marketing)
  • Natural Language Processing (Marketing)
  • Sentiment Analysis (HR)
Machine Learning - Algorithms & Implementation
Program Goals

To get a robust understanding of Data Science, knowledge of few other topics is very crucial. We will mainly focus on some of the other important topics needed for a Data Science professional in this section

  • Overview of SQL
  • Introduction to Tableau
  • Building dashboards in Tableau
  • Introduction to Power BI
  • Data Preparation in Power BI
  • Building dashboards in Power BI
  • Introduction to Deep Learning and AI
  • AI Applications in business
  • Introduction to Big Data and its Ecosystem
  • Introduction to Big Data Analytics
Other Aspects of Data Science