Business Analytics with R and Python
A program to equip oneself with 2 of top 5 most sought after applications of current times and make most out of the opportunities offered by fast growing Analytic Industry. The tools are used across enterprises (Like Google, Amazon etc) and at individual level equally.
The program is not to pass information around functionalities but aims to engage you in an involved learning of the applications and gear you to apply real world scenario in conjunction with Amazon AWS and Apache Spark.
- Build capability to use R & Python to execute BI & analytics project for structured and textual data in conjunction with Amazon AWS and Apache Spark.
- Understand fundamentals of analytics using statistics and predictive analytics techniques.
- Learn to work with Social media & unstructured text data using R and Python.
- A starter for individuals keen to build base to practice and further develop the understanding of analytics and to give a flip to their career.
- A program to build a strong knowledge base of R & Python along with understanding of application of key analytic techniques.
- Program accredited and certified by Analytics Society of India.
- Sessions handled by practicing professionals and IIMB alumni.
- Case study based approach.
- Course pedagogy comprises of short assignment.
- Spans across 45 hours of 100% in classroom knowledge sharing.
- Non-disruptive weekend program.
- V Connect – Acts as a platform to connect with industry experts and get mentored.
- Route α – Your club to connect and progress.
- Overview of Analytics, Machine learning & Big data.
- Overview of analytical tools in the market and comparative analysis.
Learning R & Statistical analysis
- Overview & getting started with R.
- Libraries, R objects, attributes, operators, vectors, lists, matrices, factors, missing values.
- Data Frameworks, subsetting, managing large data, data Interface with other tools.
- Control Structures, functions, loops, macros, debugging, operations, APIs.
- Fundamentals of Statistics: Types of data, Central Tendencies, Central limit theorem, Probability distribution, Histogram, Hypothesis Testing, Sampling, Confidence Interval.
- Missing and Sparse data treatment using R .
- Introduction to Rattle, RMarkdown and RShiny.
- Data visualization, graphs and plots in R.
- Interface of R with Apache Spark.
Predictive & Machine Learning technique with R
- Linear regression, logistic regression and Decision tree concepts along with application using R
- Machine Learning:
-Gradient Descent using R – Logistic Regression Implementation
-Bagging and Boosting
- Overview & fundamentals of Python: Library, data framework, declaring variables, arithmetic & logical operations, using built-in functions, conditional statements, control flow statements, plots & graphs.
- Working with collections – List, tuple, set & dictionary, dealing with strings, functions, loops, lambda functions, classes.
- Working with web APIs, HTMLS, XML, SQL queries in Python.
- Application of Regression & Decision tree techniques in Python.
- Machine Learning in Python:
- Cloud computing platforms for Big Data analysis using Amazon AWS with R and Python.
- Interface of Python with Apache Spark.
-Gradient Descent using Python – Logistic Regression Implementation
Social media analytics using R & Python
- Social media analytics & Text mining with unstructured textual data using R and Python.