INTRODUCTION:

This course is designed for professionals who want to explore data incepting from cleaned data set to Statistical Analysis and recommend most optimized solution. This course helps to processes and methods to extract expertise from data in several forms, like structured or unstructured,which is a continuation of a number of a data analysis system.

A) Business analytics with Excel, Decision Making Concept in Excel.
B) Fundamentals of R, Data science including data management, business analytics, regression analysis, time series analysis, data visualization with ggplot, Statistics for data science(Hypothesis testing,population Means, probability, descriptive statistics etc) in R Language.

C) Fundamentals of Python, Loading Different Packages such as Numpy, Pandas, Matplotlib etc, Graphical Representation of Data using Matplotlib & seaborn,Normality Testing, Parametric Test such as T-test, z-test, One way Anova, two way anova, chi-square test, Graphical User interface Packages in Python.

D) Various Machine Learning Algorithm including Logistic Regression, Linear Regression, Support Vector Machines, Nearest Neighbours, Naive Bayer Classification, Decesion Tree Classification, Random Forest Classification, K Fold cross Validation in R & Python both.

F) Fundamentals of Tableau, Load Data from Excel, Application of Discreate and Continuous Fields, creating various charts in Tableau(Bar-chart, Stacked Bar Chart, Scatter plot, Line chart, pie chart, Funnel chart), cross tabs, Maps, Highlight Tables, Filtering( Dimension Filter, Date filter, Measure filter, Visual filter, Context filter).

Note:- All R, Python, Excel & Tableau resources are downloadable.