Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured,which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics, similar to knowledge discovery in database.
Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.
Statistics is the Phase I of Data science
And Phase II of Data science is R Language,to view the details of R Language CLICK HERE
course content Of Statistics
Introduction to Statistics.
Five Number Summary.
The Centre of the Data and the Effects of Extreme Values.
The Spread of the Data.
The Shape of the Data.
Some Features of Data.
Relationships Between Quantitative and Categorical Variables.
Examining Relationships Between Two Categorical Variables.
Relationships Between Two Quantitative Variables.
The Need for Probability.
Some Probability Basics.
Introduction to Confidence Intervals.
Confidence Intervals for Proportions.
Sample Size for Estimating a Proportion.
Confidence Intervals for Means.
Robustness of Confidence Intervals.
Introduction to Statistical Tests.
The Structure of Statistical Tests.
Hypothesis Testing for Proportions.
Hypothesis Testing for Means.
Power and Type I and Type II Errors.
Connection Between Confidence Intervals and Hypothesis Testing.
Comparing Two Proportions.
Comparing Two Means.
The Linear Regression Formula.
Regression Coefficients Residuals and Variances.
Regression Inference and Limitations.
Residual Analysis and Transformations.
Phase II of Data science
History of R.
Advantages and disadvantages.
Downloading and installing.
How to find documentation.
Using the R console.
Learning about the environment.
Writing and executing scripts.
Saving your work.
Data Structures and Variables.
Variables and assignment.
Viewing data and summaries.
Getting Data into the R environment.
Reading local data.
Overview of Statistics in R.
Introduction to R Graphics.
Scatter plot,Box plot.
T-test and non-parametric equivalents.
Chi-squared test, logistic regression.
Object Oriented R.
More about Graphics.
Sophisticated Graphics in R.
R for Mapping and GIS
Also check Big data Anlaytics training Using R
Data Mining Training in Chennai
Introduction to Data mining
Machine Learning is Phase IV of Data science
Introduction, Regression Analysis and Gradient Descent
Linear Algebra – review
Linear Regression with Multiple Variables
machine learning techniques hands-on
Machine Learning System Design
Support Vector Machines
Large Scale Machine Learning
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