If you're interested in the exciting world of data science, but don't know where to start, then this course is the beginning for you.
This course designed to introduce participant’s to this rapidly growing field and equip them with some of its basic principles and frequently used tools.
A data scientist requires skill sets spanning mathematics, statistics, machine learning and knowledge of data analytics software like Python, R and SAS.
To make the learning contextual, case studies from a variety of disciplines used in this course.
Basic Operations in R
Different Data Types and Data Structures in R
Subsetting in R
Vectors
Logical Operator
If ELSE (Conditional Processing)
Loops
While loop
Functions
Create own function
Create matrices
Colnames() Rownames()
Matrix Operations
Subset matrices
Import Data
Operations on data frame
Application on data frame
Filter your data
Array and data manipulation
Plots and Charts in R
Merging and Sorting Functions in R
Summarising Data
Concept of Hypothesis
Null Hypothesis
Alternative Hypothesis
Type-I error
Type-II error
Level of Significance
Confidence Interval
One Sample T-Test
Two independent sample T-Test
Paired Sample T-Test
Chi square Test for Independence of Attributes
Concept of Regression and features of Linear Line.
Assumptions of Classical Linear Model
Method of Least Squares
Understanding the Goodness of Fit
Test of Significance of the Estimated Parameters
Multiple linear Regression with their Assumptions
Concept of Multicollinearity
Signs of Multicollinearity
The Idea of Autocorrelation
Concept and Applications of Logistic Regression
Principles behind Logistic Regression
Comparison between Linear Probability Model and Logistic Regression
Semi-Partial R-Square and R-Square
Principal Component Analysis
Eigen Values and Eigen Vectors
Correlation Matrix check and KMO-MSA check
Factor loading Matrix
Diagrammatic Representation of Factors
Problems of Factor Loading and Solutions
Concept of Time Series and its Applications
Assumptions of Time Series Analysis
Components of Time Series
Smoothening techniques
Stationarity
ARIMA Forecasting
Box Jenkins Technology
Introduction to SAS Programs
Running SAS Programs
Mastering Fundamental Concepts
Diagnosing and Correcting Syntax Errors
Exploring Your SAS Environment
Reading Raw Data Files: Column Input
Reading Raw Data Files: Formatted Input
Examining Data Errors
Assigning Variable Attributes
Changing Variable Attributes
Reading Excel Spreadsheets
Types of Analytics
Properties of Measurements
Scales of Measurement
Types of Data
Measures of Central Tendency
Measures of Dispersion
Measures of Location
Presentation of Data
Skewness and Kurtosis
Three Approaches towards Probability
Concept of a Random Variable
Probability Mass Function
Probability Density Function
Expectation of a Random Variable
Probability Distributions
Concept of hypothesis
Null hypothesis
Alternative hypothesis
Type-I error
Type-II error
Level of Significance
Confidence Interval
Parametric Tests and Non Parametric Tests
One Sample T test
Two independent sample T test
Paired Sample T test
Chi square Test for Independence of Attributes
Concept and Applications of Logistic Regression
Principles behind Logistic Regression
Comparison between Linear Probability Model and Logistic Regression
Mathematical Concepts related to Logistic Regression
Concordant Pairs, Discordant Pairs and Tied Pairs
Concept of Time Series and its Applications
Assumptions of Time Series Analysis
Components of Time Series
Smoothening Techniques
Stationarity
Random Walk
ARIMA Forecasting
Principal Component Analysis
Estimating the Initial Communalities
Eigen Values and Eigen Vectors
Correlation Matrix check and KMO-MSA check
Factor loading Matrix
Diagrammatic Representation of Factors
Problems of Factor Loading and Solutions
Types of Clusters
Metric and linkage
Ward’s Minimum Variance Criteria
Semi-Partial R-Square and R-Square
Diagrammatic Representation of clusters
Problems of Cluster Analysis
Concept of Regression and features of Linear Line.
Assumptions of Classical Linear Model
Method of Least Squares
Understanding the Goodness of Fit
Multiple linear Regression with their Assumptions
Concept of Multicollinearity
Signs of Multicollinearity
The Idea of Autocorrelation
Highly recommended if you want to learn or improve your Data Science skill. This course is fantastic -- the concepts & methods that are directly applicable to my field. The numerous examples and case studies are really useful for understanding all the concepts. The instructor is very good and shares her knowledge at a good pace.For the value, the course was worth every penny and more.
Manisha
Data Science course explained in detail and with examples.great case studies which helped me to understand more easily.
Sumitava Maji
The course structure is very good and useful for the student.
Soumen Lahiri
I have completed Data Science course from Hands-On System. It is really a great experience.