# Basic Data Science

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Introduction:
1.What are Data Analysis, Data Analytics and Data Science?
3.Case study of Walmart
Various Analytics Tools:
1.Descriptive
2.Predictive
3.Web Analytics
Fundamentals Of R:
1.R and features
2.Evolution of R?
Working With R & RStudio:
1.R & RStudio Installation
Data Types:
2.Scalar
3.Vectors
4.Matrix
5.List
6.Data frames
7.Factors
8.Handling date in R
9.Conversion of data types
10.Operators in R
Importing Data:
1.JSON files
2.CSV files
3.Database data (Oracle 11g)
4.XML files
7.Saving Data in R
Manipulating Data:
1.Sorting
2.Cbind, Rbind
3.Aggregating
4.Dplyr
Conditional Statements:
1.For loop
2.If …else
3.While loop
4.Repeat loop
Functions:
1.tApply ()
2.Apply ()
3.sApply ()
4.rApply ()
Statistical Concepts:
1.Descriptive Statistics
2.Inferential Statistics
3.Central Tendency (Mean,Mode,Median)
4.Hypothesis Testing
5.Probability
6.tTest
7.zTest
8.Chi Square test
9.tTest
10.Correlation
11.Covariance
12.Anova
Predictive Modelling:
1.Linear Regression
2.Normal Distribution
3.Density
Data Visualization In R Using GGPlot:

1.Box Plot
2.Histograms
3.Scatter Plotter
4.Line chart
5.Bar Chart
6.Heat maps
Data Visualization Using Plotly:
1.3D-view
2.Geo Maps
Misc. Functions:
1.Null Handling
2.Merge
3.Grep
4.Scan
1.Text Mining
2.Exploratory Data Analysis
3.Machine Learning with R (concept)
Data Science With R:
1.Algorithms
2.Classifications
3.Clustering
4.Supervised learning
5.Unsupervised learning
6.Bayesian
7.Boosting
8.K-means
9.Nearest Neighbours (KNN)
10.Page Rank
11.Support Vector Machines
12.Random forest

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