Introduction to data science:
In this free Data Science tutorial you will have the introduction to Data Scientist roles and responsibilities, machine learning algorithms,data analysis, data manipulation, data frame, random forest, linear and logistic regression, decision trees, neural networks, Python language, Python libraries, data model, variable, set, and more.There are plenty of Data Science use cases and practical examples.Data science helps the user by providing an ability to analyze huge data sets and by doing necessary operations, data science will save precious time and makes some big profit out of it.
COURSE CONTENT:
1.
What is Data Science?
2.
Business Intelligence vs Data Science
3.
Life cycle of Data Science
4.
Tools of Data Science
5.
Introduction to R
2.
Data Science
with R
1.
Introduction
2.
About S
3.
About R
4.
About CRAN
5.
Installation of R
6.
About working directory
7.
Changing working directory temporarily
8.
Changing working directory permanently
9.
Installation of R studio
10.
Atomic Datatypes in R
3.
R Data Types
1.
Arithmetic in R
2.
Using R as a calculator (+, -, *, / ...etc)
3.
Variable assignment
4.
Basic Datatypes in R(Numeric, integer, character, logical and
complex)
5.
Data type coercion
6.
aBasic Functions (class, dim, str, summary, ls, remove, as.*,
is.* ...etc)
4.
Vectors
1.
Creating vector
2.
Naming Vector
3.
Vector selections
4.
Adding elements to vector
5.
Update elements of vector
6.
Delete elements of vector
7.
Functions (c(), names()
...etc)
5.
Matrices
1.
What is matrix
2.
Create matrix
3.
Naming a matrix
4.
Arithmetic with matrix
5.
Adding row
6.
Adding column
7.
Selection of matrix elements
8.
Insert /delete/update matrix elements
9.
Transpose matrix
10.
Combine rows of matrix
11.
Combine columns of matrix
6.
Factors
1.
Categorical variables
2.
Continuous variables
3.
What is factor
4.
Factor Levels in customized format
5.
Nominal factors
6.
Ordinal factors
7.
Data Frames
1.
What is data frame
2.
Creating Data frame
3.
Add /delete/update data frame elements
4.
Summary function understanding (Mean, median, min, max, 1st
QU, 3rd Qu ...etc)
5.
Functions (head, tail, sort, order, nrow, ncol … etc)
8.
Lists
1.
Creating list
2.
Named List
3.
Add /delete /Modify list
4.
Merge lists
5.
Nested lists
6.
Query list elements
7.
Convert list to vector
9.
Read data
from files
1.
Read data from .dat files
2.
Read data from .txt files
3.
Read data from .csv files
4.
Read data from .xls files
5.
Read data from .stata files
6.
Read data from fixed files
7.
Read data from delimited files
8.
Working with functions (read.table(), read.csv, read.fwf(),
read.delim(), read.csv2(), scan(), readlines(), file() …etc)
10.
Read data
from Relational Databases
1.
Read data from commercial RDBMS
1.
Oracle
2.
Teradata
3.
..etc
2.
Read data from open source databases
1.
SQLite
2.
PostgreSQL
3.
MySQL
4.
.etc
11.
Write data
to text files
1.
Write data to files (EXCEL,
Text Files, SAS, SPSS, STATA …etc)
2.
About write.table(),write.csv(),
write.csv2(),cat() ,writelines(), sink(), dump(), dput(), save(), load() functions
12.
Write data
to Rdbms
1.
Write data to commercial RDBMS
1.
Oracle
2.
Teradata
3.
..etc
2.
Write data to open source databases
1.
SQLite
2.
PostgreSQL
3.
MySQL
4.
.etc
13.
Conditionals
and Control Flow
1.
Working with relational operators (== ,>=, <=, !=, <,
>)
2.
Working with logical operators(and ,or, not)
3.
If statements
4.
If else statements
5.
If else if statements
6.
Switch function
14.
Loops
1.
Repeat
2.
Break
3.
Next
4.
While
5.
For
15.
Functions
1.
Introduction to functions
2.
Function documentation
3.
Use a function
4.
Create own function
5.
Nested functions
6.
Function scoping
16.
Packages
1.
Default packages
2.
Create package
3.
Attach package...etc
17.
Apply family
1.
lapply
2.
sapply
3.
vapply
18.
Graphics
systems in R
1.
Base graphics
1.
Plot
2.
Histogram
3.
Scatter
4.
Bar plot
5.
Qqplot
6.
Sunflowerplot
7.
Boxplot
8.
Add more detail to graphs
2.
Grid graphics
3.
Lattice graphics
4.
ggplot2 graphics
1.
Data layer
2.
Aesthetics layer
3.
Geometries layer
4.
Facets layer
5.
Statistics layer
6.
Coordinates layer
7.
Themes layer
19.
Cleaning
data( equal to ETL work)
1.
gather function
2.
spread() function
3.
separate() function
4.
unite() function
5.
Working with lubridate package
6.
Working with stringr package
7.
Working with Missing values
8.
Working with Special values
20.
Machine
learning& Artificial intelligence
1.
What is machine learning?
2.
What is ETP?
3.
Types of machine learning
1.
Supervised learning
2.
Unsupervised learning
3.
Semi-supervised Learning
4.
Reinforcement learning
4.
Algorithms or model for machine learning
1.
Linear Regression
2.
Logistic Regression
3.
Jackknife Regression *
4.
Density Estimation
5.
Confidence Interval
6.
Test of Hypotheses
7.
Pattern Recognition
8.
Clustering - (aka Unsupervised Learning)
9.
Supervised Learning
10.
Time Series
11.
Decision Trees
12.
Random Numbers
13.
Monte-Carlo Simulation
14.
Bayesian Statistics
15.
Naive Bayes
16.
Principal Component Analysis - (PCA)
17.
Ensembles
18.
Neural Networks
19.
Support Vector Machine - (SVM)
20.
Nearest Neighbours - (k-NN)
21.
Feature Selection - (aka Variable Reduction)
22.
Indexation / Cataloguing *
23.
(Geo-) Spatial Modelling
24.
Recommendation Engine *
25.
Search Engine *
26.
Attribution Modelling *
27.
Collaborative Filtering *
28.
Rule System
29.
Linkage Analysis
30.
Association Rules
31.
Scoring Engine
32.
Segmentation
33.
Predictive Modelling
34.
Graphs
35.
Deep Learning
36.
Game Theory
37.
Imputation
38.
Survival Analysis
39.
Arbitrage
40.
Lift Modelling
41.
Yield Optimization
42.
Cross-Validation
43.
Model Fitting
44.
Relevancy Algorithm *
45.
Experimental Design
21.
Utilities
1.
Mathematical utilities
2.
Data Utilities
3.
Beat Gauss using R
4.
grepl&grep
6.
Times and Dates
22.
Tableau
23.
Power BI
