http://forums.fast.ai/t/run-jupyter-notebook-on-system-boot/749/4

# Credit Stocks Derivatives Finance CFA FRM Course: Free Classes & Strategy

Official Blog QCfinane.in Courses Downloads Ppts, Classes Videos: CFA FRM MATLAB R VBA

## Thursday, March 15, 2018

## Friday, March 2, 2018

### ico

https://github.com/JincorTech/backend-ico-dashboard

https://jincortech.github.io/backend-ico-dashboard/#initiate-password-change-post

docker-compose exec ico systemctl status mongod

https://www.digitalocean.com/community/tutorials/how-to-install-mongodb-on-ubuntu-16-04

https://docs.mongodb.com/tutorials/connect-to-mongodb-shell/

https://docs.docker.com/samples/library/mongo/#connect-to-it-from-an-application

## Thursday, March 1, 2018

### Backend Developer

Backend Developer with Web development and http, tcp, web-sockets

Modern NoSQL datastores like MongoDB, Redis

Web/mobile application development using Python

Python web framework Worked with MongoDB

Containers or VM like Docker, Kubernetes, Vagrant

Ansible and Jenkins

AWS products: For Compute, Storage, Database or Networking sections

Solr or ElasticSearch

Product startup which have ranked within top 30 in Play Store or iOS App Store

https://blog.codeship.com/using-docker-compose-for-nodejs-development/

https://sub.watchmecode.net/guides/build-node-apps-in-docker/

https://blog.docker.com/2016/07/live-debugging-docker/

https://medium.com/@creynders/debugging-node-apps-in-docker-containers-through-webstorm-ae3f8efe554d

Modern NoSQL datastores like MongoDB, Redis

Web/mobile application development using Python

Python web framework Worked with MongoDB

Containers or VM like Docker, Kubernetes, Vagrant

Ansible and Jenkins

AWS products: For Compute, Storage, Database or Networking sections

Solr or ElasticSearch

Product startup which have ranked within top 30 in Play Store or iOS App Store

https://blog.codeship.com/using-docker-compose-for-nodejs-development/

https://sub.watchmecode.net/guides/build-node-apps-in-docker/

https://blog.docker.com/2016/07/live-debugging-docker/

https://medium.com/@creynders/debugging-node-apps-in-docker-containers-through-webstorm-ae3f8efe554d

## Friday, February 16, 2018

### Ethereum Solidity Coding

Ethereum Solidity Coding

## Wednesday, January 31, 2018

### Bitcoin Blockchain

Nice Videos:

https://www.youtube.com/watch?v=pLJQy0B5OKo

## Tuesday, December 26, 2017

### Deep Learning Skills / Data Science

Programming languages (Python, R, Lua, Scala …) and multiple frameworks and technologies (Tensorflow, Torch, Hadoop, Spark, RDBMS…) to support the modeling requirements

Deep learning, other AI, natural language processing, data mining, information theory, and optimization

Python, R, Lua, Scala, C++

Major deep learning libraries:. TensorFlow, Torch, DeepLearning4J

GPU (CUDA), ASIC, or FPGA

Distributed system (e.g. Spark, Hadoop, Ignite …)

Big data visualization

Substantial programming experience with almost all of the following: SAS (STAT, macros, EM), R, H2O, Python, SPARK, SQL, other Hadoop. Exposure to GitHub.

Modeling techniques such as linear regression, logistic regression, survival analysis, GLM, tree models (Random Forests and GBM), cluster analysis, principal components, feature creation, and validation. Strong expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.) and validation (hold-outs, CV, bootstrap).

Database systems (Oracle, Hadoop, etc.), ETL/data lineage software (Informatica, Talend, AbInitio)

Data visualization (e.g. R Shiny, Spotfire, Tableau)

AWS ecosystem: experience with S3, EC2, EMR, Lambda, Redshift

Data pipelines Airflow, Luigi, Talend, or AWS Data Pipeline

APIs: Google, YouTube, Facebook, Twitter, or Oauth

version control (Github, Stash etc.)

Deep learning, other AI, natural language processing, data mining, information theory, and optimization

Python, R, Lua, Scala, C++

Major deep learning libraries:. TensorFlow, Torch, DeepLearning4J

GPU (CUDA), ASIC, or FPGA

Distributed system (e.g. Spark, Hadoop, Ignite …)

Big data visualization

Substantial programming experience with almost all of the following: SAS (STAT, macros, EM), R, H2O, Python, SPARK, SQL, other Hadoop. Exposure to GitHub.

Modeling techniques such as linear regression, logistic regression, survival analysis, GLM, tree models (Random Forests and GBM), cluster analysis, principal components, feature creation, and validation. Strong expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.) and validation (hold-outs, CV, bootstrap).

Database systems (Oracle, Hadoop, etc.), ETL/data lineage software (Informatica, Talend, AbInitio)

Data visualization (e.g. R Shiny, Spotfire, Tableau)

AWS ecosystem: experience with S3, EC2, EMR, Lambda, Redshift

Data pipelines Airflow, Luigi, Talend, or AWS Data Pipeline

APIs: Google, YouTube, Facebook, Twitter, or Oauth

version control (Github, Stash etc.)

## Sunday, December 24, 2017

### VBA code for loops play

'Option Explicit

Sub Sample()

Dim i As Long, j As Long, k As Long, l As Long

Dim CountComb As Long, lastrow As Long

Range("G2").Value = Now

Application.ScreenUpdating = False

CountComb = 0: lastrow = 6

For i = 1 To 4: For j = 1 To 4

For k = 1 To 8: For l = 1 To 12

Range("G" & lastrow).Value = Range("A" & i).Value & "/" & _

Range("B" & j).Value & "/" & _

Range("C" & k).Value & "/" & _

Range("D" & l).Value

lastrow = lastrow + 1

CountComb = CountComb + 1

Next: Next

Next: Next

Range("G1").Value = CountComb

Range("G3").Value = Now

Application.ScreenUpdating = True

End Sub

Sub Sample2()

Dim i As Long, j As Long, k As Long, l As Long

Dim CountComb As Long, lastrow As Long

Application.ScreenUpdating = False

CountComb = 0: lastrow = 6

For i = 1 To 4: For j = 1 To 4

For k = 1 To 8: For l = 1 To 12

Cells(i, 20) = i

Cells(j, 21) = j

Cells(k, 22) = k

Cells(l, 23) = l

lastrow = lastrow + 1

CountComb = CountComb + 1

Cells(lastrow, 24) = lastrow

Cells(CountComb, 25) = CountComb

Next: Next

Next: Next

Application.ScreenUpdating = True

End Sub

Sub Sample3()

Dim i As Long, j As Long, k As Long, l As Long

Dim CountComb As Long, lastrow As Long

Application.ScreenUpdating = False

CountComb = 0: lastrow = 6

For i = 1 To 4

For j = 1 To 4

For k = 1 To 8

For l = 1 To 12

Cells(i, 20) = i

Cells(j, 21) = j

Cells(k, 22) = k

Cells(l, 23) = l

lastrow = lastrow + 1

CountComb = CountComb + 1

Cells(lastrow, 24) = lastrow

Cells(CountComb, 25) = CountComb

Next l

Next k

Next j

Next i

Application.ScreenUpdating = True

End Sub

Sub playarray()

Dim myThirdColumn As Variant

myThirdColumn = Application.Index(myArray, , 3)

End Sub

' https://usefulgyaan.wordpress.com/2013/06/12/vba-trick-of-the-week-slicing-an-array-without-loop-application-index/

Sub Test()

Dim varArray() As Variant

Dim varTemp() As Variant

Dim myRng As Range

'Application.Index([A1:E10], , 2) = Application.Index(varArray, , 2)

Set myRng = Worksheets("SheetA").Range("A1:E10")

varArray = myRng.Value

varTemp = Application.Index(varArray, , 2)

' varTemp = Application.Index(varArray, Array(2, 3), 0)

' varTemp = Application.Index(varArray, , Application.Transpose(Array(2)))

MsgBox UBound(varTemp) - LBound(varTemp) + 1

'MsgBox varArray(1, 1)

End Sub

Sub Test2()

Dim varArray() As Variant

Dim varTemp() As Variant

Dim myRng As Range

'Application.Index([A1:E10], , 2) = Application.Index(varArray, , 2)

Set myRng = Worksheets("SheetA").Range("A1:Z10")

varArray = myRng.Value

varTemp = Application.Index(varArray, 3)

varTemp2 = Application.Index(varArray, , 3)

' varTemp = Application.Index(varArray, Array(2, 3), 0)

' varTemp = Application.Index(varArray, , Application.Transpose(Array(2)))

'MsgBox UBound(varTemp) - LBound(varTemp) + 1

'MsgBox varArray(1, 1)

'MsgBox UBound(varTemp2) - LBound(varTemp2) + 1

MsgBox varTemp2(10, 1)

' VBA Array starts at 1

End Sub

''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''

Sub Test3()

Dim varArray() As Variant

Dim varTemp() As Variant

Dim myRng As Range

'Application.Index([A1:E10], , 2) = Application.Index(varArray, , 2)

Set myRng = Worksheets("SheetA").Range("A1:Z10")

varArray = myRng.Value

varTemp = Application.Index(varArray, Array(1, 2))

'first two row elements

'varTemp2 = Application.Index(varArray, , 3)

' varTemp = Application.Index(varArray, Array(2, 3), 0)

' varTemp = Application.Index(varArray, , Application.Transpose(Array(2)))

' MsgBox Array(1, 2)(0)

MsgBox varTemp(1)

' the first element actually using array command

' the above var temp starts with 1 and not with 0

'MsgBox UBound(varTemp) - LBound(varTemp) + 1

'MsgBox varArray(1, 1)

'MsgBox UBound(varTemp2) - LBound(varTemp2) + 1

'MsgBox varTemp2(10, 1)

' VBA Array starts at 1

End Sub

Sub Sample()

Dim i As Long, j As Long, k As Long, l As Long

Dim CountComb As Long, lastrow As Long

Range("G2").Value = Now

Application.ScreenUpdating = False

CountComb = 0: lastrow = 6

For i = 1 To 4: For j = 1 To 4

For k = 1 To 8: For l = 1 To 12

Range("G" & lastrow).Value = Range("A" & i).Value & "/" & _

Range("B" & j).Value & "/" & _

Range("C" & k).Value & "/" & _

Range("D" & l).Value

lastrow = lastrow + 1

CountComb = CountComb + 1

Next: Next

Next: Next

Range("G1").Value = CountComb

Range("G3").Value = Now

Application.ScreenUpdating = True

End Sub

Sub Sample2()

Dim i As Long, j As Long, k As Long, l As Long

Dim CountComb As Long, lastrow As Long

Application.ScreenUpdating = False

CountComb = 0: lastrow = 6

For i = 1 To 4: For j = 1 To 4

For k = 1 To 8: For l = 1 To 12

Cells(i, 20) = i

Cells(j, 21) = j

Cells(k, 22) = k

Cells(l, 23) = l

lastrow = lastrow + 1

CountComb = CountComb + 1

Cells(lastrow, 24) = lastrow

Cells(CountComb, 25) = CountComb

Next: Next

Next: Next

Application.ScreenUpdating = True

End Sub

Sub Sample3()

Dim i As Long, j As Long, k As Long, l As Long

Dim CountComb As Long, lastrow As Long

Application.ScreenUpdating = False

CountComb = 0: lastrow = 6

For i = 1 To 4

For j = 1 To 4

For k = 1 To 8

For l = 1 To 12

Cells(i, 20) = i

Cells(j, 21) = j

Cells(k, 22) = k

Cells(l, 23) = l

lastrow = lastrow + 1

CountComb = CountComb + 1

Cells(lastrow, 24) = lastrow

Cells(CountComb, 25) = CountComb

Next l

Next k

Next j

Next i

Application.ScreenUpdating = True

End Sub

Sub playarray()

Dim myThirdColumn As Variant

myThirdColumn = Application.Index(myArray, , 3)

End Sub

' https://usefulgyaan.wordpress.com/2013/06/12/vba-trick-of-the-week-slicing-an-array-without-loop-application-index/

Sub Test()

Dim varArray() As Variant

Dim varTemp() As Variant

Dim myRng As Range

'Application.Index([A1:E10], , 2) = Application.Index(varArray, , 2)

Set myRng = Worksheets("SheetA").Range("A1:E10")

varArray = myRng.Value

varTemp = Application.Index(varArray, , 2)

' varTemp = Application.Index(varArray, Array(2, 3), 0)

' varTemp = Application.Index(varArray, , Application.Transpose(Array(2)))

MsgBox UBound(varTemp) - LBound(varTemp) + 1

'MsgBox varArray(1, 1)

End Sub

Sub Test2()

Dim varArray() As Variant

Dim varTemp() As Variant

Dim myRng As Range

'Application.Index([A1:E10], , 2) = Application.Index(varArray, , 2)

Set myRng = Worksheets("SheetA").Range("A1:Z10")

varArray = myRng.Value

varTemp = Application.Index(varArray, 3)

varTemp2 = Application.Index(varArray, , 3)

' varTemp = Application.Index(varArray, Array(2, 3), 0)

' varTemp = Application.Index(varArray, , Application.Transpose(Array(2)))

'MsgBox UBound(varTemp) - LBound(varTemp) + 1

'MsgBox varArray(1, 1)

'MsgBox UBound(varTemp2) - LBound(varTemp2) + 1

MsgBox varTemp2(10, 1)

' VBA Array starts at 1

End Sub

''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''

Sub Test3()

Dim varArray() As Variant

Dim varTemp() As Variant

Dim myRng As Range

'Application.Index([A1:E10], , 2) = Application.Index(varArray, , 2)

Set myRng = Worksheets("SheetA").Range("A1:Z10")

varArray = myRng.Value

varTemp = Application.Index(varArray, Array(1, 2))

'first two row elements

'varTemp2 = Application.Index(varArray, , 3)

' varTemp = Application.Index(varArray, Array(2, 3), 0)

' varTemp = Application.Index(varArray, , Application.Transpose(Array(2)))

' MsgBox Array(1, 2)(0)

MsgBox varTemp(1)

' the first element actually using array command

' the above var temp starts with 1 and not with 0

'MsgBox UBound(varTemp) - LBound(varTemp) + 1

'MsgBox varArray(1, 1)

'MsgBox UBound(varTemp2) - LBound(varTemp2) + 1

'MsgBox varTemp2(10, 1)

' VBA Array starts at 1

End Sub

## Thursday, December 14, 2017

### Big Data Financial Engineering

Tools and plays

Kafka, Elastic Map Reduce, Avro, Parque, Storm, Hbase

NodejS or Java

- Either:

Kafka, Storm, Neo4j or Hbase

- Mongoose

- Solr/Lucene

Cassandra, Spark

Deep working experience applying machine learning and statistics to real world problems

Solid understanding of a wide range of data mining / machine learning software packages (e.g., Spark ML, scikit-learn, H2O, Weka, Keras)

Experience with version control systems (git) and comfortable using command-line tools

Preferred:

Knowledge of semantic web technology (e.g., RDF, OWL, SPARQL)

Knowledge of search technologies (e.g., Solr, ElasticSearch)

A link to a portfolio and/or code samples demonstrating your work experience (GitHub, Kaggle, KDD contributions earn major props)

Data Analyst – BI - Training:

Coding data extraction, transformation and loading (ETL) routines.

APIs and databases to pull data together

Hadoop, SQL and NoSQL technologies is required, as well as basic scripting experience in a dynamic language, such as Python or R.

Tools like Jethro, Kyvos, Dremio, AtScale etc.

BI tools like Tableau, Domo, Qlikview etc.

Sata visualization

Relational Databases (eg., Postgres, SQL Server, Oracle, MySQL)

Distributed Databases (eg., Hive, Redshift, Greenplum)

NoSQL Data Frameworks (eg., Spark, Mongo, Cassandra, HBase)

Data Analysis and Transformation (eg., R, Matlab, Python, etc.)

Big Data providers: Cloudera CDH, Hortonworks HDP and Amazon EC2/EMR for deploying and developing large scale solutions.

Hadoop/Spark Big Data Environment Clusters using Foreman, Puppet and Vagrant. Deploy Big Data Platforms (including Hadoop & Spark) to multiple clusters using Cloudera CDH, on both CDH4 and CDH5.

Hadoop MapReduce, YARN, HBase, Spark performance for large-scale data analysis.

Spark performance based on Cloudera and Hortornworks HDP cluster setup in Production Server.

Machine learning data models on Terabytes of data using Spark Ml and Mlib libraries.

ETL systems using Python, HIVE and Apache spark SQL framework. Storing all the result files in Apache parquet and mapping them to HIVE for Enterprise Datawarehousing.

Real-time data pipelines using Kafka and Python consumers to ingest data through Adobe Real-time Firehorse API into Elastic Search and built real-time dashboards using Kibana.

Aribnb Airflow tool, to run the machine learning scripts in a DAG manner.

Test cases using Python Nose framework.

Scikit learn python scripts to Ml\Mlib spark scripts, which resulted to scalable pipeline framework computing.

PySpark.

Data Pipelines using Spark and Scala on AWS EMR framework and S3.

Real-time Data pipelines using Spark Streaming and Apache Kafka in Python.

Real-time Data pipelines using Apache Storm Java API for processing live streams of data and ingesting to Hbase.

Data pipelines on Cloudera/Hortornworks Hadoop Platform using Apache PIG and automating workflow using Apache Oozie.

Technology: Hadoop Ecosystem /Spring Boot/Microservices/AWS /J2SE/J2EE/Oracle

DBMS/Databases: DB2, My SQL, SQL, PL/SQL

Big Data Ecosystem: HDFS, Map Reduce, Oozie, Hive/Impala, Pig, Sqoop, Zookeeper and Hbase,

Spark, Scala

NOSQL Databases: Mongo DB, Hbase

Version Control Tools: SVN, CVS, VSS, PVCS

Kafka, Elastic Map Reduce, Avro, Parque, Storm, Hbase

NodejS or Java

- Either:

Kafka, Storm, Neo4j or Hbase

- Mongoose

- Solr/Lucene

Cassandra, Spark

Deep working experience applying machine learning and statistics to real world problems

Solid understanding of a wide range of data mining / machine learning software packages (e.g., Spark ML, scikit-learn, H2O, Weka, Keras)

Experience with version control systems (git) and comfortable using command-line tools

Preferred:

Knowledge of semantic web technology (e.g., RDF, OWL, SPARQL)

Knowledge of search technologies (e.g., Solr, ElasticSearch)

A link to a portfolio and/or code samples demonstrating your work experience (GitHub, Kaggle, KDD contributions earn major props)

Data Analyst – BI - Training:

Coding data extraction, transformation and loading (ETL) routines.

APIs and databases to pull data together

Hadoop, SQL and NoSQL technologies is required, as well as basic scripting experience in a dynamic language, such as Python or R.

Tools like Jethro, Kyvos, Dremio, AtScale etc.

BI tools like Tableau, Domo, Qlikview etc.

Sata visualization

Relational Databases (eg., Postgres, SQL Server, Oracle, MySQL)

Distributed Databases (eg., Hive, Redshift, Greenplum)

NoSQL Data Frameworks (eg., Spark, Mongo, Cassandra, HBase)

Data Analysis and Transformation (eg., R, Matlab, Python, etc.)

Big Data providers: Cloudera CDH, Hortonworks HDP and Amazon EC2/EMR for deploying and developing large scale solutions.

Hadoop/Spark Big Data Environment Clusters using Foreman, Puppet and Vagrant. Deploy Big Data Platforms (including Hadoop & Spark) to multiple clusters using Cloudera CDH, on both CDH4 and CDH5.

Hadoop MapReduce, YARN, HBase, Spark performance for large-scale data analysis.

Spark performance based on Cloudera and Hortornworks HDP cluster setup in Production Server.

Machine learning data models on Terabytes of data using Spark Ml and Mlib libraries.

ETL systems using Python, HIVE and Apache spark SQL framework. Storing all the result files in Apache parquet and mapping them to HIVE for Enterprise Datawarehousing.

Real-time data pipelines using Kafka and Python consumers to ingest data through Adobe Real-time Firehorse API into Elastic Search and built real-time dashboards using Kibana.

Aribnb Airflow tool, to run the machine learning scripts in a DAG manner.

Test cases using Python Nose framework.

Scikit learn python scripts to Ml\Mlib spark scripts, which resulted to scalable pipeline framework computing.

PySpark.

Data Pipelines using Spark and Scala on AWS EMR framework and S3.

Real-time Data pipelines using Spark Streaming and Apache Kafka in Python.

Real-time Data pipelines using Apache Storm Java API for processing live streams of data and ingesting to Hbase.

Data pipelines on Cloudera/Hortornworks Hadoop Platform using Apache PIG and automating workflow using Apache Oozie.

Technology: Hadoop Ecosystem /Spring Boot/Microservices/AWS /J2SE/J2EE/Oracle

DBMS/Databases: DB2, My SQL, SQL, PL/SQL

Big Data Ecosystem: HDFS, Map Reduce, Oozie, Hive/Impala, Pig, Sqoop, Zookeeper and Hbase,

Spark, Scala

NOSQL Databases: Mongo DB, Hbase

Version Control Tools: SVN, CVS, VSS, PVCS

## Wednesday, September 6, 2017

### Advanced Data Science with Python: Machine Learning

Advanced Data Science with Python: Machine Learning

Prerequisites

Knowledge of Python programming and basic features of Python

Able to munge, analyze, and visualize data in Python with Pandas and charting

Syllabus

Unit 1: Introduction and Regression

How to dive into Machine Learning

Simple Linear Regression and Multiple Linear Regression

Forward and Backward Selection

Numpy/Scikit-Learn Lab

Class 2:

Part Classification I

Logistic Regression - Application in Default and other variables

Discriminant Analysis

Naive Bayes

Supervised Learning Lab

Resampling and Model Selection

Cross-Validation

Bootstrap - Breaking it down into simple

Feature Selection

Model Selection and Regularization lab

Class 3:

Classification II

Support Vector Machines SVM

Decision Trees - and Branch Analysis

Bagging and Random Forests

Decision Tree in Python and SVM Lab

Class 4:

Unsupervised Learning - Breaking it down

Principal Component Analysis

Kmeans and Hierarchical Clustering

PCA and Clustering Lab

Recommended Readings

An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Applied Predictive Modeling, by Max Kuhn and Kjell Johnson

Machine Learning for Hackers, by Drew Conway, John White

R Course Recommended Readings

An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Applied Predictive Modeling, by Max Kuhn and Kjell Johnson

Data Mining with R, by Luis Torgo

Machine Learning with R, by Brett Lantz

http://www.qcfinance.in/python-for-data-science-machine-learning/

Knowledge of Python programming and basic features of Python

Able to munge, analyze, and visualize data in Python with Pandas and charting

Syllabus

Unit 1: Introduction and Regression

How to dive into Machine Learning

Simple Linear Regression and Multiple Linear Regression

Forward and Backward Selection

Numpy/Scikit-Learn Lab

Class 2:

Part Classification I

Logistic Regression - Application in Default and other variables

Discriminant Analysis

Naive Bayes

Supervised Learning Lab

Resampling and Model Selection

Cross-Validation

Bootstrap - Breaking it down into simple

Feature Selection

Model Selection and Regularization lab

Class 3:

Classification II

Support Vector Machines SVM

Decision Trees - and Branch Analysis

Bagging and Random Forests

Decision Tree in Python and SVM Lab

Class 4:

Unsupervised Learning - Breaking it down

Principal Component Analysis

Kmeans and Hierarchical Clustering

PCA and Clustering Lab

Recommended Readings

An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Applied Predictive Modeling, by Max Kuhn and Kjell Johnson

Machine Learning for Hackers, by Drew Conway, John White

R Course Recommended Readings

An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Applied Predictive Modeling, by Max Kuhn and Kjell Johnson

Data Mining with R, by Luis Torgo

Machine Learning with R, by Brett Lantz

http://www.qcfinance.in/python-for-data-science-machine-learning/

## Saturday, September 2, 2017

### Django

Django

https://docs.djangoproject.com/en/1.11/intro/install/

https://stackoverflow.com/questions/25716185/page-not-found-404-on-django-site

https://docs.djangoproject.com/en/1.11/howto/windows/

Creating Backend:

https://docs.djangoproject.com/en/1.11/intro/tutorial02/

Need to install SQL

https://www.sqlite.org/download.html

https://docs.djangoproject.com/en/1.11/intro/install/

https://stackoverflow.com/questions/25716185/page-not-found-404-on-django-site

https://docs.djangoproject.com/en/1.11/howto/windows/

Creating Backend:

https://docs.djangoproject.com/en/1.11/intro/tutorial02/

Need to install SQL

https://www.sqlite.org/download.html

## Sunday, August 27, 2017

### R Code

#https://bookdown.org/rdpeng/rprogdatascience/managing-data-frames-with-the-dplyr-package.html

library(dplyr)

chicago <- readRDS("chicago.rds")

dim(chicago)

str(chicago)

select(chicago, -(city:dptp))

chicago <- arrange(chicago, date)

head(select(chicago, date, pm25tmean2), 3)

chicago <- rename(chicago, dewpoint = dptp, pm25 = pm25tmean2)

chicago <- mutate(chicago, pm25detrend = pm25 - mean(pm25, na.rm = TRUE))

chicago <- mutate(chicago, year = as.POSIXlt(date)$year + 1900)

years <- group_by(chicago, year)

#https://www.r-bloggers.com/introducing-dplyr/

library(Lahman)

library(dplyr)

players <- group_by(Batting, playerID)

games <- summarise(players, total = sum(G))

head(arrange(games, desc(total)), 5)

#https://www.rdocumentation.org/packages/dplyr/versions/0.7.2/topics/group_by

#for checking group by

#http://www.listendata.com/2016/08/dplyr-tutorial.html

mydata = read.csv("sampledata.csv")

sample_n(mydata,3)

mydata2 = select(mydata, Index, State:Y2008)

mydata7 = filter(mydata, Index == "A")

mydata6 = rename(mydata, Index1=Index)

mydata8 = filter(mydata6, Index1 %in% c("A", "C") & Y2002 >= 1300000 )

summarise(mydata, Y2015_mean = mean(Y2015), Y2015_med=median(Y2015))

dt = mydata %>% select(Index, State) %>% sample_n(10)

t = summarise_at(group_by(mydata, Index), vars(Y2011, Y2012), funs(n(), mean(., na.rm = TRUE)))

t = mydata %>% group_by(Index) %>%

summarise_at(vars(Y2011:Y2015), funs(n(), mean(., na.rm = TRUE)))

t = summarise_at(group_by(mydata, Index), vars(Y2011, Y2012), funs(n(), mean(., na.rm = TRUE)))

t = mydata %>% group_by(Index) %>%

summarise_at(vars(Y2011:Y2015), funs(n(), mean(., na.rm = TRUE)))

t = mydata %>% filter(Index %in% c("A", "C","I")) %>% group_by(Index) %>%

do(head( . , 2))

t = mydata %>% filter(Index %in% c("A", "C","I")) %>% group_by(Index) %>%

do(head( . , 2))

head(mydata, . , 2)

slice(mydata,3)

t = mydata %>% select(Index, Y2015) %>%

filter(Index %in% c("A", "C","I")) %>%

group_by(Index) %>%

do(arrange(.,desc(Y2015))) %>% slice(3)

t = mydata %>% select(Index, Y2015) %>%

filter(Index %in% c("A", "C","I")) %>%

group_by(Index) %>%

do(arrange(.,desc(Y2015))) %>% slice(3)

t = mydata %>% select(Index, Y2015) %>%

filter(Index %in% c("A", "C","I")) %>%

group_by(Index) %>%

filter(min_rank(desc(Y2015)) == 3)

t = mydata %>%

group_by(Index)%>%

summarise(Mean_2014 = mean(Y2014, na.rm=TRUE),

Mean_2015 = mean(Y2015, na.rm=TRUE)) %>%

arrange(desc(Mean_2015))

#https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html

library(nycflights13)

library(tidyverse)

flights_sml <- select(flights,

year:day,

ends_with("delay"),

distanceis.na(x)is.na(x),

air_time )

mutate(flights_sml,

gain = arr_delay - dep_delay,

speed = distance / air_time * 60)

arrange(flights, desc(arr_delay))

View(flights)

arrange(flights,dep_delay)

df <- tibble(x = c(5, sin(1), NA))

arrange(df, desc(is.na(x)))

x <- flights %>% mutate(travel_time = ifelse((arr_time - dep_time < 0),

2400+(arr_time - dep_time),

arr_time - dep_time)) %>%

arrange(travel_time) %>% select(arr_time, dep_time, travel_time)

select(flights,1:5)

arrange(flights, desc(distance)) %>% select(1:5, distance)

flights %>% select(matches("^dep|^arr_time$|delay$"))

x=c(1:4)

y=(1:4)

x==y

x(1,23,4)

x=c('d','d')

x

select(flights,

air_time,

gain = arr_delay - dep_delay ,

hours = air_time / 60,

gain_per_hour = gain / hours

)

library(dplyr)

chicago <- readRDS("chicago.rds")

dim(chicago)

str(chicago)

select(chicago, -(city:dptp))

chicago <- arrange(chicago, date)

head(select(chicago, date, pm25tmean2), 3)

chicago <- rename(chicago, dewpoint = dptp, pm25 = pm25tmean2)

chicago <- mutate(chicago, pm25detrend = pm25 - mean(pm25, na.rm = TRUE))

chicago <- mutate(chicago, year = as.POSIXlt(date)$year + 1900)

years <- group_by(chicago, year)

#https://www.r-bloggers.com/introducing-dplyr/

library(Lahman)

library(dplyr)

players <- group_by(Batting, playerID)

games <- summarise(players, total = sum(G))

head(arrange(games, desc(total)), 5)

#https://www.rdocumentation.org/packages/dplyr/versions/0.7.2/topics/group_by

#for checking group by

#http://www.listendata.com/2016/08/dplyr-tutorial.html

mydata = read.csv("sampledata.csv")

sample_n(mydata,3)

mydata2 = select(mydata, Index, State:Y2008)

mydata7 = filter(mydata, Index == "A")

mydata6 = rename(mydata, Index1=Index)

mydata8 = filter(mydata6, Index1 %in% c("A", "C") & Y2002 >= 1300000 )

summarise(mydata, Y2015_mean = mean(Y2015), Y2015_med=median(Y2015))

dt = mydata %>% select(Index, State) %>% sample_n(10)

t = summarise_at(group_by(mydata, Index), vars(Y2011, Y2012), funs(n(), mean(., na.rm = TRUE)))

t = mydata %>% group_by(Index) %>%

summarise_at(vars(Y2011:Y2015), funs(n(), mean(., na.rm = TRUE)))

t = summarise_at(group_by(mydata, Index), vars(Y2011, Y2012), funs(n(), mean(., na.rm = TRUE)))

t = mydata %>% group_by(Index) %>%

summarise_at(vars(Y2011:Y2015), funs(n(), mean(., na.rm = TRUE)))

t = mydata %>% filter(Index %in% c("A", "C","I")) %>% group_by(Index) %>%

do(head( . , 2))

t = mydata %>% filter(Index %in% c("A", "C","I")) %>% group_by(Index) %>%

do(head( . , 2))

head(mydata, . , 2)

slice(mydata,3)

t = mydata %>% select(Index, Y2015) %>%

filter(Index %in% c("A", "C","I")) %>%

group_by(Index) %>%

do(arrange(.,desc(Y2015))) %>% slice(3)

t = mydata %>% select(Index, Y2015) %>%

filter(Index %in% c("A", "C","I")) %>%

group_by(Index) %>%

do(arrange(.,desc(Y2015))) %>% slice(3)

t = mydata %>% select(Index, Y2015) %>%

filter(Index %in% c("A", "C","I")) %>%

group_by(Index) %>%

filter(min_rank(desc(Y2015)) == 3)

t = mydata %>%

group_by(Index)%>%

summarise(Mean_2014 = mean(Y2014, na.rm=TRUE),

Mean_2015 = mean(Y2015, na.rm=TRUE)) %>%

arrange(desc(Mean_2015))

#https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html

library(nycflights13)

library(tidyverse)

flights_sml <- select(flights,

year:day,

ends_with("delay"),

distanceis.na(x)is.na(x),

air_time )

mutate(flights_sml,

gain = arr_delay - dep_delay,

speed = distance / air_time * 60)

arrange(flights, desc(arr_delay))

View(flights)

arrange(flights,dep_delay)

df <- tibble(x = c(5, sin(1), NA))

arrange(df, desc(is.na(x)))

x <- flights %>% mutate(travel_time = ifelse((arr_time - dep_time < 0),

2400+(arr_time - dep_time),

arr_time - dep_time)) %>%

arrange(travel_time) %>% select(arr_time, dep_time, travel_time)

select(flights,1:5)

arrange(flights, desc(distance)) %>% select(1:5, distance)

flights %>% select(matches("^dep|^arr_time$|delay$"))

x=c(1:4)

y=(1:4)

x==y

x(1,23,4)

x=c('d','d')

x

select(flights,

air_time,

gain = arr_delay - dep_delay ,

hours = air_time / 60,

gain_per_hour = gain / hours

)

## Friday, July 21, 2017

### Web Scraping and Content Mining

DESCRIPTION

Web Scraping and Content Mining

Most interesting course in NYC.

2 sessions workshop

Web Scraping is a method for extracting textual characters from websites so that they could be analyzed. Web scraping is sort of content mining, which means that you collect useful information from websites, including quotes, prices, news company info, etc.This method for gathering data is direct, either through looking at websites' html code or visual abstraction techniques using Python programming language.

We start workshop by exploring different methods to gather data from Web. We go through the whole process of gathering, storing and analyzing data. For our examples we use real-life financial quotes and Annual reports 10-K. During the course we learn how to use numerous Python libraries - Urllib, Requests, Wget, BeautifulSoup 4.0, SSL, PDFminer3k, Twitter and others.

Also, we learn to constract Regular expressions patterns to find targeted information on Web pages. As a part of content mining, we build Twitter application to search and analyze the trends.

The price is for two classes:

You will Learn:

BeautifulSoup Python Library

How to use Urllib and Requests

Regular Expressions patterns

Read and analyze PDF files

Store Data with CSV files and SQL Database

Create Twitter app

Build Custom Google Search Engine

Web Scraping and Content Mining

Most interesting course in NYC.

2 sessions workshop

Web Scraping is a method for extracting textual characters from websites so that they could be analyzed. Web scraping is sort of content mining, which means that you collect useful information from websites, including quotes, prices, news company info, etc.This method for gathering data is direct, either through looking at websites' html code or visual abstraction techniques using Python programming language.

We start workshop by exploring different methods to gather data from Web. We go through the whole process of gathering, storing and analyzing data. For our examples we use real-life financial quotes and Annual reports 10-K. During the course we learn how to use numerous Python libraries - Urllib, Requests, Wget, BeautifulSoup 4.0, SSL, PDFminer3k, Twitter and others.

Also, we learn to constract Regular expressions patterns to find targeted information on Web pages. As a part of content mining, we build Twitter application to search and analyze the trends.

The price is for two classes:

You will Learn:

BeautifulSoup Python Library

How to use Urllib and Requests

Regular Expressions patterns

Read and analyze PDF files

Store Data with CSV files and SQL Database

Create Twitter app

Build Custom Google Search Engine

## Sunday, July 16, 2017

### Craiglist Adds

## Financial Modeling Tutor $25/hr (Midtown)

Offering lessongs on excel, especially build models for companies.

Very affordable rate of $25, and you will be given all the skills needed to land a job at a hedge fund, investment bank, or private equity firm.

Valuation Methods for Companies, putting together models, write-ups, and presentations.

This is a very limited service and is temporarily offered for this month while I am vacation and interested to share what I learned.

Take advantage while you can, hours are limited, availability also at 6 pm - just after your office.

Feel free to contact me if you are interested in learning how to be an finance/excel expert! Thank you!

## Google Sheets and App Script (JavaScript) tutor $25/hr (Manhattan)

Charge $25/hour.

Location: All in NYC area.

Please get in touch

## GRE / GMAT Quant / Quantitative/ Math Tutor $25/hr

Price $25 per hour

5 years of exp in quantitative GRE GMAT tutoring

From the author of FreeGREGMATClass dot com

Check out the youtube videos contributed by students and tutors for FGGC

First session of 30 minutes is free.

## Quantitative Math Excel VBA $25/hr (New York)

I have extensive teaching experience with students of various profiles and backgrounds where I have learned and enhanced my skills and also helped learners. I am also an extremely friendly person. I have taken many online classes as tutor on qcfinance.in

QcFinance.in has tutors available for meetups (mix of onsite at home & online through Wiziq/Skype/Adobe-connect & custom videos support emailed to you for your doubts on holidays)

Subject areas teach includes: Quantitative Methods for GRE / SAT / GMAT, CFA L1, FRM L1, MATLAB/R/SPSS for Quant, Excel-VBA Programming. Quantitiative and Analytics Excel programming & VBA programming.

Sub topics:

Basics of Excel: Vlookups, Hlookups, Index Match, Dependents, Data tables (1 way and 2 ways), Pivots, Charting, Filters, SQL integration, VBA coding, Address and indirect, Offset, Array functions, etc.

Applications: Regression, Histograms, Monte carlo simulation, rank correlation, dashboards, more.

Automation using VBA: Loops (for, do while, case), Recorder, Arrays and Matrices, if else, indexing, etc.

Website: www.qcfinance.in

Playlist of sample quant videos: https://www.youtube.com/playlist?list=PL_-KSXJS5pxOiLjAoe5uAHPAsv-UhIM7i

Keywords: Quant Trainer, Tutor, Trainer, Teacher, Home tuition, GRE, GMAT, Quant, Programming, CFA Level 1, FRM Part 1, Mathematics Tutor, Maths, Onsite.

## Office Automation on Excel VBA Python SQL R (new york)

Automation is the next biggest revolution, legacy methods if not replaced by automation will reduce the productivity of the firm which might even lead to extinction.

Our can reduce a lot of manual work and use lot of Excel Analytics features, our clients have reduced work by upto 50-70% which helped me focus on their product and other value addition to their core business.

Get more hours from your employees and more robust analytical framework!

Please contact me for more details about various processes that we can automate.

**Statistics, Data Science, Machine Learning, Statistical Computing, R**

## Monday, July 3, 2017

### Analytics course New York

PROJECTS

FINAL PROJECT For the Analytics final project, you will collect, clean and

analyze a data set to solve a real world problem. From this

data, you will segment the data set and perform analysis.

Following your analysis, you will create both a dashboard and

presentation.

In order for your project to be considered a success, you will

complete the following steps

‣ Identify a problem

‣ Obtain the data

‣ Understand the data

‣ Prepare, clean and format the data

‣ Analyze the data

‣ Create a dashboard to display insights both numerically and

graphically.

‣ Present high level insights and the resulting actions to key

stakeholders.

As you complete elements of your final project, you will be

required to present materials and receive feedback from your

instructional team and classmates as well industry experts.

Our instructors are on hand to validate the feasibility and

manage the scope of your project.

6

Data Analytics

Units

UNITS

UNIT 1: DATA IN EXCEL ‣ The Value of Data Lesson 1

‣ Prepare Data in Excel Lesson 2

‣ Clean Data in Excel Lesson 3

‣ Dynamic Data Referencing Lesson 4

‣ Dynamic Data Aggregation Lesson 5

‣ Conditional Formatting and Aggregation Lesson 6

‣ The Value of Databases Lesson 7

‣ Query Large Databases Lesson 8

‣ Data Aggregation in SQL Lesson 9

‣ More Data Aggregation in SQL Lesson 10

‣ Efficient and Dynamic Queries Lesson 11

‣ Present Analysis Results Lesson 12

‣ Statistics to Validate Analysis Lesson 13

‣ Predictive Analysis Lesson 14

‣ Dashboard Design Lesson 15

‣ Track Metrics with Dashboards Lesson 16

‣ Effective Presentations with Data Lesson 17

‣ Flexible Session Lesson 18

‣ Flexible Session Lesson 19

‣ Final Project Presentation Lesson 20

UNIT 2: DATA IN SQL

UNIT 2: COMMUNICATION AND

DASHBOARD DESIGN

1 THE VALUE OF DATA

‣ Explain the value of data.

‣ Describe the analytics workflow

‣ Use mean, median, mode to describe data and find outliers

2 PREPARE DATA IN EXCEL

‣ Describe best practices in data cleaning and collection to

ensure the best results from data analysis

‣ Use complex nested logical functions [IF, OR, and AND] to

further manipulate data sets

‣ Manipulate data formats to gain insights on how to analyze

data

3 CLEAN DATA IN EXCEL

‣ Clean a large messy datasets by removing duplicate rows

and performing text manipulations

‣ Transform and rearrange columns and rows to structure

data for analysis

‣ Manipulate data formats to gain insights on how to analyze

data

4 DYNAMIC DATA REFERENCING

‣ Use data functions [VLOOKUP and HLOOKUP] to

manipulate data sets

‣ Use data functions [INDEX and MATCH] to look up values

in other tables

‣ Reconcile data values by joining and matching

5 DYNAMIC DATA AGGREGATION

‣ Summarize data using the pivot tables

‣ Use excel aggregation commands [‘Min’, ‘Max’, ‘Sum’,

‘Average’, ‘Count’, ‘Frequency’ to accomplish “count

distinct” ] and their conditional variants [‘COUNTIF’,

‘COUNTUNIQUE’, ‘COUNTA’, ‘COUNTIFS’,

‘COUNTBLANKS’] to summarize data sets

6 CONDITIONAL FORMATTING AND AGGREGATION

‣ Derive insights from data by highlight cells based on

conditionals

‣ Describe color theory and how it applies to data visualization

7

Data Analytics

Units Continued

DATA IN EXCEL 1

8

Data Analytics

Units Continued

GA.CO/AN

7 THE VALUE OF DATABASES AND SQL

‣ Use database schema to design appropriate queries

‣ Explain differences between relational databases (tabular

data storage) and document-based databases(key-value

pairs)

‣ Collect data using standard sql commands [Select, From,

Create, Update, Delete, Truncate, Drop]

8 QUERY LARGE DATABASES

‣ - Use advanced SQL commands [where, groupby, having,

orderby, limit] to filter data

‣ - Use joins to create relationships between tables to obtain

data

‣ - Use SQL boolean operators [AND and OR] and SQL

conditional operators [=,!=,>,<,IN and BETWEEN] to

obtain filtered data

9 DATA AGGREGATION IN SQL

‣ U- Create relationships between tables and data points

including has_many and many_to_many with join tables

using Joins [‘full’, and ‘union’]

‣ - Use sql conditional operators [=,!=,>,<,IN and

BETWEEN] and Null functions[‘is Null’, ‘ is not Null’ and

‘NVL’ ] to create boolean statements

‣ - Use sql mathematical functions [ABS, SIGN, MOD,

FLOOR, CEILING, ROUND, SQRT] to clean data

10 MORE DATA AGGREGATION IN SQL

‣ - Use aggregation commands [‘Min’, ‘Max’, ‘Sum’, ‘Average’,

‘Count’, ‘Count Distinct’] to summarize data sets

‣ - Use aggregation methods to determine trends from data

11 EFFICIENT AND DYNAMIC QUERIES

‣ Use CASE statements to structure data and create new

attributes

‣ - Use "WITH AS (" to combine subqueries into one query

‣ - Present analysis results and describe stakeholder

implications and insights

DATA IN SQL 2

9

Data Analytics

Units Continued

12 PRESENT ANALYSIS RESULTS

‣ Provide appropriate context of dataset

‣ Appropriately describe analysis techniques

‣ Present and describe stakeholder implications and insights

13 STATISTICS TO VALIDATE ANALYSIS

‣ Describe the value of descriptive and summary statistics in

understanding a dataset

‣ Create basic statistical measures to better understand the

range, average, and variance within a dataset

‣ Present the most salient statistics in order to provide

context to your audience

‣ Explain the importance of segmentation

14 PREDICTIVE ANALYSIS

‣ Describe the value of inferential statistics and predictive

analysis

‣ Review linear regression and Ordinary Least Squares (OLS)

‣ Use sample data to make predictions about a larger

population

15 DASHBOARD DESIGN

‣ Use scatter plots and bar graphs to visualize data

‣ Apply the best practices to build a dashboard

‣ Demonstrate good visual design without overloading their

dashboard with complexity

16 TRACK METRICS WITH DASHBOARDS

‣ Use bubble graphs to visualize data

‣ Apply the best practices to build a dashboard

‣ Contextualize data analysis by creating Tableau dashboards

[includes charts + conditional formatting] with supporting

information specific to the dataset

17 EFFECTIVE PRESENTATIONS WITH DATA

‣ Display geocoded information in Tableau

‣ Provide real-world context for basis of analysis

‣ Provide localized context for implications of findings

‣ Deliver short, effective presentations

DATA ANALYSIS 2 (CONTINUED)

DATA COMMUNICATION 3

10

Data Analytics

Units Continued

18 FLEXIBLE SESSION

‣ Focus on a topic selected by the instructor/class in order to

provide deeper insight into a specific area of data analysis

19 FLEXIBLE SESSION

‣ Focus on a topic selected by the instructor/class in order to

provide deeper insight into a specific area of data analysis

20 FINAL PROJECT PRESENTATION

‣ Present final project presentation to class

FINAL PROJECT For the Analytics final project, you will collect, clean and

analyze a data set to solve a real world problem. From this

data, you will segment the data set and perform analysis.

Following your analysis, you will create both a dashboard and

presentation.

In order for your project to be considered a success, you will

complete the following steps

‣ Identify a problem

‣ Obtain the data

‣ Understand the data

‣ Prepare, clean and format the data

‣ Analyze the data

‣ Create a dashboard to display insights both numerically and

graphically.

‣ Present high level insights and the resulting actions to key

stakeholders.

As you complete elements of your final project, you will be

required to present materials and receive feedback from your

instructional team and classmates as well industry experts.

Our instructors are on hand to validate the feasibility and

manage the scope of your project.

6

Data Analytics

Units

UNITS

UNIT 1: DATA IN EXCEL ‣ The Value of Data Lesson 1

‣ Prepare Data in Excel Lesson 2

‣ Clean Data in Excel Lesson 3

‣ Dynamic Data Referencing Lesson 4

‣ Dynamic Data Aggregation Lesson 5

‣ Conditional Formatting and Aggregation Lesson 6

‣ The Value of Databases Lesson 7

‣ Query Large Databases Lesson 8

‣ Data Aggregation in SQL Lesson 9

‣ More Data Aggregation in SQL Lesson 10

‣ Efficient and Dynamic Queries Lesson 11

‣ Present Analysis Results Lesson 12

‣ Statistics to Validate Analysis Lesson 13

‣ Predictive Analysis Lesson 14

‣ Dashboard Design Lesson 15

‣ Track Metrics with Dashboards Lesson 16

‣ Effective Presentations with Data Lesson 17

‣ Flexible Session Lesson 18

‣ Flexible Session Lesson 19

‣ Final Project Presentation Lesson 20

UNIT 2: DATA IN SQL

UNIT 2: COMMUNICATION AND

DASHBOARD DESIGN

1 THE VALUE OF DATA

‣ Explain the value of data.

‣ Describe the analytics workflow

‣ Use mean, median, mode to describe data and find outliers

2 PREPARE DATA IN EXCEL

‣ Describe best practices in data cleaning and collection to

ensure the best results from data analysis

‣ Use complex nested logical functions [IF, OR, and AND] to

further manipulate data sets

‣ Manipulate data formats to gain insights on how to analyze

data

3 CLEAN DATA IN EXCEL

‣ Clean a large messy datasets by removing duplicate rows

and performing text manipulations

‣ Transform and rearrange columns and rows to structure

data for analysis

‣ Manipulate data formats to gain insights on how to analyze

data

4 DYNAMIC DATA REFERENCING

‣ Use data functions [VLOOKUP and HLOOKUP] to

manipulate data sets

‣ Use data functions [INDEX and MATCH] to look up values

in other tables

‣ Reconcile data values by joining and matching

5 DYNAMIC DATA AGGREGATION

‣ Summarize data using the pivot tables

‣ Use excel aggregation commands [‘Min’, ‘Max’, ‘Sum’,

‘Average’, ‘Count’, ‘Frequency’ to accomplish “count

distinct” ] and their conditional variants [‘COUNTIF’,

‘COUNTUNIQUE’, ‘COUNTA’, ‘COUNTIFS’,

‘COUNTBLANKS’] to summarize data sets

6 CONDITIONAL FORMATTING AND AGGREGATION

‣ Derive insights from data by highlight cells based on

conditionals

‣ Describe color theory and how it applies to data visualization

7

Data Analytics

Units Continued

DATA IN EXCEL 1

8

Data Analytics

Units Continued

GA.CO/AN

7 THE VALUE OF DATABASES AND SQL

‣ Use database schema to design appropriate queries

‣ Explain differences between relational databases (tabular

data storage) and document-based databases(key-value

pairs)

‣ Collect data using standard sql commands [Select, From,

Create, Update, Delete, Truncate, Drop]

8 QUERY LARGE DATABASES

‣ - Use advanced SQL commands [where, groupby, having,

orderby, limit] to filter data

‣ - Use joins to create relationships between tables to obtain

data

‣ - Use SQL boolean operators [AND and OR] and SQL

conditional operators [=,!=,>,<,IN and BETWEEN] to

obtain filtered data

9 DATA AGGREGATION IN SQL

‣ U- Create relationships between tables and data points

including has_many and many_to_many with join tables

using Joins [‘full’, and ‘union’]

‣ - Use sql conditional operators [=,!=,>,<,IN and

BETWEEN] and Null functions[‘is Null’, ‘ is not Null’ and

‘NVL’ ] to create boolean statements

‣ - Use sql mathematical functions [ABS, SIGN, MOD,

FLOOR, CEILING, ROUND, SQRT] to clean data

10 MORE DATA AGGREGATION IN SQL

‣ - Use aggregation commands [‘Min’, ‘Max’, ‘Sum’, ‘Average’,

‘Count’, ‘Count Distinct’] to summarize data sets

‣ - Use aggregation methods to determine trends from data

11 EFFICIENT AND DYNAMIC QUERIES

‣ Use CASE statements to structure data and create new

attributes

‣ - Use "WITH AS (" to combine subqueries into one query

‣ - Present analysis results and describe stakeholder

implications and insights

DATA IN SQL 2

9

Data Analytics

Units Continued

12 PRESENT ANALYSIS RESULTS

‣ Provide appropriate context of dataset

‣ Appropriately describe analysis techniques

‣ Present and describe stakeholder implications and insights

13 STATISTICS TO VALIDATE ANALYSIS

‣ Describe the value of descriptive and summary statistics in

understanding a dataset

‣ Create basic statistical measures to better understand the

range, average, and variance within a dataset

‣ Present the most salient statistics in order to provide

context to your audience

‣ Explain the importance of segmentation

14 PREDICTIVE ANALYSIS

‣ Describe the value of inferential statistics and predictive

analysis

‣ Review linear regression and Ordinary Least Squares (OLS)

‣ Use sample data to make predictions about a larger

population

15 DASHBOARD DESIGN

‣ Use scatter plots and bar graphs to visualize data

‣ Apply the best practices to build a dashboard

‣ Demonstrate good visual design without overloading their

dashboard with complexity

16 TRACK METRICS WITH DASHBOARDS

‣ Use bubble graphs to visualize data

‣ Apply the best practices to build a dashboard

‣ Contextualize data analysis by creating Tableau dashboards

[includes charts + conditional formatting] with supporting

information specific to the dataset

17 EFFECTIVE PRESENTATIONS WITH DATA

‣ Display geocoded information in Tableau

‣ Provide real-world context for basis of analysis

‣ Provide localized context for implications of findings

‣ Deliver short, effective presentations

DATA ANALYSIS 2 (CONTINUED)

DATA COMMUNICATION 3

10

Data Analytics

Units Continued

18 FLEXIBLE SESSION

‣ Focus on a topic selected by the instructor/class in order to

provide deeper insight into a specific area of data analysis

19 FLEXIBLE SESSION

‣ Focus on a topic selected by the instructor/class in order to

provide deeper insight into a specific area of data analysis

20 FINAL PROJECT PRESENTATION

‣ Present final project presentation to class

## Tuesday, June 27, 2017

### SQL:Fundamentals of Querying Course New York Manhattan

SQL:Fundamentals of Querying Course New York Manhattan nyc

Duration: 1 day(s)

Outline

Executing a Simple Query

Connect to the SQL Database

Query a Database

Save a Query

Modify a Query

Execute a Saved Query

Performing a Conditional Search

Search Using a Simple Condition

Compare Column Values

Search Using Multiple Conditions

Search for a Range of Values and Null Values

Retrieve Data Based on Patterns

Working with Functions

Perform Date Calculations

Calculate Data Using Aggregate Functions

Manipulate String Values Organizing Data

Sort Data

Rank Data

Group Data

Filter Grouped Data

Summarize Grouped Data

Use PIVOT and UNPIVOT Operators

Retrieving Data from Tables

Combine Results of Two Queries

Compare the Results of Two Queries

Retrieve Data by Joining Tables

Check for Unmatched Records

Retrieve Information from a Single Table Using Joins

Presenting Query Results

Save the Query Result

Generate an XML Report

Appendix A: The OGCBooks Database

After completing this course, students will know how to:

Connect to the SQL Server database and execute a simple query.

Include a search condition in a simple query.

Use various functions to perform calculations on data.

Organize data obtained from a query before it is displayed on-screen.

Retrieve data from tables.

Format an output, save a result, and generate a report.

Duration: 1 day(s)

Outline

Executing a Simple Query

Connect to the SQL Database

Query a Database

Save a Query

Modify a Query

Execute a Saved Query

Performing a Conditional Search

Search Using a Simple Condition

Compare Column Values

Search Using Multiple Conditions

Search for a Range of Values and Null Values

Retrieve Data Based on Patterns

Working with Functions

Perform Date Calculations

Calculate Data Using Aggregate Functions

Manipulate String Values Organizing Data

Sort Data

Rank Data

Group Data

Filter Grouped Data

Summarize Grouped Data

Use PIVOT and UNPIVOT Operators

Retrieving Data from Tables

Combine Results of Two Queries

Compare the Results of Two Queries

Retrieve Data by Joining Tables

Check for Unmatched Records

Retrieve Information from a Single Table Using Joins

Presenting Query Results

Save the Query Result

Generate an XML Report

Appendix A: The OGCBooks Database

After completing this course, students will know how to:

Connect to the SQL Server database and execute a simple query.

Include a search condition in a simple query.

Use various functions to perform calculations on data.

Organize data obtained from a query before it is displayed on-screen.

Retrieve data from tables.

Format an output, save a result, and generate a report.

## Sunday, June 25, 2017

### R Analytics data science

http://nycdatascience.com/data-science-bootcamp/

## Thursday, June 22, 2017

### Excel VBA Class in Manahttan

Below are the list of classes I found in Manhattan.

Active - First

http://vbaclass.com/excel-classes/vba-classes/

421 7th Ave, New York, NY 10001

excel-training-nyc.com

Phone: 212-537-6125

Address: 421 7th Avenue, 4th Floor

Last

Sam - not small but not that big also

1723 E 12th St, Brooklyn, NY 11229

samconsulting.com

Moribound 2nd:

http://www.exceltrainingnyc.com/index.html

545 8th Ave

Suite 1530

New York, NY 10018

Phone: 212-564-2351

Active - First

http://vbaclass.com/excel-classes/vba-classes/

421 7th Ave, New York, NY 10001

excel-training-nyc.com

Phone: 212-537-6125

Address: 421 7th Avenue, 4th Floor

Last

Sam - not small but not that big also

1723 E 12th St, Brooklyn, NY 11229

samconsulting.com

Moribound 2nd:

http://www.exceltrainingnyc.com/index.html

545 8th Ave

Suite 1530

New York, NY 10018

Phone: 212-564-2351

## Wednesday, June 21, 2017

### Quant /Math Tutoring in NYC areas close to Ozone Queens

To be checked:

http://www.flexprep.org/ - Closest

http://www.parkslopetutoring.org/ - down in Brooklyn

http://www.ridgewoodtutors.com/contact/ - Closest

http://pinnacleprep.com/ - Close to Iskon

https://www.ontutoring.com/ - Close

https://www.kumon.com/KEW-GARDENS

http://www.flexprep.org/ - Closest

http://www.ridgewoodtutors.com/contact/ - Closest

http://pinnacleprep.com/ - Close to Iskon

https://www.ontutoring.com/ - Close

https://www.kumon.com/KEW-GARDENS

http://alohamindmath.com/

## Tuesday, June 20, 2017

### Data Science Programs in New York

Python for Data Science & Machine learning

Course by QcFinance.in

Skills that you will GAIN

- Python Programming Language
- Statistical Hypothesis Testing
- IPython
- Hypothesis-testing
- NetworkX
- Matplotlib
- Numpy
- Pandas
- Scipy
- Python Lambdas
- Python Regular Expressions

Python Basics

An introduction to the basic concepts of Python. Learn how to use Python both interactively and through a script. Create your first variables and acquaint yourself with Python's basic data types.

Learn to store, access and manipulate data in lists: the first step towards efficiently working with huge amounts of data.

Functions and Packages

To leverage the code that brilliant Python developers have written, you'll learn about using functions, methods and packages. This will help you to reduce the amount of code you need to solve challenging problems!

NumPy

NumPy is a Python package to efficiently do data science. Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.

Course Syllabus

Section 1: Python Basics

Take your first steps in the world of Python. Discover the different data types and create your first variable.

Section 2: Python Lists

Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.

Section 3: Functions and Packages & Control flow and Pandas

Learn how to get the most out of other people's efforts by importing Python packages and calling functions.

Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.

Section 4: Numpy and Matplotlib

Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.

Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data.

Collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:

- python
- jupyter notebooks
- pandas
- numpy
- matplotlib
- git
- and many other tools.

We'll cover the machine learning and data mining techniques real employers are looking for, including:

- Regression analysis
- K-Means Clustering
- Principal Component Analysis
- Train/Test and cross validation
- Bayesian Methods
- Decision Trees and Random Forests
- Multivariate Regression
- Multi-Level Models
- Support Vector Machines
- Reinforcement Learning
- Collaborative Filtering
- K-Nearest Neighbor
- Bias/Variance Tradeoff
- Ensemble Learning
- Term Frequency / Inverse Document Frequency
- Experimental Design and A/B Tests

Statistics and Probability Refresher, and Python

- Bayes' Theorem
- Predictive Models
- Linear Regression
- Polynomial Regression
- Multivariate Regression, and Predicting Car Prices
- Multi-Level Models
- Machine Learning with Python
- Supervised vs. Unsupervised Learning, and Train/Test
- Using Train/Test to Prevent Overfitting a Polynomial Regression
- Bayesian Methods: Concepts
- Implementing a Spam Classifier with Naive Bayes
- K-Means Clustering
- Clustering people based on income and age
- Measuring Entropy
- Install GraphViz
- Decision Trees: Concepts
- Decision Trees: Predicting Hiring Decisions
- Ensemble Learning
- Support Vector Machines (SVM) Overview
- Using SVM to cluster people using scikit-learn
- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
- Finding Movie Similarities
- Improving the Results of Movie Similarities
- Making Movie Recommendations to People
- Improve the recommender's results
- More Data Mining and Machine Learning Techniques
- K-Nearest-Neighbors: Concepts
- Using KNN to predict a rating for a movie
- Dimensionality Reduction; Principal Component Analysis
- PCA Example with the Iris data set
- Data Warehousing Overview: ETL and ELT
- Reinforcement Learning
- Dealing with Real-World Data
- Bias/Variance Tradeoff
- K-Fold Cross-Validation to avoid overfitting
- Data Cleaning and Normalization
- Cleaning web log data
- Normalizing numerical data
- Detecting outliers
- –Apache Spark: Machine Learning on Big Data
- Installing Spark - Part
- Spark Introduction
- Spark and the Resilient Distributed Dataset (RDD)
- Introducing MLLib
- Decision Trees in Spark
- K-Means Clustering in Spark
- TF / IDF
- Searching Wikipedia with Spark
- Using the Spark 2.0 DataFrame API for MLLib
- Experimental Design
- A/B Testing Concepts
- T-Tests and P-Values
- Hands-on With T-Tests
- Determining How Long to Run an Experiment
- A/B Test Gotchas

Please email info@qcfinance.in to know more information.

Some links from online search:

www.skilledup [dot] com/articles/list-data-science-bootcamps

Generalassemb[dot]ly/education/data-science

Some general Videos That are suggested:

https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y

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