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

Sunday, July 16, 2017

Craiglist Adds

Financial Modeling Tutor $25/hr (Midtown)



I am a former investment fund analyst and experienced in investment banking. 


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)

Hi,
I am a tutor for Google App script (Java script) used for automation in Google sheets. If your company is using google sheet learning automation will help you to progress. Also, important for business students.
Charge $25/hour.
Location: All in NYC area.
Please get in touch

.    

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

GRE / GMAT Quant Tutor
Experience in tutoring, videos online and good references.
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)

Tutoring Data Science has been my hobby and recreational activity. Many tutoring projects are volunteering and networking oriented (getting new insight). I do it so that I can revise what I do at school and at work. I am an Electrical Engineering graduate, GAARP-FRM certified, PG Dip in Fin analysis and Risk Management, cleared CFA L1, International - MBA (15-16).

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.
Keywords: Trainer, Tutor, Home tuition, Excel, VBA, Onsite, trainer, help assignment, video solutions, In Person, 1 on 1, Home Tutor. 

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

We provide official automation services on Excel VBA Python SQL R. 

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   


Tutoring for statistics, machine learning, and data science. The focus includes statistical theory as well as its application, building models. That includes the following: 
•Theory Courses: Probability, Statistical Inference, Bayesian Statistics, Decision Theory, Point Estimation, High-Dimensional Inference, Time Series, and other MS/Ph.D. courses

•Machine Learning: Ridge Regression, LASSO, Basis Pursuit, Supervised/Unsupervised Learning, Neural Network, Statistical Learning Theory 

•Social Science: Causal Inference, Hierarchical Model, Multiple Imputation, Matching

•Statistical computing: R programming, Matlab, STATA, Python, Java, C

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

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.

Sunday, June 25, 2017

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

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://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






Sunday, June 18, 2017

Data Analytics courses in NY

COURSE DESCRIPTION:

This unique comprehensive course provides solid knowledge in Data Analysis and Statistical Software. Through a combination of lectures and exercises, students will learn the key techniques and approaches necessary to be an effective Data Analyst.
Students will learn fundamentals of Data Analysis with Python programming, advanced Excel techniques and VBA macros, basics of SQL Server Databases In the end of the course students will polish their knowledge practicing Data Analysis projects.

COURSE CONTENT:

1. Statistics for Business. Overview – students will review basic concepts and terminology in Statistics, including: Mean, Mode, Range, Standard Deviation, Normal Distribution, T-Distribution, Hypothesis Testing, Chi Square Test, A-B Testing etc.
 
2. Python Programming Fundamentals – the module includes following topics: Basic Data Types (Basic Data Types, Variables, Operators, Functions and Modules), Compound Data Types (Lists, Strings,  Sets, Dictionaries), Flow control (Conditional expressions, Loops, Iterators), Working with files, Working with functions, OOP Concepts, Benefits of Standard Library.

3. Data Analysis and Visualization with Python – module provides solid fundamentals of Data Analysis and Data Visualization using Python Libraries: Numpy, Pandas and Seaborn. Students will learn Machine Learning (Linear Regression, Logic Regression etc) and Data Visualization with Histograms, Kernel Density Plots, Box and Violin Plots, Clustered Matrices, Regression Plots etc.

4. Advanced Excel Techniques – in this module students will learn Excel advanced techniques, including Math Functions, Logical Functions, Statistical Functions, Lookup, Sort/Filter Data, Pivot Tables and Pivot Charts, Data Analysis Tools etc.

5. VBA Programming Fundamentals – this module provides VBA Programming fundamentals, including VBA Sub Procedures, VBA Function Procedures, Excel Objects, and using Macros. VBA is simple but powerful scripting language that will help to analyze data, work with objects and develop automated procedures.

6. Microsoft Access. Overview – this module student reviews fundamental functions of Microsoft Access, such as Tables and Datasheets, Lookups, Forms, Reporting etc.

7. Introduction to SQL Server Databases – this module provides students with the understanding of database concepts and the essentials of relational database with the SQL language. Through a series of hands-on activities students will learn data selection and manipulation using multi-vendor compliant SQL syntax and writing simple queries. The module includses: data types, table structure, essential SQL commands, retrieving data, queries, group functions, data manipulation, control transactions etc.

8. Data Visualization with R – in this module students learn fundamental, intermediate and some advanced techniques for Data Visualization and Business Intelligence. The module starts with the following topics: using the R interface/paradigm to create data visualizations, creating basic calculations (arithmetic, custom aggregations/ratios, date math, and quick table calculations), building Dashboards to share visualizations. After this module students will know how to build some advanced chart types and visualization, complex calculations to manipulate data, use statistical techniques to analyze data, implement advanced geographic mapping techniques and visualizations of non-geographic data, prep data for analysis, combine data sources using data blending.

9. Data Analysis Projects Workshop – at the end of the course students will polish their knowledge practicing Data Analysis and Data Visualization with Excel, Access, Python and R.

180 instructor-led class hours, plus labs

Prerequisites:
Basics of Statistics and Excel. Related education or work experience is a plus.


CREATING RECORD MACROS
Recording a Macro
Running a Macro
Running a Macro from the Macros Dialog Box
Creating a Short-cut key to run a macro
Running a Macro with a Shortcut key
Assigning a Macro to a Menu or Toolbar
Editing a Macro with Visual Basic
VISUAL BASIC EDITOR
Objects
Methods
Properties
Programming Tools
The Menu Bar
THE PROJECT EXPLORER
Using the Project Explorer
USING CONSTANTS
Excel Constants
Variable Constants
EXCEL OBJECTS
Objects, Properties and Methods
Getting & Setting Properties
Calling Methods
Passing Arguments
Singular Objects & Collections of Objects
USING VISUAL BASIC FUNCTIONS
InputBox Function
MsgBox Function
Using a Set Statement
BUILDING FORMULAR CONTROL STRUCTURES
If Then Janision Structures
Logical Operators
Select Case Janision Structures
Case Else
Comparison Operators with Select Case Structure
For Loops
Do Loops
While...Wend Statement
RUNNING CODE
Run mode and Design mode
Running Code from the development environment
Running Code from the host application
THE PROPERTIES WINDOW
Changing a Property
VARIABLES
Dimensioning a variable
Using variables in routines
Object Variables
USERFORMS
Designing & Creating Forms
Working with Controls
Creating Custom Dialog Boxes
Userform Properties Methods & Events
ADVANCED VBA PROGRAMMING TECHNIQUES
Developing Excel Utilities with VBA
Error Handling
Using In-Built Excel Features in VBA
Working with Pivot Tables
Working with Charts
Understanding Excel's Events
Interacting with Other Applications
Creating and Using Add-Ins
DEVELOPING APPLICATIONS
Working with the Ribbon
Working with Shortcut Menus
Providing Help for Your Applications
Developing User Oriented Applications
EVENT HANDLING
VBA Editing & Debugging
Auto Macros
Error Handling