act2.careers - Integer School Student Handbook & Course Catalog … · 2019. 9. 20. · Integer...

Integer School

Student Handbook & Course Catalog 2020

www.integer.school 207 Van Vorst St, #1504 Jersey City, NJ 07302 [email protected]

Disclosures Disclosure Statement to Prospective Students Changes in Policies 2020 Pilot Program

About Integer School

Vision and Mission

Institutional Objectives

Administration

School Calendar Observed Holidays Class Schedules Program Starting Dates

Registration Requirements

School Policies Nondiscrimination Policy Non-US Citizens

Code of Conduct

Conditions for Dismissal

Re-entry Policy

Credit for Previous Training

Student Rights

Dispute Resolution / Grievance / Appeal Procedure

Standards for Student Achievement

Student Records

Costs

Financial Aid, Student Loans, Grants, and Scholarships

Credit Disclaimer Statement

Facilities

Student Services Career Services

All rights reserved © 2019 Integer School LLC Student Handbook & Course Catalog 2020, page 2

Student Equipment Requirements Computer Bandwidth Requirement

Program Offered by Integer School Technical Skills Communication Skills Business Skills Career Skills Team Projects

Course Descriptions Probability and Statistics - 30 hours Data Analysis with Excel - 30 hours Python Programming - 75 hours Python Data Analysis & Machine Learning with SciKit - 75 hours TensorFlow for Deep Learning with Python - 50 hours R Programming - 50 hours Machine Learning with R - 30 hours Structured Query Language (SQL) and Databases - 25 hours Tableau - 30 hours Data Visualization Design - 10 hours Data Presentation Design - 10 hours Presentations and Public Speaking - 10 hours Accounting - 5 hours Business Fundamentals & Strategy - 5 hours Finance and Financial Modeling - 25 hours Data Science & Business Intelligence Career Guidance - 2 hours Business Etiquette - 5 hours Soft Skills - 2 hours Building Your Network - 4 hours Resume Design - 40 hours Job Search - 40 hours Job Search - 40 hours

Appendix: US Department of Labor, Standard Occupational Classifications


Disclosures

Disclosure Statement to Prospective Students

The Integer School Student Handbook & Course Catalog (the “Handbook”) is published online for students and is intended to be a guide and resource. Integer School expects all students to be familiar with our expectations concerning membership in the Integer School community as set forth in this Handbook. However, this Handbook is not a contract between Integer School and its students. The most recent electronic version of the Handbook is in effect at any given time and will be updated electronically periodically. Please check the online version of the Handbook for the latest version of policies and information as changes are sometimes made during the school year. This Handbook is valid for the 2020 school year beginning on 1 January 2020 and up through 31 December 2020.

Changes in Policies

Integer School further reserves the right to limit student registration for its program, and to change or discontinue classes.

2020 Pilot Program

Integer School will begin operations in 2020 with its Pilot Program. Integer School will admit a smaller group of students to the Pilot Program, those who understand and accept the challenges and risks of being the first class of students at a new school.

About Integer School Integer School was founded in 2019 with a specific social benefit in mind. We formed Integer School for those students who want a good-paying job and career in Data Science and Business Intelligence, but who do not want to live with the crushing burden of student loans.

Vision and Mission Our vision is a lifelong education and career for every person who has the will to succeed. Our mission is to provide an educational program in Data Science and Business Intelligence, which is a profession with many good jobs in many industries, without student loans.

Administration Note: Integer School is a newly launched school, and at this time all correspondence should be directed to Steffen Parratt at [email protected]


School Calendar

Observed Holidays

Integer School is closed on the following holidays:

● New Year’s Day ● Martin Luther King’s Day ● President’s Day ● Good Friday ● Memorial Day ● Independence Day ● Labor Day ● Columbus Day ● Veterans Day ● Thanksgiving Day ● Christmas Day

Class Schedules

Class Hours ● Integer School classes are online and can be attended whenever the student has available

time. This flexible class format is one of the benefits of Integer School’s program.

Learning Team Hours ● Learning teams will have in-person or virtual meetings (e.g., via skype) at times and

locations that suit all learning team members.

Program Starting Dates

Integer School will start its Pilot Program for a small select set of students on 2 January 2020.

Registration Requirements Students must be admitted by Integer School before they are allowed to register for the year-long program. Students must complete the following steps to be registered:

● Identification (current driver's license, birth certificate or passport) must be shown to an admissions officer

● Non-US citizens must show authorization to work in the United States ● High school diploma or G.E.D. certificate must be shown to an admissions officer


School Policies

Nondiscrimination Policy

Integer School does not discriminate on the basis of age, gender, race, color, creed, religion, national or ethnic origin, physical capability, or sexual orientation in its student admissions, employment, educational programs, activities, or administration of educational policies.

Non-US Citizens

Non-US Citizens with authorization to work within the United States are welcome to apply. Documentation of work authorization will be required before enrolling. Integer School does not provide visa services for students, including vouching for student status and any associated charges.

All instruction and coursework will be in English. Integer School will require documentation as proof of English language proficiency, as English language services will not be provided. We require English fluency as established by our Admissions staff and technical assessment. Please note that no external qualification (e.g. TOEFL, IELTS) will be considered in lieu of our individual assessment.

Code of Conduct Integer School is committed to providing a collaborative, supportive, and respectful environment where students can learn and work with each other and with our faculty. Students are expected to comply with all Integer School policies and with Federal, State, and local laws.

The following conduct is unacceptable and will not be tolerated:

1. All forms of bias, including race, ethnicity, gender, disability, national origin, and creed as demonstrated through verbal and/or written communication and/or physical acts.

2. Sexual harassment including creating a hostile environment and coercing an individual to perform sexual favors in return for something.

3. All types of proven dishonesty, including cheating, plagiarism, knowingly furnishing false information to the institution, forgery and alteration or use of institution documents of identification with intent to defraud.

4. Intentional disruption or obstruction of teaching, research, administration, disciplinary proceedings, public meetings and programs, or other school activities.

5. Physical or mental abuse of any person on school premises or at functions sponsored or supervised by the school.

6. Theft or damage to the school premises or damage to the property of a member of the school community on the school premises.


7. Failure to comply with directions of institutional officials acting in the performance of their duties.

8. Violation of the law on school premises in a way that affects the school community’s pursuit of its proper educational objectives. This includes, but is not limited to, the use of alcoholic beverages and/or controlled dangerous substances on school premises.

Conditions for Dismissal Students must maintain good academic standing throughout their time at Integer School. Good Standing includes completing required assessments according to the curriculum calendar.

Students are held accountable to the academic expectations of the program. If a student fails to complete the academic work required of the program, they may be dismissed from the cohort.

Re-entry Policy Students who have been academically dismissed and would like to return to Integer School will be required to reapply for admission. A re-enrollment application will only be considered if the student is able to provide evidence of an improvement on their readiness and motivation for the program. Any re-enrollments after a dismissal must be approved by the administration.

Credit for Previous Training Although we encourage everyone to come in with knowledge of Excel, Python, R, SQL, Tableau and statistics, we do not require or accept credits earned at other institutions or through challenge examinations and achievement tests. Integer School does not have any transfer agreements with other colleges or universities, and does not award credit for prior learning.

Student Rights Students have the following rights:

● Students have the right to equal opportunity education and non-discrimination based on sex, race, color, religion, ancestry, national origin, disability, medical condition, genetic information, marital status, sexual orientation or other categories protected by law of the states in which we operate.

● Students have the right to cancel or withdraw from their course, per Integer School’s Cancellation, Withdrawal and Refund Policy.

● Students have the right to file a complaint with the School, per Integer School’s Dispute Resolution / Grievance / Appeal Procedure.


Dispute Resolution / Grievance / Appeal Procedure Integer School aims to provide a high quality and safe learning environment for all students and staff. Any student member who feels their rights have been violated, who has a dispute with the School, or who wishes to appeal a School decision, is strongly encouraged to bring the issue to Integer School leadership through the following procedure:

(a) Contact the school Director, Steffen Parratt, at [email protected]. All complaints, disputes, grievances, appeals, and other issues are handled by the Director.

(b) Report the dispute/grievance/appeal/issue within five (5) days of its occurrence or when the student becomes aware of the problem.

(c) The Director will reply within 24 hours to acknowledge receipt of the email and will respond with a process and timeline for addressing the issue. The timeline will depend on the specifics of the problem.

(d) The procedure to be followed will depend on the nature of the complaint raised:

○ Small administrative matters may be addressed immediately through email.

○ Matters that are very sensitive, which may not be appropriate for email, will likely lead to a private meeting with the Director.

○ Matters that may require an investigation, or personnel outside of the school, may lead to a longer and more involved timeline and process to be resolved.

In all cases, the Director will respond with a process, timeline, and provide timely updates to the student. Integer School believes that issues should be resolved as soon as possible, to minimize the impact on our learning community and or students’ learning experience.

Standards for Student Achievement Integer School strives to have assessment processes that resemble the working world as much as possible. We assess the skills and tools that are relevant to job success, and deliver the feedback in a manner similar to real-world employment (performance reviews, check-ins, email feedback).

Student Records Student records will be maintained by the school until the school closes. Students will receive a digital certificate upon satisfactory completion of the program.


Costs for Students Integer School’s curriculum consists of online courses from AWS, Coursera, edX and Udemy. Students are responsible for registering and paying for these courses. The courses we have chosen are reasonably priced, and often can be obtained at discount prices. All of the courses in our curriculum cost, in sum, just a few hundred dollars. Students may also have incidental costs, such as the monthly cost of obtaining a Microsoft Excel license.

Financial Aid, Student Loans, Grants, and Scholarships Integer School is not accredited, and therefore does not participate in federal or state financial aid programs. At this time Integer School does not offer grants or scholarships.

Credit Disclaimer Statement Integer School is not licensed to offer college credits.

Facilities Integer School class sessions take place online. There is no physical location for students to meet for instruction.

Student Services Integer School provides Career Services for enrolled students. It does not provide any other services, including housing, food, medical, counseling, financial assistance, child care, athletic facilities, library services, etc.

Career Services

During the third trimester of the Integer School program, students will receive tools, strategies and support to seek and obtain post-program employment in the data science and business intelligence field. However, post-program job placement is primarily the responsibility of the student. While Integer School will support students in this process, it is up to the student to obtain their desired job placement after graduation.


Student Equipment Requirements

Computer

Students are responsible for providing their own desktop computer or laptop (PC or Mac) that is less than 4 years old and is not a Chromebook. An internal or external microphone and webcam are also required. No equipment is purchased through Integer School. Integer School is not responsible for resolving problems with student’s computers.

Bandwidth Requirement

Many Integer School class sessions and group sessions take place online via video. Students are required to have access to high-speed internet that allows for video chat communication.


Program Offered by Integer School Integer School is composed of three main experiences, which map to each 14-week trimester of the program. A total of 1,200 clock hours are required for the program.

Integer School's program is designed to develop students' skills in the following areas: technical, communication, business, and career related.

Technical Skills

The core of our curriculum is technical skills development. Our students learn new skills, learn how to continue learning new skills, and practice those skills in a variety of projects over the course of a year. Students will have a strong working knowledge of Microsoft Excel and Tableau software packages, and the Python, R and SQL languages. Students will have developed the confidence and resources to learn new languages on their own, as necessary. Students will be able to acquire data from a variety of sources, manipulate it, clean it, analyze it, visualize it, perform statistical tests, draw meaningful conclusions, and learn from it.

Below are the specific skill areas areas of study:

● Probability and Statistics ● Data Analysis with Excel ● Python Programming ● Python Data Analysis & Machine Learning with SciKit ● TensorFlow for Deep Learning with Python ● R Programming ● Machine Learning with R ● Structured Query Languages (SQL) and Databases ● Tableau

Communication Skills

Students will understand the importance of communication to their business audiences, and the practice of telling stories through data in reports, presentations, and meetings. Students will learn the importance of the design of data visualization Student teams will practice their skills presenting their findings to audiences, and will receive feedback and coaching.


● Data Visualization Design ● Presentation Design ● Presentations and Public Speaking


Business Skills

Students will be working on business-related projects, and therefore will learn the language of business and concepts. For example, students will understand depreciation, marginal return, and net present value, and other concepts that are often used in Business Intelligence.


● Accounting ● Business Fundamentals & Strategy ● Finance and Financial Modeling

Career Skills

Finally, students will learn and exercise career skills towards the end of the year, as they are looking for their new job and career.


● Data Science Career Guidance ● Business Etiquette ● Soft Skills ● Building Your Network ● Resume Design ● Job Search ● Interviewing

Team Projects

Students are members of several 5-person learning teams through the year, and those teams work on projects together. There are three projects in the first trimester, each is focused on one package/language: Excel/Tableau, Python, and R. These are structured and well-known data science projects, where students can practice their technical and presentation skills.

The three projects in the second semester require teams to experiment with structuring the problem and applying their project management skills. These projects blend in newly learned business concepts. Teams also participate in a kaggle.com data science competition to become part of the broader Data Science community.

In the third semester, students take on actual business projects from local employers and then present their findings directly to local employers.

All of these projects are posted may be posted on students' GitHub repositories to showcase their skills and experience.


Course Descriptions

Probability and Statistics - 30 hours

This course provides an introduction to the concepts in probability and statistics that are used in data science and business intelligence. The course can be completed easily in 30 hours for students with some familiarity with probability and statistics; students with no experience in these topics will require more time. The course outline is provided below:

Data

● Measuring Data ● Measurements of Central Tendency ● Measurements of Dispersion ● Quartiles and IQR ● Bi-variate Data and Covariance ● Pearson Correlation Coefficient

Probability

● Permutations ● Combinations ● Intersections, Unions, and Complements ● Independent and Dependent Events ● Conditional Probability ● Addition and Multiplication Rules ● Bayes Theorem

Distributions

● Uniform ● Binomial ● Poisson ● Normal ● Formulas and Z Scores ● Dash Scripts

Statistics

● Sampling ● Central Limit Theorem ● Standard Error ● Hypothesis Testing ● Type 1 and Type 2 Errors ● Student's T Distribution


Analysis of Variance (ANOVA)

● F Distribution ● Two Way ANOVA ● Two Way ANOVA with Replication

Regression

● Linear Regression ● Multiple Regression

Chi-Square Analysis

Data Analysis with Excel - 30 hours

This course provides an introduction to Microsoft Excel, and also covers advanced topics for data analysis. The course outline is provided below:

Foundations

● User Interface ● Keyboard Shortcuts ● Basic Editing ● Freeze Panes and Split Boxes ● Formatting numbers ● Formatting cells ● Grouping and Hiding Rows or Columns ● Installing Excel Add-Ins

Formulas and Functions

● Basic Math Functions ● Conditional Math Functions ● MIN, MAX, and AVERAGE Functions ● Logical Operators IF, AND, & OR ● Anchoring Cell References and Name Ranges ● Array Functions ● Internal Rate of Return (IRR) Functions ● Net Present Value (NPV) and Cash Multiple ● Loan Amortization Table ● Compound Annual Growth Rate (CAGR) ● Formula Auditing ● Fill Commands ● IS Functions ● Rounding Numbers

Text, Times, Dates Data Formats

● How Excel Treats Text, Times, and Dates Data


● Changing the Case of Text ● Combining Strings of Text Together ● Cutting Substrings from a Text ● Using the FIND Function ● Searching for Specific Text in Data ● Splitting a Text Field into Multiple Columns ● Converting Dates from Text into Values ● Adding and Subtracting Dates ● Compiling Monthly and Annual Tools ● Conducting Day of the Week Analysis ● Combining Date and Time Data ● Adding and Subtracting Times ● Conducting Hours of the Day Analysis

Statistics in Excel

● Mean, Median, and Mode ● Percentile and Percentile Rank ● Frequency Distributions ● Standard Deviation and Variance ● Trends in Data ● Correlation

Handling & Wrangling Data in Excel

● VLOOKUP ● HLOOKUP ● Name Arrays for Reuse and Readability ● Group Records with Nested If Statements ● Avoiding Errors with IF and ISNUMBER ● Create Dropdown Lists for Filtering ● Create Unique Lists ● Fast Lookup with INDEX and MATCH ● Multi-Condition Lookup with DSUM ● Create Rankings with SMALL and LARGE ● Multi-Condition Lookup with INDEX and MATCH

Tables

● Insert a Pivot Table ● Prepare Data for Pivoting ● Learn to Pivot Table ● Group Rows and Columns ● Format for Readability ● Modify Output Values for Insights ● Insert Calculated Fields


● Sort, Filter, and Slicer ● Handle New Data in Pivot Tables

Charts

● Line ● Pie ● Bar ● Column Chart - Clustered ● Column Chart - Stacked ● Column Chart - 100% Stacked ● Combination Chart ● Scatter Plot ● Area Chart ● Bubble Chart ● Gantt Chart ● Bullet Chart ● Waterfall Chart

Case Study: Cryptocurrency Data

● Sourcing and Preparing Data ● Bitcoin vs S&P500 ● Ethereum, Litecoin, and Bitcoin ● Moving Averages

Case Study: Survey Data

● Preparing Data ● Pivoting Data ● Joining Tables

Case Study: Startup Market

● Startup Funding CSV File Import ● Cleaning Data ● Joining Tables ● Making Unique Lists ● Scan the Data ● Dig into the Categories ● Pivot and Chart Data ● Analysis Leads to Finding

Python Programming - 75 hours

This course provides an introduction to the Python programming language, and also covers advanced topics for data analysis. The course outline is provided below:

Overview


● Python 3 versus Python 2

Python 3 Setup

● Command Line Basics ● Installing Python 3 ● Running Python Code ● Getting Notebooks and Course Material ● Git and GitHub Overview

Object and Data Structure Basics

● Python Data Types ● Numbers ● Variable Assignments ● Strings ● Indexing and Slicing with Strings ● String Properties and Methods ● Print Formatting with Strings ● Lists ● Dictionaries ● Tuples ● Sets ● Booleans ● I/O with Basic Files

Comparison Operators

● Comparison Operators ● Chaining Comparison Operators with Logical Operators

Statements

● If Elif and Else Statements ● For Loops ● While Loops ● Useful Operators ● List Comprehensions

Methods and Functions

● Methods and Python Documentation ● Functions ● *args and **kwargs ● Lambda Expressions, Maps, and Filter Functions ● Nested Statements and Scope

Object Oriented Programming

● Attributes and Class Keyword


● Class Object Attributes and Methods ● Inheritance and Polymorphism ● Special (Magic/Dunder) Methods

Modules and Packages

● Pip Install and PyPi ● Module and Packages ● >__name__ and __main__

Errors and Exceptions Handling

● Pylint ● Running tests with the Unittest Library

Decorators

Generators

Advanced Modules

● Collections Module - counter ● Collections Module - defaultdict ● Collections Module - OrderedDict ● Collections Module - namedtuple ● Datetime ● Python Debugger - pdb ● Timing your code - timeit ● Regular Expressions - re ● StringIO

Advanced Objects and Data Structures

● Advanced Numbers ● Advanced Strings ● Advanced Sets ● Advanced Dictionaries ● Advanced Lists

Introduction to Graphical User Interfaces (GUIs)

● Ipywidgets ● Interact Functionality ● GUI Widget Basics ● List of Possible Widgets ● Widget Styling and Layouts

Python 2 Reference


Python Data Analysis & Machine Learning with SciKit - 75 hours

This course provides an introduction to the relevant Python libraries and describes their use in machine learning with the SciKit-Learn package. The course outline is provided below:

Environment Set-Up

Jupyter Overview

Python

NumPy

● NumPy Arrays and Indexing ● NumPy Operations

Pandas

● Series ● DataFrames ● Missing Data ● Groupby ● Merging Joining and Concatenating ● Operations ● >Data Input and Output

Matplotlib

Seaborn

● Distribution Plots ● Categorical Plots ● Matrix Plots ● Grids ● Regression Plots ● Style and Color

Plotly and Cufflinks

Geographical Plotting

● Choropleth Maps

Introduction to Machine Learning

Linear Regression

● Model Selection Update for SciKit Learn 0.18

Cross Validation and Bias-Variance Trade-Off

Logistic Regression

K Nearest Neighbors


Decision Trees and Random Forest

Support Vector Machines

K Means Clustering

Principal Component Analysis

Recommender Systems

Natural Language Processing

Big Data and Spark with Python

● Big Data Overview ● Spark Overview ● Local Spar Set-Up ● AWS Account Set-Up ● EC2 Instance Set-Up ● SSH with Mac or Linux ● PySpark Setup ● Lambda Expressions Review ● RDD Transformations and Actions

Neural Nets and Deep Learning

● Neural Network Theory ● TensorFlow Basics ● TensorFlow Estimators

TensorFlow for Deep Learning with Python - 50 hours

This course provides an introduction to Google’s TensorFlow framework to create artificial neural networks for deep learning. The course outline is provided below:

Installation and Setup

Introduction Neural Networks

● Introduction to Perceptron ● Neural Network Activation Functions ● Cost Functions ● Gradient Descent Backpropagation ● TensorFlow Playground

TensorFlow Basics

● TensorFlow Basic Syntax ● TensorFlow Graphs ● Variables and Placeholder ● A Neural Network


● Regression Example ● Classification Example ● Saving and Restoring Models

Convolutional Neural Networks (CNN)

● MNIST Data Overview ● CNN Theory ● CNN MNIST Code

Recurrent Neural Networks (RNN)

● RNN Theory ● Vanishing Gradients ● LSTM and GRU Theory ● RNN and TensorFlow API ● Word2Vec Theory

Misc Topics

● Deep Nets with TensorFlow Abstractions ● Tensorboard

AutoEncoders

● Autoencoder Basics ● Dimensionality Reduction with Linear Autoencoder ● Stacked Autoencoder

Reinforcement Learning with OpenAI Gym

● Introduction to OpenAI Gym ● OpenAI Gym Actions ● Simple Neural Network Game ● Policy Gradient Theory ● Policy Gradient Code

Generative Adversarial Networks


R Programming - 50 hours

This course provides an introduction to the R Programming language for data analysis. The course outline is provided below:

Installation and Set-Up

Development Environment

● Guide to RStudio

R Basics

● Arithmetic ● Variables ● Basic Data Types ● Vector Basics ● Vector Operations ● Vector Indexing and Slicing ● Getting Help with R and RStudio

R Matrices

● Creating a Matrix ● Matrix Arithmetic ● Matrix Operations ● Reference of Built-in Functions ● Matrix Selection and Indexing ● Factor and Categorical Matrices

R Data Frames

● Data Frame Basics ● Data Frame Indexing and Selection ● Overview of Data Frame Operations

R Lists

Data Input and Output with R

● CSV Files with R ● Excel Files with R ● SQL with R ● Web Scraping with R

R Programming

● Logical Operators ● if, else, and else if Statements ● While Loops ● For Loops


● Functions

Advanced R Programming

● Built-in R Functions ● Apply ● Math Functions with R ● Regular Expressions ● Dates and Timestamps

Data Manipulation with R

● Guide to Using Dplyr ● Pipe Operator ● Guide to Using Tidyr

Data Visualization with R

● Overview of ggplot2 ● Histograms ● Scatterplots ● Barplots ● Boxplots ● 2 Variable Plotting ● Coordinates and Faceting

Interactive Visualizations with Plotly

● Overview ● Resources for Plotly and ggplot2

Machine Learning with R - 30 hours

This course revisits machine learning topics we explored in Python, but this time we implement them in R. The course outline is provided below:

Introduction

Linear Regression

Logistic Regression

K Nearest Neighbors

Decision Trees and Random Forests

Support Vector Machines

K-Means Clustering

Natural Language Processing

Neural Networks


Structured Query Language (SQL) and Databases - 25 hours

This course is an introduction to SQL and databases.

The course outline is provided below:

Setup and Installation

SQL Statements

● SELECT Statement ● SELECT DISTINCT ● SELECT WHERE ● COUNT ● LIMIT ● ORDER BY ● BETWEEN ● IN ● LIKE

GROUP BY Statements

● MIN, MAX, SUM, AVG ● GROUP BY ● HAVING

JOINS

● AS ● Join Types ● Inner Join ● Outer Join ● Union

Advanced SQL Commands

● Timestamps and Extract ● Math Functions ● String Functions and Operators ● SubQuery ● Self-Join

Creating Databases and Tables

● Data Types ● Primary Keys and Foreign Keys ● Create Table ● Insert ● Update


● Delete ● Alter Table ● Drop Table ● CHECK Constraint ● NOT NULL Constraint ● UNIQUE Constraint

Views

PostGreSQL with Python

Databases and Tables

● Creating and Restoring a DatabaseRestoring a Table Schema

Tableau - 30 hours

This course is an introduction to Tableau.


Setup and Installation

Working with Groups

● Creating Static Set ● Creating Dynamic Set ● Combining Sets ● Controlling Sets with Parameters ● Dashboard: The Startup Quadrant ● Dashboard: The Startup Quadrant

Advanced Table Calculations

● Creating Multiple Joins ● Calculated fields vs Table Calculations ● Creating Advanced Table Calculations ● Saving a Quick Table Calculation ● Specifying Direction of Computation ● Writing your own Table Calculations ● Adding a Second Layer Moving Average ● Quality Assurance for Table Calculations ● Trendlines for Power-Insights ● Creating a Storyline ● Executive Report is Ready

Advanced Data Prep & Analytics

● Building Box Plots in Tableau ● Analyzing Box Plots


● Working with Large Data Sources ● Pivot & Split ● Use Case for Data Source Filters ● Trendlines ● Advanced Time Series Blending ● Calculating Sales Per Capita ● Forecasting in Tableau ● How to Present a Storyline

Creating Animations

● Editing Blending Relationships ● Building the Visualization ● Adding Animation ● Manually Sorting Blended Data ● Leaving a Trail in your Animations ● Finalizing the Dashboard

Level of Detail (LOD) Calculations

● Preparing the Workbook ● Aggregation and Granularity ● LOD Calculations Intuition ● LOD Type 1: INCLUDE ● Understanding ATTR() in Tableau ● LOD Type 2: EXCLUDE ● Multiple Fields in an LOD Calculation ● LOD Type 3: FIXED ● Finalizing the Visualization

Data Visualization Design - 10 hours

This course is an introduction to data visualization design.


The Beauty of Data Visualization

The Art of Data Visualization

Declutter Data Visualizations

● Gestalt Principles of Visual Perception ● Proximity ● Similarity ● Enclosure ● Closure ● Continuity ● Connection


● Case Study: Employing Visual Order ● Case Study: Creating Clear Contrast ● Case Study: If it's hard to read, it's hard to do ● Case Study: Strip it Down & Build it Up

Data Presentation Design - 10 hours

This course is an introduction to data presentation design.


Storytelling with Data

Presentations and Public Speaking - 10 hours

This course is an introduction to presentations and public speaking.


Establish Goals

Five Main Points

The Stories Behind the Numbers

Test Your Communication

Command the Room for Two Minutes

You are the Messenger

PowerPoint Tips

Video Practice

Critiques from Others

Accounting - 5 hours

This course is an introduction to accounting, the “language of business”.


Accounting Basics

● What is Accounting ● Bookkeeping ● Financial Accounting ● Managerial Accounting ● Income Taxes

Financial Accounting

● The Balance Sheet


● Balance Sheet versus Income Statement ● Income Statement Overview ● Cash Flow Statement ● Financial Reporting

Managerial Accounting

● Product Costing ● Break-even Analysis ● Budgets ● Performance Evaluation

Income Taxes

● Tax Brackets and Tax Rates ● Tax Deductions and Credits ● Capital Gains and Ordinary Income

Business Fundamentals & Strategy - 5 hours

This course is an introduction to the fundamentals of business and business strategy.


Why Companies need a Strategy

● Distinguishing Corporate and Business Strategy ● Mission, Goals and Values

The Industry Life Cycle Model

● Introduction Stage ● Growth Stage ● Maturity Stage ● Decline Stage

The Five Forces Model

● The Threat of New Entrants ● The Threat of Substitute Goods ● Competitor Analysis ● Managing the Relationship with Suppliers ● The Bargaining Power of Clients ● Applying the Five Forces in Practice

Applied Game Theory

● The Essence of Zero-Sum Games ● Game Theory and Why it is Useful in Strategy ● Understanding the Prisoner's Dilemma and its Implications in Business ● Game Theory in Practice


The Concept of Competitive Advantage

● Internal Decision Making ● The Company Life Cycle Model ● What is Competitive Advantage ● Acquiring a Sustainable Competitive Advantage ● The Fundamental Role of Resources and Capabilities ● Acquiring a Competitive Advantage

Choosing a Competitive Model

● Focusing on Competitive Strategy ● The Cost Leadership Strategy ● Differentiation ● Niche Strategies ● Hybrid Strategies

Types of Growth Strategies

● Organic ● Inorganic ● Growing Horizontally ● Growing Vertically

SWOT Framework

● SWOT Analysis ● Case Study

Practical Case Study

Finance and Financial Modeling - 25 hours

This course is an introduction to finance and financial modeling.


Introduction

● The Financial Modeling Process ● Financial Model Design

Important Finance & Accounting Principles

● Finance & Accounting Principles ● Balance Sheet & Capital Structure ● Income Statement ● Statement of Cash Flows ● Financial Forecasting Process ● Time Value of Money ● Capital Asset Pricing Model


● Weighted Average Cost of Capital ● Investment Return Metrics

Important Excel Features & Techniques

● Anchoring Cells ● Data Validation ● Conditional Functions ● Financial Functions ● Reference and Lookup Functions ● Dealing with Formula Errors ● Importing External Data ● Leveraging PivotTables & PivotCharts ● Using Solver ● Data Tables ● Recording Macros ● Workbook and Worksheet Security

Building a Financial Forecast Model

● Overview ● Example

Building a Financial Reporting Model

● Overview ● Example

Building a Decision-Making Model

● Overview ● Example ● Peak Capital

Back of the Envelope Models

● Rental Income Example

Data Science & Business Intelligence Career Guidance - 2 hours

This course is an introduction to a career....

A Career in Data Science

Business Etiquette - 5 hours



Build the Know, Like and Trust Factor


● Build the Know Element ● Build the Like Element ● Build the Trust Element

Image

● Your Professional Image ● Dress for Your Industry

Networking

● Seven Networking Archetypes ● Steps to Better Networking

Conversations

● The Building Blocks of a Conversation ● Start, Continue, Ending ● Conversation Pitfalls ● Remembering and Using Names

Nurture Your Digital Footprint

● Importance of Online Presence ● Your Goals and the Right Platform for You ● LinkedIn Profile Basics

Soft Skills - 2 hours



Happy to Help

Communication

Feedback

EQ and IQ

Leading without a Leadership Title

Building a Team

Respect

Positivity

Be Available

Fit In


Building Your Network - 4 hours



Introduction

Don't Keep Score/Generosity

What's Your Mission?

Take Names

Build it Before You Need It

The Genius of Audacity

Warming the Cold Call

Do Your Homework

The Gatekeeper

Leverage Your Passions

Making Your Small Talk Matter

Follow up or Fail

Ping All the Time

Build and Broadcast Your Brand

Connecting in the Digital Age

Connect Your Connectors

Be a Conference Commando

Expand Your Circle

Find Mentors and Mentees

Be Interesting

Balance

Resume Design - 40 hours


Job Search - 40 hours



Job Search - 40 hours



Appendix: US Department of Labor, Standard Occupational Classifications The Data Science and Business Intelligence program prepares students to become analysts in a broad range of fields. While “Data Analyst” is not listed in the US Department of Labor’s Standard Occupational Classifications, there are several similar analyst roles. Listed below are the roles and the occupation code:

● 13-2051, Corporate Financial Analysts

● Financial Analysts, 13-2051

● Market Research Analysts, 13-1161

● Statistical Analysts, 15-2041

● Budget Analysts, 13-2031

Integer School does not guarantee employment in one of the occupations listed above, but we work with the students to help them succeed by obtaining gainful employment.


act2.careers - Integer School Student Handbook & Course Catalog … · 2019. 9. 20. · Integer...

Documents

Transcript of act2.careers - Integer School Student Handbook & Course Catalog … · 2019. 9. 20. · Integer...