Applied Data Analytics

Data analytics discovers the buried treasure. Learn the essential techniques and tools to reveal clues that help companies launch the best products, market them, and improve how they are produced.

Live remote instruction
Real world exercises
Industry experts
One-on-one mentorship
Jeff Burnett
Data Analytics

Experience:

About the course

Data analytics has transformed from niche work performed by trained statisticians to a core skill requirement for professionals across most fields.  From advertising to product management to financial trading, data analytics is the workhorse that drives smarter decisions and better performance.

Most introductory statistics and analytics courses focus on theoretical frameworks and canned problems.  They fail to teach contextual awareness and decision making, which real-world problems always require.  This course teaches applied data analytics, providing the common workflow of retrieving data, asking the right questions, building models, experimenting and testing results, and ultimately, communicating the data story through visualization.

What are you going to learn?

  • Core SQL data querying skills (join, where, having clauses, etc.)
  • Defining the right metrics and goals to improve performance for a given product or process
  • Designing the optimal experiments to test highest impact on goals
  • Data storytelling and visualization: translating data into consumable, actionable content for non-data analytics audiences
  • Common concepts in data analytics interviews: A/B test cases, pareto questions, etc.
  • Basic probability and statistics
  • Fundamentals of linear models
  • Fundamentals of time-series data
  • How much data is needed?
  • How to decide what data is meaningful
  • What is machine learning and how is it different from statistics?
  • Summarizing data and visualizations for effective reporting and decision making through a reusable workflow

Introduction

  • Why analyze data?
  • What is the difference between data analysis, statistics, machine learning, artificial intelligence, data mining, and math?
  • How much statistics do I really need to know?
  • What tools should I use? Do I need to know how to program?

Probability intro

  • Coin tosses
  • Dice
    • One die, sum of two dice
    • Craps
  • Cards
    • One deck vs multiple decks
    • Probability of poker hands
  • The lottery
    • How much money should I spend to hit the Power Ball?
    • What if we change the rules a little?
  • Drawing numbers with and without replacement
  • Discrete probability vs continuous probability
  • The bestiary of probability distributions
    • Distributions that count events
    • Distributions that in the long run look like the normal distribution
  • The Monte Hall problem (a classic interview question) https://en.wikipedia.org/wiki/Monty_Hall_problem

Statistics intro

  • What is a statistic?
  • Moments of probability distributions
    • Mean (location)
    • Standard deviation (scale)
    • Skew
    • Kurtosis
  • Relationship between counts and frequency
  • Hypothesis tests
    • p-value vs p-hacking

Linear models

  • Is my data just random or is there a pattern? is my data linear?
  • Exploratory data analysis
    • Basic summary statistics that are (almost) universal
    • How do I know if I have enough data?
  • Building a regression model
    • Why regression
    • How many variables should I include? which ones are significant? how do I tell?
    • Does my model fit the data?
  • Time series analysis
    • Seasonality, how to test for it and how to build a model to accommodate it
    • Autocorrelation, how to test for it and how to build a model to accommodate it
  • Building an analysis of variance model
    • Why do ANOVA?
    • Explaining results

Bayesian techniques (very lite, really just to understand the jargon)

  • Bayes’ method vs Bayesian techniques
  • Prior vs posterior

Data viz

  • What BI/viz tool should I use?
    • Excel
    • Tableau
      • Exploratory data analysis, plotting data and visualizing
      • Publishing reports, more formal (pdf style)
    • Things that require programming
  • Making viz understandable
    • Keeping it simple
    • What points do I want to convey
  • Simple plots in Tableau
$1,795
$2,450 if you apply before Nov. 10
$150 tuition rebate per student that you refer — and they get $150 off their tuition, too.
APPLY NOW
$ 2,950.00 USD
Duration: 
5hr 42min
Upcoming start dates:
November 30, 2020
December 7, 2020
December 14, 2020
December 28, 2020
January 4, 2021
Time requirements:
2 weeks live instruction + 4 weeks group project
Exact schedule will vary slightly around holidays.
Resume preparation:
  • Live project for corporate sponsor
  • Alpha Fellowship resume editing & LinkedIn profile review

About the Course

About the course

Data analytics has transformed from niche work performed by trained statisticians to a core skill requirement for professionals across most fields.  From advertising to product management to financial trading, data analytics is the workhorse that drives smarter decisions and better performance.

Most introductory statistics and analytics courses focus on theoretical frameworks and canned problems.  They fail to teach contextual awareness and decision making, which real-world problems always require.  This course teaches applied data analytics, providing the common workflow of retrieving data, asking the right questions, building models, experimenting and testing results, and ultimately, communicating the data story through visualization.

What are you going to learn?

  • Core SQL data querying skills (join, where, having clauses, etc.)
  • Defining the right metrics and goals to improve performance for a given product or process
  • Designing the optimal experiments to test highest impact on goals
  • Data storytelling and visualization: translating data into consumable, actionable content for non-data analytics audiences
  • Common concepts in data analytics interviews: A/B test cases, pareto questions, etc.
  • Basic probability and statistics
  • Fundamentals of linear models
  • Fundamentals of time-series data
  • How much data is needed?
  • How to decide what data is meaningful
  • What is machine learning and how is it different from statistics?
  • Summarizing data and visualizations for effective reporting and decision making through a reusable workflow

Our courses include live projects and guest speakers from companies such as

Weapons of Financial Destruction: Options in Practice

Warren Buffet coined options "Weapons of Financial Mass Destruction" yet even he turned the launch keys. What did he mean, and why did he use them? Learn the core mechanics and use-cases of options in the real-world for investors ranging from retail to institutional scale.

Applied Finance

Money makes the world go 'round, but who controls it? Learn how investment banks, private equity, venture capital and hedge funds direct money to opportunities. And the survival tools of finance professionals — including three statement analysis, core excel skills and valuation techniques.

Integrated Digital Marketing

Marketing, advertising, business development, and sales are the primary forces behind revenue growth. Learn the complex, nuanced mix of creative ideas, best practices, processes, and technology that drive marketing operations at companies large and small.