In our Bellevue Data Science course you will learn an overview of the various data jobs and where data science stands, some statistics, some machine learning, how to acquire data from various sources, how to visualize data in meaningful way, how to do exploratory analysis to get an understand of the data and its quality, how to perform classification of data, how to predict/forecast what might happen in future, how to optimize decision making and how to tell an impactful data story. You will also get tips on advancing your career in the data science field.
Market research on the field:
- #1 in the List of Best Jobs in America
- #1 in the List of 25 Jobs with Work-Life Balance
- The 10 Hardest Jobs to Fill Right Now
- Harvard Business Review calls it sexiest job of 21st century
- 190,000 predicted shortage in Data Scientists by 2018
Jobs for data scientist:
June 21st, 2018
Thursdays 6 – 9pm
& tools of the trade
|Introduction to Analytics|
Analytics in Decision Making
Maturity modelSetup with toolsIntroduction to RDemo & Lab
|Data Acquisition, Data Profiling||Sources of Data|
Data storage and Acquisition
Data Quality Framework
Data ProfilingContinue learning RIntroduction to Azure MLDemo & Lab
|Descriptive||Visualization||Introduction to visualization|
Tools & Techniques for Visualization
Demo using PowerBI & Lab
|Exploratory Analysis||Introduction to Statistics|
Univariate & Bivariate Distributions
Demo & Lab
|Descriptive||Exploratory Analysis||Introduction to Real-Time Stream Analytics|
Demo & Lab: Creating end to end stream analytics pipeline and visualization
|Predictive||Simple Linear Regression||Introduction to Regression|
Demo using Excel & R & Lab
|Predictive||Multiple Linear Regression||Multiple Linear Regression|
Estimation of Regression Parameters and Model Diagnostics
Dummy, Derived & Interaction Variables
Demo using R & Lab
|Predictive||Logistic Regression||Logistic Regression|
Estimation of Parameters and Model Diagnostics
Logistic Model Deployment
Demo using R & Lab
Support Vector Machine
|Introduction to Decision Trees|
Classification and Regression Tree (CART)
Naive Bayes Classification Support Vector Machine
Neural Network Demo & Lab
Demo & Lab
|Predictive||Forecasting and Time series Analysis||Forecasting|
Time Series Analysis
Auto-regressive Integrated Moving Average (ARIMA)
Forecasting AccuracyDemo & Lab
|Introduction to Linear/Integer/Network model|
Demo of Linear/Integer/Network Model Building
Introduction to Simulation
Demo & LabDiscussion of resources available & Next step forward
Frequently Asked Questions:
Do I need programming experience for this course?
While knowledge of programming is helpful, it is NOT mandatory for this course. We will cover R as part of the course. As long as you bring LOT of passions to learn, can follow through basic algebraic concepts, have some literacy in computer tools like Excel and work hard, you should be fine.
Do I need statistics knowledge for this course?
We will teach you any statistics concept needed as part of the course.
Who should take this course?
The course is designed for audience of diverse backgrounds. If Analytics is a field you REALLY want to learn, you can sign-up for the course whether you are software engineer, product/program manager, Analyst, researcher, consultant, statistician, student etc. We will adjust the approach based on the attendees.
How much time do I need to spend outside of classroom?
It can vary depending on your unique background. However, it usually takes 3-6 hours/week outside the classroom for study and homework.
What types of problems we will be solving as part of the course?
The business problems for demo, labs and homework will be taken from a wide variety of industries representing practical problems.
Would I become a data scientist after finishing the course? How can I transition to ‘Data Science’ career?
We do not promise that with 36 hours of course you will become “Data Scientist”. However, this course is jam packed with topics starting from basic to advanced data science area and follows Gartner maturity model for Analytics. This helps you benchmark your learning. With the knowledge of the course, you will definitely feel confident to expand your knowledge and expertise further and we will show you some potential career paths to transition to “Data Science” career.
Will I be given a certificate after the completion of the course?
Yes, you will be given a certificate of completion for this course after you finish the course.
What is the credential of the teacher?
The teacher has both strong academic and industry background. He has a MS in Computer Science from University of Southern California (USC) and a MBA from University of Washington. He has been working in the Analytics field since year 2000 and has worked for a variety of industries including Retail, CPG, Financial, Telecom etc.