Introduction to Data Analytics – June 13th, 2019


Data is considered as new gold, and data scientists are the new gold miners. Learn the skills you need for this hottest job of our time. This 12-week hands-on course will take you an an end-to-end journey in data science career.

Ready to reinvent yourself and become a data scientist? Please fill out an application so that we can learn more about you and determine if this course is the best fit for you and your goals. As soon as we receive your application, we’ll be in contact. Get started.

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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:

Jobs for data scientist:,14.htm

Check out this video about how much Data Analysts make?


Anwar Hossain


June 13th, 2019


Thursdays 6 – 9pm


320 120th Ave NE, Room B 105

Bellevue, WA 98005

(Click for directions)

Course Outline:

Area Description Topics
Introduction Overview

& 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 Types
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
Descriptive +
Exploratory Analysis Introduction to Statistics

Univariate & Bivariate Distributions

Probability 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
Model Development
Model Validation
Demo using Excel & R & Lab
Predictive Multiple Linear Regression Multiple Linear Regression
Estimation of Regression Parameters and Model Diagnostics
Dummy, Derived & Interaction Variables
Model Deployment
Demo using R & Lab
Predictive Logistic Regression Logistic Regression
Estimation of Parameters and Model Diagnostics
Logistic Model Deployment
Demo using R & Lab
Predictive Decision Tree

Support Vector Machine

Neural Network

Introduction to Decision Trees
Classification and Regression Tree (CART)
Naive Bayes Classification Support Vector Machine
Neural Network Demo & Lab
Predictive Unsupervised learning

Ensemble Methods

K-Means Clustering

Bootstrapping, Bagging

Random Forests

Demo & Lab

Predictive Forecasting and Time series Analysis Forecasting
Time Series Analysis
Auto-regressive Integrated Moving Average (ARIMA)
Forecasting AccuracyDemo & Lab
Prescriptive Optimization, Simulation

Next Step

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.


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