Advanced Data Analytics – Sept. 26th, 2019


Data is the new gold and data scientists are the new gold miners. The average salary for a Data Scientist is $120k per year. Learn the skills you need for the hottest job of our time. This 12-week hands-on course will take you into the advanced area of data science. It is intended for those who have some knowledge of Data Analytics or those who have taken our Intro to Data Analytics course.

Please feel free to email us at to find out if this course is right for you or if you will need to start with the introductory course.

Ready to reinvent yourself and learn to code? Get started by filling out an application and we will contact you shortly.

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In our Bellevue Data Science course, you will receive an overview of the various jobs in data and where data science stands in today’s job market. You will learn relevant statistics, some machine learning, how to acquire data from various sources, how to visualize data in meaningful ways, how to do exploratory analysis to understand the data and its quality, how to perform classification of data, how to predict/forecast what may happen in the future, how to optimize decision making, and how to tell an impactful data story. You will also receive tips on advancing your career in the data science field. We will assist you with your resume, prepare you for interviews, and assist you in your job search process by submitting your resume to our partners. We will also show you how to maximize your odds of landing a job by providing guidance on your own job search so you are able to advance your career in the future.



Market research on the field:

Jobs for data scientist:,14.htm  

Check out this video on how much Data Analysts make?


Anwar Hossain


Sept. 26th, 2019


Thursdays 6 – 9pm


2265 116th Ave NE, Ste 200

Bellevue, WA 98004

(Click for directions)

Course Outline:

Area Description Topics
Introduction Overview and Tools of the trade Introduction to Analytics
Analytics in Decision Making
Maturity model Setup with tools. Introduction to Python,

Demo & Lab



Data Acquisition, Data Profiling Sources of Data
Data Types
Data storage and Acquisition
Data Quality Framework
Data Profiling,

Continue learning Python,

Introduction to Azure ML, Demo & Lab

Descriptive Visualization Introduction to visualization
Tools & Techniques for Visualization
Demo using Power BI & 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, Python & Lab
Predictive Multiple Linear Regression Multiple Linear Regression
Estimation of Regression Parameters and Model Diagnostics
Dummy, Derived & Interaction Variables
Model Deployment
Demo using Python & Lab
Predictive Logistic Regression Logistic Regression
Estimation of Parameters and Model Diagnostics
Logistic Model Deployment
Demo using Python & 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 Accuracy, Demo & Lab
Prescriptive Optimization, Simulation

Next Step

Introduction to Linear/Integer/Network model
Demo of Linear/Integer/Network Model Building
Introduction to Simulation
Demo & Lab Discussion 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/Python as part of the course. As long as you bring lot of passion to learn, can follow 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 concepts needed as part of the course.

Who should take this course?

The course is designed for diverse backgrounds; however, our Intro to Data Analytics course is highly recommended if you don’t have knowledge or experience with the basics of ​Excel, SQL, and Power BI used in data analytics. If this 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 of the classroom for homework and study time.

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 cannot promise that you will become a data scientist with a 36 hours course. It depends on how well you are able to analyze and use the data you have in front of you. This course is jam packed with topics starting from basic to advanced data science topics and follows the Gartner Maturity Model for Analytics. We will show you some potential paths to a career in Data Analytics or Data Science based on your personal abilities and accomplishments.

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 pass your final exam.

What is the credential of the teacher?

Anwar Hossain has a strong academic and industry background. He has his MS  in Computer Science from the University of Southern California (USC) and his MBA from the University of Washington. He has been working in the Analytics field since 2000 and has worked for a variety of industries including Retail, CPG, Financial, and Telecom.


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