Data Science with Python Training in Seattle, WA, United States

Get the ability to analyze data with Python using basic to advanced concepts

  • 40 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms
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Description

Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.
Extensive hands-on labs and an interview prep will help you land lucrative jobs.


What You Will Learn

1. Tools & Technologies

Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn

2. Statistics for Data Science

Understand the behavior of data;build significant models using concepts of Statistics Fundamentals

3. Python for Data Science

Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel

4. Exploratory Data Analysis

Use Python libraries and work on data manipulation, data preparation and data explorations

5. Data Visualization using Python

Use of Python graphics libraries like Matplotlib, Seaborn etc.

6. Advanced Statistics & Predictive Modeling

ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

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Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Certification Process

What is Data Science
Data Scientist Skills & Qualifications
How to become a Data Scientist in Seattle, WA
Data Scientist Salary in Seattle, WA
Data Science Conferences in Seattle, WA
Data Scientist Jobs in Seattle, WA
Data Science with Python Seattle, WA

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on: 

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

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Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

Data Science includes exploring data at the microscopic level to understand the complex trends, behavior, and inferences which help companies to make better and smarter decisions based on the results obtained. Data Scientists analyze data to understand the pattern and characteristics of data by applying techniques like synthetic control experiments, inferential models,  time series forecasting, segmentation analysis, etc.

According to reports from Linkedin, Data scientists is the no. 1 most promising job in America for 2019. Some of the common data scientist job titles are as follows:

  • Data Architect
  • Data Scientist
  • Data Analyst
  • Business Intelligence Manager
  • Data Administrator
  • Data/Analytics Manager

Seattle, WA is considered to be the fastest growing city in the US. It has a strong job market and a tech space that is in dire need of data scientists. Companies like Google, Amazon.com, ExtraHop Networks, Zillow Group, T-Mobile, Weyerhaeuser, NordStrom, TenPoint7, etc. are looking for data scientists to analyze their data and find useful insights that can optimize their business processes. The reasons for the popularity of Data Science as a career choice are as follows:

  • Supply-Demand gap: The demand for Data Science will continue to rise both from a company’s perspective and employee’s perspective since the dependency of organizations on data-driven insights is increasing continuously. Many new tools to analyze data have been introduced in the market like Tableau, Sisense, Microsoft Power BI,  SAP, Microsoft Dynamics, and Google Analytics. These tools are easy to use and also increase the skills requirements in the industry. Therefore, the demand is rising more than the supply available, also leading to a rise in salaries. It is estimated that the salaries for data scientists will remain high for upcoming years as well since there is still a lack of skilled and proficient minds in this area.
  • Transform the future of organizations: Data sets if analyzed and worked on correctly, will help to predict and hence shape the future. Banking sectors can use it to analyze the behavior of financial institutions and detect fraud. Data Science in healthcare is making breakthroughs by applying deep learning to recognize problems quickly and accurately, one such example is deepsense.ai, which was used for diagnosing diabetic retinopathy with deep learning. These are just a few examples of how data science can bring about changes in a miraculous way, which also leads to an increase in dependency of organizations on this technology. This creates a rise in job opportunities.

  • Fun to learn: Besides its financial and economic aspects, data science is simply a fascinating discipline with huge opportunities for learning and exploring. Since the potentials of changes it can bring out are still being explored, the only limitation of its application is your imagination. It offers the ideal platform to apply your creativity. There is still great scope for more inventions and discoveries. It’s pretty new and as its contributions in various sectors increases, many new technologies and skills are offered. This also offers a shift from the conventional and monotonous skill sets dominating the industries for a long time.

There are several colleges in Seattle, WA where you could earn a degree in Data Science and get all the technical skills required to be a Data Scientist. Colleges like City University of Seattle and Seattle University are known for their Master’s degree program in Data Science.

The top skills that are needed to become a data scientist include the following:

  1. Programming/Software
  2. Hadoop Platform
  3. Statistics/ Mathematics
  4. Machine Learning and Artificial Intelligence
  5. Data Cleaning
  6. Apache Spark
  7. Data Visualization
  8. Unstructured data

1. Programming/Software: Programming languages and software packages are top skills necessary to be possessed by the data scientists to extract, clean, analyze, and visualize data efficiently. The main programming languages that an aspiring data scientist should be familiar with are as follows:

  • R: R is data analysis software and can be used for statistical analysis, data visualization, and predictive modeling. It is an object-oriented programming language used to explore, model, and visualize data.
  • Python: Analyzing data with Python is easier since a number of tools have been built specifically for data science to efficiently work with Python. Packages tailored to their needs are freely available for download.
  • SQL: SQL or Structured Query Language is a special-purpose programming language used for data insertion, queries, updating and deleting, schema creation and modification, and data access control of data held in relational database management systems.

2. Hadoop Platform: Hadoop is an open-source software framework and is heavily preferred in several data science projects for processing of large data sets. It can store unstructured data such as text, images, and video. Hadoop is equipped with features like flexibility, scalability, fault tolerance, and low cost which makes it a preferable choice for data scientists.

3. Statistics/ Mathematics: A concrete understanding of multivariable calculus and linear algebra is essential for a data scientist since it forms the basis of many data analysis techniques. Math is considered to be the second language for data scientists since it simplifies writing algorithms. Data interpretation requires a deep understanding of correlations, distribution, maximum likelihood estimators and so much more. 

4. Machine Learning and Artificial Intelligence: Machine Learning requires a better understanding of neural networks, reinforcement learning, adversarial learning, etc. It can be considered as a subset of Artificial Intelligence but focuses on making predictions from data available from past experiences. Machine Learning connects Artificial Intelligence with Data Science. Artificial Intelligence focuses on understanding core human abilities such as speech, vision, decision making, language, and other complex tasks, and designing machines and software to emulate these processes through techniques like Computer vision, language processing, and machine learning.

5. Data Cleaning: It is important that the data is correct and accurate before data scientists analyze it. Therefore, a considerable amount of time and effort is spent to ensure this. Data cleaning also termed as data cleansing is identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing it with the correct data. Tools like Trifacta, OpenRefine, Paxata, Alteryx, Data Ladder, WinPure are used for data cleaning. Therefore, data quality should possess the features of accuracy, validity, completeness, uniformity, and consistency.

6. Apache Spark: Apache Spark is a fast and general-purpose cluster computing system designed to cover a wide range of workloads such as interactive queries, batch applications, streaming and, iterative algorithms. The top highlighted features of Apache Spark are as follows:

  • Advanced Analytics
  • Speed 
  • Supports multiple languages

The important feature of Spark is its in-memory cluster computing that increases the processing speed hence provides fast computation.

7. Data visualization: Data visualization tools provide a better and accessible way to see and understand trends, outliers, and patterns in data by using visual elements like maps, graphs, and charts. A good and effective data visualization tool make large data sets coherent. The main focus of data visualization is on information presentation which is achieved through the following:

  • Heat map
  • Gantt chart
  • Treemap
  • Streamgraph
  • Network
  • Bar graph
  • Histogram
  • Scatter plot

8. Unstructured Data: Unstructured data can be defined as data that cannot fit neatly into a database and does not follow the conventional data model like Word documents, email messages, PowerPoint presentations, survey responses, transcripts of call center interactions, and posts from blogs and social media sites. Working with unstructured data provides a better insight into analyzing data.

Below are the top 5 behavioral traits of a successful Data Scientist -

  • Eagerness to learn – Since data science is a field which is evolving at a very fast pace day by day, it is important to keep up with the trend. This requires long term dedication and intellectual curiosity towards this technology. A majority of the time of a data scientist is invested in analyzing and understanding data. However, it's important to remain inquisitive to move ahead in the career. He/she should discuss doubts and queries with senior professionals to excel in this field since the competition is increasing by the day.
  • Business acumen – It is important to understand the business requirements of the organization you are working with, on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. It also helps to categorize the problems on the grounds of priority. 
  • Creativity – Data Science is an ever-evolving technology. New applications and inventions are added every day to its role in reshaping and reinventing almost all organizations. There is a great scope for more skills. This requires you to think in a creative and innovative way. The best part of this field is that it is not limited, so you can enjoy the freedom of your imagination.

  • Communication skills – Data insights usually presented in the form of tables, charts, or any other concise forms are not easily understood. These should be elaborated and explained. A data scientist should have good communication skills to translate the ideas in a way that is understandable by other people. Using a storytelling approach to explain ideas makes it easier to understand.

Data Scientists are in high demand in Seattle, WA right now. The reason behind this is that the city is home to several big tech corporations and there are not enough data scientists to help harness the data these companies process. These companies include Tableau, Thunder, QVC, Logic 20/20, Facebook, Brillio, Convoy, Microsoft, etc.

A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. Some of its benefits can be summarised as follows:

  1. Handsome payoff: The average salary of a data scientist in the US is around $120,000. Due to the demand-supply gap of skilled employees, the salary of data scientists will be rising. According to a report from LinkedIn, Data scientist is the no. 1 most promising job in America for 2019. Data scientist topped Glassdoor's list of Best Jobs in America for the past three years. 
  2. Proper training and certification: Data scientists do not have to create unnecessary and detailed study material for beginners, unlike the IT industry. Many courses offered in data science are created by experts with solid experience and knowledge in the field. Data scientists with certification in related fields of data science can expect around 58% pay raise, which is comparatively higher than non-certified professionals who get 35% chances.
  3. A safe career to pursue: Most of the technologies in the IT industries stays in the market for a while and then is replaced by a different alternative or an upgraded version. This has made most of the roles unstable in IT industry because the employees need to keep on learning new technologies within a short span of time or switch jobs. This causes job insecurity and unreliability. But this is not the case with data science owing to its ever-growing contributions in different sectors. This technology will be getting more advanced in the future with even more opportunities, hence it will not fade anytime soon.
  4. Freedom to work: A data scientist often has no restrictions when it comes to pursuing their own ideas to explore trends and patterns in the data. The best part of the data science industry is that you are not limited to a specific industry. You can explore your options in Healthcare, Finance, FMCG, etc. You can choose to be a part of something that has immense potential. 
  5. Network: Due to the ever-increasing progress in the field of data science, many conferences and workshops are organized from time to time. These workshops invite notable experts from various industries to share their ideas and knowledge with others. It provides a platform for networking with the leading data scientists and form connections.

Data Scientist Skills & Qualifications

Below is the list of top business skills needed to become a data scientist: 

  1. Analytic Problem-Solving
  2. Communication Skills
  3. Business acumen
  4. Teamwork

1. Analytic Problem-Solving – You must possess a data-driven mindset to understand the problem. You must be able to select relevant information and ask the right questions in order to match the pace of your organization. Sometimes, the available information is sufficient enough to guide you to the solution. But at times, you need to use your own problem-solving skills by using all the knowledge you acquire, so you should be able to handle the issues on your own.

2. Communication Skills – Communication skill is very important in data science. In other terms, the way you communicate your findings to an audience of non-data scientists is as important as the findings themselves. As a data scientist, you may have the best idea but it is of no value if you are unable to communicate those ideas to executives, managers, or your colleagues. 

3. Business acumen – It is important to understand the business requirements of the organization you are working with, on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. It also helps to categorize the problems on the grounds of priority. 

4. Teamwork – A data scientist works in a team of people with different roles from different sectors including business, finance, marketing, technology, etc. and each of the members of the team contributes to the development of the work going on.  It is important to understand the team spirit and maintain a jovial and professional bond with the people to promote overall growth and success.

Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps: Boot camps are the perfect way to brush up your basics. They are useful to develop specific skills demanded by your desired company. There is no shortage of boot camps in Seattle, WA. The top boot camps available in Data Science are as follows:
    • Dataquest
    • Springboard
    • Metis
    • Thinkful
    • The Dev Masters
    • Level
    • Jedha
  • Online courses: These are online courses and include some of the latest trends in the industry. These are taught by data science experts and help polish implementation skills in the form of assignments.
  • Certifications: If you want to stand out amidst the competition in Data Science, a Certification is a solution. Certified Data Scientists gets more job opportunities compared to non-certified data scientists. Some of the top certifications available for you to choose from are as follows:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Professional: CCP Data Engineer
    • Cloudera Certified Associate: Data Analyst
    • Certified Analytics Professional (CAP)
    • Dell Technologies Data Scientist Analytics Specialist (DCS-DS)
    • Dell Technologies Data Scientist Associate (DCA-DS)
    • Microsoft Professional Program in Data Science
    • Microsoft Certified Azure Data Scientist Associate
    • Microsoft MCSE: Data Management and Analytics
    • SAS Certified Data Scientist
    • SAS Certified Big Data Professional
  • Projects: Projects help you apply your knowledge in real time and understand how you can contribute to the industry. It will help to upgrade your profile to get you your dream job.

  • Competitions: You get to develop your problem-solving skills and learn many aspects of this technology by participating in Data Science competitions. It will help you to showcase your talent to a bigger audience with experts from data science and at times, recruiters as well.

Seattle, WA is considered to be the fastest growing city in the US where Data Scientists are in high demand in Seattle right now. It has a strong job market and a tech space in dire need of data scientists. The reason behind this is that the city is home to several big tech corporations and there are not enough data scientists to help harness the data these companies process. The companies that are hiring Data Scientists in Seattle, WA include Google, Amazon.com, ExtraHop Networks, Zillow Group, T-Mobile, Weyerhaeuser, NordStrom, TenPoint7, Tableau, Thunder, QVC, Logic 20/20, Facebook, Brillio, Convoy, Microsoft, etc.

The best ways to practice your Data Science skills are as follows:

  • Projects: Data Science projects are a great way to boost your knowledge. You get a practical edge to the problems and their solutions, and you can decipher how data science contributes in real time. It not only lets you apply your knowledge but also enhances your CV, hence increases your chances of getting hired. There are lots of datasets available online. Some of the websites where you can find free data sets are as follows: 
  • Competitions: Participating in Data Science competitions provides a platform to enable you to apply your knowledge to processes and see how you fare in comparison to others. It is an opportunity to showcase your talent to the world and a chance of getting recruited. Some of the platforms are as follows:

How to Become a Data Scientist in Seattle, Washington

Below are the right steps to becoming a successful data scientist:

1. Choose an academic path: It is important to have a Bachelor’s degree in Computer Science or a related field. More and more data scientists are opting for master's degrees and Ph.D. This depends on the role offered by the company. So you must choose your academic path accordingly and choose your specialization according to your interest.

2. Mathematics and statistics: Key concepts in statistics include:

  • Probability distributions 
  • Statistical significance
  • Hypothesis testing
  • Regression.

You should have your basics cleared in statistics. A good understanding of maths helps you to write a better algorithm.

3. Fundamentals: You must have your basics cleared in Data Science which will help you build the foundation for future learning.

4. Programming: It is very essential to have the following programming knowledge:

  • R
  • Python
  • SQL

5. Specializations: Choose your field of interest and the technology that you want to build your career in. Gain proficient knowledge in one or more of the following technologies:

  • Big Data
  • Visualization
  • Machine Learning
  • Artificial Intelligence
  • Data Ingestion
  • Data Munging
  • Business analytics

6. Apply for jobs: Follow up with all the notifications posted by your desired company and keep a check on the posts available. Apply for the jobs you find yourself suitable. You can contact experienced professionals and look up the various interview questions and tips to apply for these jobs.

The essential steps that you should follow to become a data scientist are as follows:

  • Work on your math and statistics skills. A good data scientist must be able to understand what the data is conveying, therefore, you must have concrete skills in basic linear algebra, an understanding of algorithms and statistics skills. 
  • It is important to understand the concept of machine learning. Machine learning is getting more popular but it is inextricably linked to big data. Many machine learning courses are provided by reputed institutions which you can refer to.
  • Have a good understanding of programming languages and platforms. Data scientists must know how to manipulate code and write algorithms in order to program the computer to analyze the data. 
  • Understand databases, data lakes, and distributed storage. Develop a clear understanding of SQL to start with.
  • Learn the concepts of data munging and data cleaning. You can refer to some of the courses available online as well as offline to learn to work on such tools.
  • Data visualization is also an important aspect of Data Science. It will help you to graphically represent the data in a concise form.

Institutions like City University of Seattle and Seattle University have Data Science programs that will help you kick start your career in the Data Science field. These courses will introduce you to the Data Science and how you can apply its concepts in the real world.A degree in data science important because of the following – 

  • Networking – You get an opportunity to connect with a lot of people from your desired field of interest. It helps you grow your professional network and expand your circle.
  • Structured learning – It puts you in a practice to follow sequential learning since you need to follow up with your academics.
  • Internships – While pursuing the degree, you get a lot of opportunities for internships which gives you exposure to hands-on experience in real time.
  • Recognized academic qualifications for your résumé – It boosts up your CV and helps you get recognition among recruiters.

A few institutes have built undergraduate programs that will be akin to a computer science degree. Colleges like City University of Seattle and Seattle University are known for their Master’s degree program in Data Science. Based on the trends in job requirements, the skills in most demand are Hadoop/Big Data, tools including R and SAS, and some domain knowledge. Theoretical knowledge is a prerequisite, but usually good data selection and engineering are more important than advanced algorithms. 

However, there is a strong demand for analytic talent and a shortfall in the supply of skilled employees. If you have a master's degree, it will be add-on for you but if you don't have, many companies will overlook this as long as you have the right skills.

Programming in Data Science is the key skill to have in order to become a Data Scientist. Coding is involved in so many procedures in Data Science. Some of these are as follows: 

  • Hadoop is a platform used for data exploration, data filtration, data sampling, and summarization. 
  •  By using Python, you can easily import SQL tables into your code.
  • SQL is a programming language that can help you to carry out operations like add, delete and extract data from a database through concise commands that can help you to save time and lessen the amount of programming.
  • Programming languages help you clean, arrange and organize an unstructured set of data.
  • According to the most recent O’Reilly data science salary survey, this is either Python(54%) or R (57%).

Data Scientist Salary in Seattle, Washington

The average annual salary for a Seattle based Data Scientist is $106,466.

The average annual salary of a data scientist in New York is $99,716, which is $6,750 less than that of Seattle.

The earning of a Data Scientist is $106,466 per year in Seattle as compared to $125,310 earned by a data scientist working in Boston.

The annual earnings of a data scientist in Seattle is Rs. $106,466 as compared to $88,202 in Washington.

The average annual pay in Seattle for data scientist is $106,466 which is slightly higher than the salary paid to a data scientist in Spokane, which is $97,517.

The average pay for data scientists in Seattle is $106,466 for data scientists, with cities like Bellevue having an average salary of $108,455. 

Currently, the demand for data scientist in Washington is quite high owing to the increasing usage of data science in several firms.

The benefits of being a data scientist in Seattle include the multiple job opportunities and tremendous job growth it offers. 

Apart from the Salary, being a Data Scientist offers many perks and advantages. Data Scientist is a lucrative job that offers job growth. This job is not bound to a particular field. Today, every major organization, no matter the field, is investing their time and money in Data Science. That not only improves the job opportunities but also gets them in the sight of executives. 

Companies like Amazon Web Services, Facebook and Omnidian are hiring Data Scientists in Seattle. 

Data Science Conferences in Seattle, Washington

S.noConference nameDateVenue
1.Data Science Salon | SeattleOctober 17, 2019

TBD Seattle, WA 98101 United States

2.The Business of Data Science - Seattle14 May, 2019 to 15 May, 2019

Hilton Seattle 1301 6th Avenue Seattle, WA 98101 United States

3.Intro to Web Scraping with Python for Data ScienceMay 2, 2019

Galvanize Seattle 11, South Jackson Street Seattle, WA 98104 United States

4.The Inaugural Sounders FC Analytics Conference22 June, 2019 to 23 June, 2019The Ninety 406, Occidental Avenue South Seattle, WA 98104 United States
5.Dataware Symposium SeattleMay 9, 2019W Seattle 1112 4th Ave Seattle, WA 98101 United States
6.Getting Started in Data ScienceMay 22, 2019

The Pioneer Collective 100, South King Street #100 Seattle, WA 98104 United States

7.Data Science in Product DesignMay 15, 2019

Product School Seattle 100 S King St #100 Seattle, WA 98104

8.

Data Management in the Geodatabase - July 23-24, 2019

23 July, 2019 to 24 July, 2019

King Street Center 201 S. Jackson St. Room: 7289 Computer Training Room (located in the 7th floor elevator lobby) Seattle, WA 98104 United States

9.Lunch + Learn : Tiny Data - Intuition in BusinessMay 17, 2019

Modern Species 1917 1st Avenue Suite 400 Seattle, WA 98101 United States

10.Graph Data Modeling with Neo4j - Seattle, WAMay 15, 2019

TLG Learning IBM Building 1200 5th Avenue Suite 1565 Seattle, WA 98101 United States

1. Data Science Salon, Seattle

  • .About the conference: The conference will help attendees learn the application of Machine Learning and Artificial Intelligence in Retail technology and E-commerce. 
  • Event Date: October 17, 2019
  • Venue: TBD, Seattle, WA 98101, United States
  • Days of Program: 1
  • Timings: 7:45 AM – 8:00 PM PDT
  • Purpose: The purpose of the conference is to bring together Data Science practitioners to discuss best practices and new solutions. 
  • Registration cost: $125 – $350
  • Who are the major sponsors: Formulated by

2. The Business of Data Science, Seattle

  • About the conference: The objective of the conference is learning to use Data Science and Artificial Intelligence for making informed decision for your organization. 
  • Event Date: 14 May, 2019 to 15 May, 2019
  • Venue: Hilton Seattle 1301 6th Avenue Seattle, WA 98101 United States 
  • Days of Program: 2
  • Timings: Tue, May 14, 2019, 9:00 AM –Wed, May 15, 2019, 4:30 PM PDT
  • Purpose: The purpose of the conference is to make the business leaders understand the fundamentals of Data Science and how they can implement it in their organization.
  • Registration cost: $2,000 – $2,190
  • Who are the major sponsors: Pragmatic Institute

3. Intro to Web Scraping with Python for Data Science, Seattle

  • About the conference: The conference is for beginners in the field of Data Science to teach them the basics of Python used for web scraping. 
  • Event Date: May 2, 2019
  • Venue: Galvanize Seattle 111 South Jackson Street Seattle, WA 98104 United States
  • Days of Program: 1
  • Timings: 6:30 PM – 8:30 PM PDT
  • Purpose: The purpose of the conference is to teach the basics of high-level programming language, Python for beginners. 
  • Registration cost: Free
  • Who are the major sponsors: Galvanize Seattle

4. The Inaugural Sounders FC Analytics Conference, Seattle

  • About the conference: The aim of the Analytics Conference is to put light on the importance of using analytics to help executives and coaches make an informed decision. 
  • Event Date: 22 June, 2019 to 23 June, 2019
  • Venue: The Ninety 406 Occidental Avenue South Seattle, WA 98104 United States
  • Days of Program: 2
  • Timings: Sat, Jun 22, 2019, 1:30 PM – Sun, Jun 23, 2019, 12:30 PM PDT
  • Purpose: The purpose of the conference is to deal with real-world, day-to-day challenges faced by the decision makers and how to overcome them. 
  • Speakers & Profile: 7
    • Sarah Rudd — VP of Software and Analytics, StatDNA, Arsenal Football Club, London
    • Miguel Rios — Football Intelligence Manager, OptaPro
    • Dafydd Steele — Statistical Researcher, Liverpool Football Club 
    • Sam Gregory — Data Analyst, Sportlogiq
    • Evin Keane — Data Analyst, Sportlogiq
    • Sam Robertson — Head of Research & Innovation, Western Bulldogs–Victoria University partnership
    • Devin Pleuler — Senior Manager for Analytics, Toronto FC
  • Registration cost: $100
  • Who are the major sponsors: Sportlogiq

5. Dataware Symposium, Seattle

  • About the conference: The conference aims to help the attendees with the biggest data challenges including managing data from edge to cloud, orchestrating data across data soils, and feeding data-hungry tools and applications. 
  • Event Date: May 9, 2019
  • Venue: W Seattle 1112 4th Ave Seattle, WA 98101 United States 
  • Days of Program: 1
  • Timings: 8:00 AM – 6:00 PM PDT
  • Purpose: The purpose of the conference is to make the way for a better future for analytics and AI. 
  • How many speakers: 9
  • Speakers & Profile: 
    • Ted Dunning PhD- Chief Technology Officer at MapR Technologies
    • Jack Norris, Senior Vice President Data & Applications at MapR Technologies
    • Charles Wheelus, Principal Data Scientist at Cequint
    • Davor Bonaci, CEO at Operiant
    • Emily Kruger, VP of Product at Operiant
    • Jake Mannix, Data Scientist at Salesforce
    • Li Kang, Technical Director, Partnership at Kyligence
    • Justin Vincent, Data Scientist at MapR Technologies
    • Andrew Chung, Director of Architecture and Analytics at Gesa Credit
  • Registration cost: $59.99 – $199
  • Who are the major sponsors: MapR Technologies

6. Getting Started in Data Science, Seattle

  • About the conference: The conference explores the skills that you will need to become a Data Scientist and what different roads will open up once you have mastered the skill. 
  • Event Date: May 22, 2019
  • Venue: The Pioneer Collective 100 South King Street #100 Seattle, WA 98104 United States
  • Days of Program: 1
  • Timings: 6:30 PM – 8:00 PM PDT
  • Purpose: The purpose of the conference is to understand the emergence of big data and the role of a Data Scientist. 
  • Registration cost: Free
  • Who are the major sponsors: Thinkful Seattle

7. Data Science in Product Design, Seattle

  • About the conference: The conference aims at offering the product manager effective ways to handle problems. 
  • Event Date: May 15, 2019 
  • Venue: Product School Seattle 100 S King St #100  Seattle, WA 98104
  • Days of Program: 1
  • Timings: 6:30 PM to 8:30 PM (PDT)
  • Purpose: The purpose of the conference is to understand the general models used in Data Science for Product Design. It also explores the real life applications of Data Science in Fashion industry. 
  • How many speakers: 1
  • Speakers & Profile: Cristina Perez - Chief Data Scientist at DalmondFx
  • Registration cost: $0 - $20

8. Data Management in the Geodatabase, Seattle

  • About the conference: The conference will explore the usage of ArcGIS to look for advanced methods of accomplishing your goals. 
  • Event Date: 23 July, 2019 to 24 July, 2019
  • Venue: King Street Center 201 S. Jackson St. Room: 7289 Computer Training Room (located in the 7th floor elevator lobby) Seattle, WA 98104 United States 
  • Days of Program: 2
  • Timings: Tue, Jul 23, 2019, 8:30 AM – Wed, Jul 24, 2019, 5:00 PM PDT
  • Purpose: The purpose of the conference is to make sure that the advanced operations, that can enhance the effectiveness and efficiency of GIS, are not overlooked. 
  • Whom can you Network in this Conference: In this conference, you will be able to network with like-minded developers with knowledge of basics of ArcGIS. 
  • Registration cost: $1,050
  • Who are the major sponsors: King County GIS Center

9.Lunch + Learn: Tiny Data - Intuition in Business, Seattle

  • About the conference: The conference will explore neuroscience and its use in business. Participants will learn to balance the Big data with intuition and “gut reactions”. 
  • Event Date: May 17, 2019
  • Venue: Modern Species 1917 1st Avenue Suite 400 Seattle, WA 98101 United States
  • Days of Program: 1
  • Timings: 1:00 PM – 2:30 PM PDT
  • Purpose: The purpose of the conference is to get faster insights leading to innovation using Data Science and Neuroscience.
  • How many speakers: 1
  • Speakers & Profile: Leslie Hale - Principal of Knot Strategy, a brand and market strategy consultancy
  • Registration cost: $22 – $32
  • Who are the major sponsors: Modern Species

10. Graph Data Modeling with Neo4j, Seattle

  • About the conference: The conference will focus on designing and implementing a graph data model. All attendees must be familiar with the Cypher language and the Neo4j. 
  • Event Date: May 15, 2019
  • Venue: TLG Learning IBM Building 1200 5th Avenue Suite 1565 Seattle, WA 98101 United States
  • Days of Program: 1
  • Timings: 9:00 AM – 5:00 PM PDT
  • Purpose: The purpose of the conference is to learn the application of graph model for solving common modeling problems.
  • Registration cost: $149 – $299
  • Who are the major sponsors: Neo4j
S.NoConference nameDateVenue
1.MLconf Seattle: The Machine Learning ConferenceMay 19, 2017

AXIS Pioneer aSquare 308, 1st Avenue South, Seattle, WA 98104, USA

2.The DRIVE/conference (Data, Reporting, Information, Visualization Exchange)23 May, 2017 to 24 May, 2017Hyatt Regency, 900 Bellevue Way NE, Bellevue, WA 98004
3.The Data Science Conference21 September, 2017 to 22 September, 2017Hyatt at Olive 8, Seattle, WA 98101, USA
4.TDWI Accelerate, The Fastest Path to Achieving Your Analytics Goals
October 16-18, 2017
Hyatt Regency Bellevue on Seattle’s Eastside 900 Bellevue Way NE Bellevue, WA 98004
5.PASS Summit
October 31, 2017, to November 3, 2017
Washington State Convention Center
6.The 4th AI NEXTCon Conference
January 17, 2018 - January 20, 2018
Meydenbauer Convention Center 11100 NE 6th St, Bellevue, WA 98004
7.2018 INFORMS Regional Analytics Conference
September 14, 2018
Center for Urban Horticulture NHS Hall, 3501 NE 41st Street Seattle, WA 98105
8.7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial-2018)
November 6, 2018
Seattle, WA
9.PASS Summit 2018
November 6, 2018
Seattle, WA
10.2018 IEEE Int. Conf. on Big Data
December 10, 2018, to  December 13, 2018December 10, 2018, to  December 13, 2018
1900 5th Avenue. Seattle, WA 98101, United States

1. MLconf Seattle: The Machine Learning Conference, Seattle

  • About the conference: The conference helped the attendees in learning the latest trends in machine learning.
  • Event Date: May 19, 2017
  • Venue: AXIS Pioneer Square, 308 1st Avenue South, Seattle, WA 98104, USA
  • Days of Program: 1
  • Timings: 9:00 A.M. - 6:00 P.M.
  • Purpose: The purpose of the conference was to discuss the latest research in machine learning techniques and practices, application of tools, algorithms, and platforms to solve the issues pertaining to data sets.
  • How many speakers: 9
  • Speakers & Profile:
    • Misha Bilenko - Principal Researcher, Microsoft, USA
    • Carlos Guestrin - Founder & CEO at Dato, Inc. USA
    • Bak - Sr. Data Scientist & Mathematician, Ayasdi, USA
    • Robert Moakler - Data Science Intern, Integral Ad Science, USA
    • Ray Richardson - Chief Technology Officer, Simularity, USA
    • Xavier Amatriain - VP Engineering, Quora, USA
    • Mark Zangari - LLC, Quantellia, USA
    • Ewa Dominowska - Engineering Manager, Facebook, USA
    • Ehtsham Elahi - Senior Research Engineer, Netflix, USA

    2. The DRIVE/conference (Data, Reporting, Information, Visualization Exchange), Seattle

    • About the conference: The conference aimed to help attendees gain a better knowledge and learning in data, analytics, visualization, modeling, reporting and more.
    • Event Date: 23 May, 2017 to 24 May, 2017
    • Venue: Hyatt Regency, 900 Bellevue Way NE, Bellevue, WA 98004
    • Days of Program: 2
    • Timings: 12 A.M to 11:59 P.M. (PDT)
    • Purpose: The purpose of the conference was to promote innovative and new ideas in the world of data through interaction with world-class professionals from different areas.
    • How many speakers: 3
    • Speakers & Profile:
      • Heather Campbell, Director of Analytics and Data Management, Princeton University
      • Brenden Goetz, Data Manager, Office of Information Technology, University of Colorado Denver
      • Michael Torregrossa, Senior Director of Information Technology, Chief Technology Officer, The University of Arizona Foundation

      3. The Data Science Conference, Seattle

      • About the conference: The conference helped the professionals to interact and discuss content related to data science.
      • Event Date: 21 September, 2017 to 22 September, 2017
      • Venue: Hyatt at Olive 8, Seattle, WA 98101, USA
      • Days of Program: 2
      • Timings: 9AM to 6PM
      • Purpose: The aim of the conference was to provide a space where professionals can discuss and share ideas related to data science with the purpose to grow as professional analysts and not with the purpose of business.

      4. TDWI Accelerate, The Fastest Path to Achieving Your Analytics Goals, Seattle

      • About the conference: The conference brought together experienced professionals to discuss the hottest topics in data science.
      • Event Date: October 16-18, 2017
      • Venue: Hyatt Regency Bellevue on Seattle’s Eastside, 900 Bellevue Way NE, Bellevue, WA 98004
      • Days of Program: 3
      • Purpose: The purpose of the conference was to highlight the important areas of analytics which included predictive analytics, self-service analytics, advanced analytics and future of advanced analytics.
      • How many speakers: 10
      • Speakers & Profile:
        • Dave McColgin, Executive Creative Director, Artefact
        • Eduardo Arino de la Rubia, Chief Data Scientist, Domino Data Lab
        • Wee Hyong Tok, Principal Data Science Manager, Microsoft Corporation
        • Kirk Borne, Booz Allen Hamilton
        • Donald Farmer, Principal, TreeHive Strategy
        • Skye Moret, Data Visualizer, Periscopic
        • Natasha Balac, Ph.D., President and CEO, Data Insight Discovery, Inc.
        • Sheridan Hitchens, Vice President, Data Products, Ten-X.com
        • Debraj GuhaThakurta, Senior Data Scientist, Microsoft Corporation
        • Francesco Mosconi, Data Scientist, Catalit LLC
        • Mark Madsen, President, Third Nature, Inc.
        • Nicholas Kelly, BluLink Solutions

      5. PASS Summit, Seattle

      • About the conference: Attendees learned the latest technologies in maintaining, designing, and building physical and logical database models.
      • Event Date: October 31, 2017, to November 3, 2017
      • Venue: Washington State Convention Center
      • Days of Program: 3
      • Speakers & Profile:
        • Rob Girard, Sr. Technical Marketing Engineer at Tintri
        • Shawn Meyers, lead Principal Architect for Microsoft SQL Server at House of Brick
        • Aaron Cutshall, Sr. Data Architect
        • Aaron Nelson,  PowerShell Virtual Group of PASS (SQLPS.io)
        • Adam Jorgensen
        • Adam Saxton
        • Ajay Jagannath
        • Alberto Ferrari
        • Alex Andrushchenko
        • Ali Hamud
        • Allan Hirt
        • Amit Banerjee
        • Amit RS Bansal
        • Amy Herold
        • André Kamman, DBA and SQL Server Solutions Architect for CloudDBA
        • Andrew Liu, program manager working on the Azure DocumentDB team at Microsoft
        • Andy Leonard, Data Philosopher at Enterprise Data & Analytics
        • Andy Yun, SentryOne Senior Solutions Engineer and a Microsoft MVP
        • Anthony Nocentino,  Enterprise Architect, Founder and President of Centino Systems
        • Anup Gopinathan, senior DBA consultant with Datavail Corp
        • Argenis Fernandez, Data Platform MVP, Microsoft Certified Master, VMware vExpert and Principal Data Management Architect for Pure Storage
        • Artur Kiulian, Partner at Colab
        • Arvind Shyamsundar, Principal Program Manager on the SQL Customer Advisory Team (SQLCAT.)
        • Ashish Thapliyal, Principal Program Manager in Azure HDInsight
        • Ben Miller, MaritzCX
        • Bill Gibson,  Program Manager Architect on the SQL Server Data Tools team in SQL Server
      • Who were the major sponsors:
        • Microsoft
        • Quest
        • Redgate
        • Sentryone
        • Amazon Web Services
        • Idera
        • Atscale
        • Dell EMC
        • DELPHIX
        • Google Cloud
        • Profisee
        • SolarWinds
        • Vexata
        • Wherescape

      6. The 4th AI NEXTCon Conference, Seattle

      • About the conference: The attendees learned over 60 tech topics and gained practical experience in data science.
      • Event Date: January 17, 2018 - January 20, 2018
      • Venue: Meydenbauer Convention Center, 11100 NE 6th St, Bellevue, WA 98004
      • Days of Program: 4
      • Timings: 8AM to 5PM
      • Purpose: The purpose of the conference was to allow its attendees to connect with over 500 data scientists and tech engineers, and gain in-depth knowledge in NLP, Speech Recognition, computer vision, machine learning, analytics, and data science.
      • How many speakers: 29
      • Speakers & Profile:
        • Steve G - CVP, AI, Microsoft
        • Oren E. - CEO, AI2
        • Nikko S. - Sr. Principal Scientist, Amazon
        • Cristian C. - Engineering Lead, Facebook
        • Jieping Y. - VP, Didi Chuxing
        • Alex S. - Sr. Engineer, Uber
        • Amy U. -  Engineer, Google
        • Baiyang L. - Research Scientist, Facebook
        • Ding D. - Software Engineer, Intel
        • Xiaodong H. - Principal Researcher, Microsoft
        • Mukund N. - Engineer, Pinterest
        • Kip L. - Principal Product Manager, Amazon
        • Fei Y. - Research Scientist, Facebook
        • Miro E. - Data Scientist, NVIDIA
        • Zhen L. - Sr Data Scientist, Microsoft
        • Martin G - Software Engineer, Google
        • Jon P. - Software Engineer, Algorithmia
        • Rangan S. - Analytics Architect, Cray Inc.
        • Chris M. - Manager of AI Instruments, Stitch Fix
        • Zhenliang Z - Staff Engineer, Alibaba
        • Arthur Juliani - Machine Learning Engineer, Unity Technologies
        • Dong Y. - Distinguished Scientist, Tencent AI Lab
        • Joe X. - Tech Lead, Twitter
        • Nick A. - Tech Lead, IBM Watson
        • Tony Q. - Staff Researcher, Didi Research
        • Xiangang L. - Staff Researcher, Didi Research
        • Dennis Y. - CTO, BlitzMetrics
        • Liang Z. - Principal AI Researcher, LinkedIn
        • Anusua T. - Data Scientist, Microsoft

      7. 2018 INFORMS Regional Analytics Conference, Seattle

      • Event Date: September 14, 2018
      • Venue: Center for Urban Horticulture, NHS Hall, 3501 NE 41st Street, Seattle, WA 98105
      • Days of Program: 1
      • Timings: 8:30 AM to 4 PM
      • Purpose: The conference aimed to discuss the latest trends in operation research and analytics tools in order to contribute to the advancement of data science. 
      • How many speakers: 8
      • Speakers & Profile:
        • Jacquelyn Howard - VP, Global Food Supply Chain – Starbucks
        • Jim Cochran - Sports Analytics, University of Alabama
        • Bertan Altuntas - Analytics at Facebook
        • Archis Ghate - Professor, University of Washington
        • Greg Glockner - Mathematical Programming, Gurobi
        • Sareh Nabi - Product Manager, Microsoft
        • Ram Krishnan - Director, eCommerce and Digital Analytics, Samsung
        • Mauricio Resende - research scientist, Amazon

      8. 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial-2018), Seattle

      • About the conference: It connected researchers working in government, academic, and industrial research labs from different areas of spatial analytics.
      • Event Date: November 6, 2018
      • Days of Program: 1
      • Timings: 9 A.M. to 5 P.M.
      • Purpose: The purpose of the conference was to provide a platform for researchers working in government, academic and industrial areas to share and discuss their ideas on the emerging big data challenges.
      • How many speakers: 28
      • Speakers & Profile:
        • Dr. Shashi Shekhar, University of Minnesota
        • Md Mahbub Alam
        •  Suprio Ray 
        • Virendra C. Bhavsar
        • W. Randolph Franklin
        •  Salles Viana Gomes De Magalhaes 
        • Marcus Andrade
        • Shen Ren, Bo Yang
        •  Liye Zhang 
        • Zengxiang Li
        • Peter Baumann
        •  Ismoil Isroilov
        • Vlad Merticariu
        •  Katrin Kohler
        • Mengyu Ma
        •  Wei Xiong
        • Luo Chen 
        • Guo Ning, Jun Li 
        • Ning Jing
        • Haowen Lin 
        • Yao-Yi Chiang
        • Nathan Pool 
        • Ranga Raju Vatsavai
        • Arun Sharma
        •  Syed Mohammed Arshad Zaidi
        •  Varun Chandola,
        •  Melissa R. Allen 
        • Budhendra L. Bhaduri
      • Who were the major sponsors:
        • Microsoft
        • IBM
        • Apple
        • Oracle
        • Uber
        • Amazon
        • HERE Technologies

        9. PASS Summit 2018, Seattle

        • About the conference: It allowed its attendees to gain data knowledge and hands-on practical experiences on data skills and innovation.
        • Event Date: November 6, 2018
        • Days of Program:1
        • Timings: 09:00 AM-06:00 PM
        • Purpose: The purpose of the conference was to share learnings with peers from various fields and discuss the latest technologies and devise solutions that would have helped the attendees in their career growth in the field of data science.
        • How many speakers: 7
        • Speakers & Profile:
          • Amit Banerjee - Enterprise Services at LinkedIn at LinkedIn
          • Allan Hirt - Enterprise Services at LinkedIn at LinkedIn
          • Adam Saxton - Sr. Escalation Engineer at Microsoft at Microsoft
          • Aaron Bertrand - Senior Consultant at SQL Sentry, LLC at SQL Sentry, LLC, USA
          • Andy Leonard - Director at Andy Leonard Associates Ltd. at Andy Leonard Associates Ltd.
          • Grant Fritchey - Product Evangelist at Professional Association for SQL Server (PASS)
        • Who were the major sponsors:
          • Microsoft
          • Quest
          • Redgate
          • Amazon Web Services
          • SentryOne

        10. 2018 IEEE Int. Conf. on Big Data, Seattle

        • About the conference: It allowed its attendees to gain and share knowledge on the latest advancements in different aspects of data science including neural networks, cloud services, speech processing and other challenges in data science.
        • Event Date: December 10, 2018, to  December 13, 2018
        • Venue: 1900 5th Avenue. Seattle, WA 98101, United States
        • Days of Program: 4
        • Purpose: The purpose of the conference was to provide insights into the latest technologies in data science like Big Data for Speech processing, Society 5.0, Decentralized Machine Learning, PCS,  and on metric learning for complete data analysis.
        • How many speakers: 5
        • Speakers & Profile:
          • Blaise Agüera y Arcas - Distinguished Scientist, Google AI, Google, USA
          • Xuedong Huang - Microsoft Technical Fellow of Microsoft Cloud and AI, Microsoft's Speech and Language Group, Microsoft, USA
          • Masaru Kitsuregawa - Professor and Director, Center for Information Fusion, University of Tokyo and National Institute of Informatics (NII), Japan
          • Bin Yu - Chancellor’s Professor, Departments of Statistics and of Electrical Engineering & Computer Sciences, University of California at Berkeley, USA
          • Aidong Zhang - SUNY Distinguished Professor, Program Director, Department of Computer Science and Engineering, CISE/IIS, State University of New York at Buffalo and National Science Foundation, USA

        Data Scientist Jobs in Seattle, Washington

        Here is the logical sequence of steps you should follow to get a job as a Data Scientist.

        1. Choosing a programming language
        2. Brushing up on Mathematics and Statistics
        3. Libraries
        4. Learning Data visualization through libraries available and tools.
        5. Data preprocessing
        6. Learn Machine Learning and Deep Learning 
        7. Natural Language processing
        8. Polishing skills

        If you are thinking to apply for a data science job, then follow the below steps to increase your chances of success:

        • Study: To prepare for an interview, cover all important topics, including-
          • Probability
          • Machine Learning
          • Statistics
          • Mathematics
          • Understanding neural networks
          • Statistical models
        • Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network with expertise in your field of interest or for expanding your professional connections.
        • Competitions: Many online competitions are available online and offline that help you to evaluate your understanding and knowledge, and give you an idea of real-world problems. 
        • Referral: Referrals gets you closer to your dream job. Having good connections with people holding reputable positions can get you a referral in their company. You must update your LinkedIn profile.

        • Interview: If you are confident enough with your skills, you should appear for interviews.

         The major roles & responsibilities of a Data Scientist include the following:

        • Their main focus is on data management, analytics modeling, and business analysis.
        • They conduct undirected research and frame open-ended industry questions for their companies.
        • They employ sophisticated analytics programs, algorithms, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling.
        • Explore and analyze data from a variety of angles to determine hidden information, weaknesses, trends and/or opportunities.
        • Invent new algorithms to solve problems and build new tools to automate work based on data available.
        • Recommend cost-effective changes to existing procedures and strategies.
        • Extract huge volumes of data from multiple internal and external sources also called data ingestion.

        The national average salary for a Data Analyst is $95,850 in Seattle, WA. A Data Scientist earns about $120,955 per year.

        The career path in the field of Data Science can be explained in the following ways:

        Business Intelligence Analyst: A business intelligence analyst develops and provides new business intelligence solutions. They may be tasked with defining, reporting on or otherwise developing new structures for business intelligence in ways that will serve a specific purpose. Report writing can be a significant component of this role. They ensure that the business is always in the best position to utilize its most valuable information in a manner that is conducive to its success. 

        Data Mining Engineer: A Data Mining Engineer performs the following responsibilities:

        • Generating derived datasets
        • Detecting and remediating production issues
        • Tracking data usage and data access performance
        • Creating data flow and transformation pipelines
        • Automating data reliability and quality checking.
        • Providing data access via databases and API services

        Data Architect: The responsibilities of Data Architect is to create database solutions, evaluating requirements, and preparing design reports.

        • Data warehousing
        • Elastic working and functioning.
        • Data cleaning
        • Data modeling
        • ETL working

        Data Scientist: The chief data officer is a senior executive responsible for the utilization and governance of data across the organization through data management, ensuring data quality and creating data strategy. The various roles include the following:

        • Advanced predictive algorithm
        • NoSQL
        • Data advanced algorithm
        • ETL Logic   
        • Big Data processing       

        Senior Data Scientist: The Senior Data Scientist oversees the activities of the junior data scientists and supervises and provides advanced expertise on statistical and mathematical concepts for the broader Data and Analytics department. 

        The following are the top associations and groups for Data Scientists in Seattle, WA:

        Referrals are the most effective way to get hired. Some of the other ways to network with data scientists in Seattle, WA are:

        • Attend workshops related to data science
        • Go to local meetups
        • Follow influential data scientists
        • Try out social platforms like Twitter and LinkedIn

        There are several career options for a data scientist – 

        1. Data Scientist
        2. Data/Analytics Manager
        3. Data Engineer
        4. Big Data Engineer
        5. Data Analyst
        6. Data Architect
        7. Business Intelligence Developer
        8. Marketing Analyst

        Some of the tools or software that are preferred over others, following the current trends of recruitment are as follows:

        • Programming languages like Python and R.
        • Platform like Hadoop
        • SQL
        • Machine Learning and related tools
        • Artificial Intelligence is not a must but a bonus in case you have command over it.
        • Data visualization tools.

        Data Science with Python Seattle, Washington

        Python is currently among the fastest-growing programming languages in the world. The reasons for it to be a preferred choice are as follows:

        • Python is a multi-paradigm programming language 
        • The inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. 
        • The huge number of dedicated analytical libraries and packages that are customized for use in data science are some of the main reasons why data scientists prefer the use of Python for Data Science projects, as opposed to any other programming language.
        • Python has enough scientific packages.
        • It is much easier to integrate with other applications like Hadoop-HDFS, Spark (spark now supports R in version 1.4), Apache Kafka (Apache Kafka), etc. to create better data pipelines.

        The 5 Most popular programming languages commonly used for Data Science are as follows:

        • R: R is a language for statistical analysis and graphics. Some benefits of using R are as follows:
          • It’s open source.
          • Instant access to over 7800 packages customized for various computation tasks.
          • Packages in R such as dplyr, tidyr, readr, data.table, SparkR, ggplot2 have made data manipulation, visualization, and computation much faster.
          • The style of coding is quite easy.
        • Python: Python has a dedicated library for data analysis and predictive modeling.
          • It is very easy to learn
          • Open Source
          • Number of third-party libraries
          • Strong community support.
        • SQL: SQL is a special-purpose programming language for managing and manipulating data held in relational database management systems.
          • It is easy to generate queries from a query
          • It is to load data into your database
          • Text mining
        • Java: Java is a general-purpose object-oriented programming language. Some of the uses of Java are as follows:
          • Machine learning and Deep learning
          • Data import and export.
          • Statistical analysis
          • Data visualization
          • Cleaning data.
        • Scala: Scala is a general-purpose programming language, which offers features of object-oriented and functional programming. It is a preferred language in data science domain due to the following advantages:
          • Amazing concurrency support, which is key in parallelizing a lot of the processing needed for large data sets
          • It also runs on the JVM, which makes it easier to use when paired with Hadoop

        Follow these steps to successfully install Python 3 on windows:

        • Download the Python 3 Installer: 

        Navigate to the Download page for Windows at python.org. Click on the link for the Latest Python 3 Release - Python 3.x.x. Scroll to the bottom and select either Windows x86-64 executable installer for 64-bit or Windows x86 executable installer for 32-bit.

        • Run the Installer:

        Simply run it by double-clicking on the downloaded file. Then just click Install Now. You must check the box that says Add Python 3.x to PATH as shown to ensure that the interpreter will be placed in your execution path.

        There are multiple ways to install Python 3, including a download from the official Python site, but it is recommended instead to use a package manager like Homebrew to manage all your dependencies going forward :

        • Confirm your Python version

        To check if Python 3 is already installed try running the command:

        python3 --version

        • Install Xcode and Homebrew

        run the following command to install Homebrew

        $ xcode-select --install

        Click through all the confirmation commands. Install Homebrew:

        /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

        run this command To confirm Homebrew installed correctly:

        $ brew doctor

        Your system is ready to brew.

        • Install Python 3

        run the following command To install the latest version of Python:

        $ brew install python3

        To confirm the version installed:

        $ python3 --version

        Python 3.7.0

        To open a Python 3 shell from the command line:

        $ python3

        reviews on our popular courses

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        I liked the way KnowledgeHut framed the course structure. The trainer was really helpful and completed the syllabus on time and also provided live examples.  KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

        Jules Furno

        Cloud Software and Network Engineer
        Attended Certified ScrumMaster®(CSM) workshop in May 2018
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        Everything was well organized. I would like to refer to some of their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.

        Steffen Grigoletto

        Senior Database Administrator
        Attended PMP® Certification workshop in May 2018
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        I would like to thank KnowledgeHut team for the overall experience. I loved our trainer so much. Trainers at KnowledgeHut are well experienced and really helpful completed the syllabus on time, also helped me with live examples.

        Elyssa Taber

        IT Manager.
        Attended Agile and Scrum workshop in May 2018
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        Knowledgehut is the best training institution which I believe. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask feedback every time and clear all the doubts.

        Issy Basseri

        Database Administrator
        Attended PMP® Certification workshop in May 2018
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        The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help and supported throughout the process.

        Astrid Corduas

        Telecommunications Specialist
        Attended Agile and Scrum workshop in May 2018
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        The trainer took a practical session which is supporting me in my daily work. I learned many things in that session with live examples.  The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

        Marta Fitts

        Network Engineer
        Attended PMP® Certification workshop in May 2018
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        I was totally surprised by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. Training session changed my way of life.

        Matteo Vanderlaan

        System Architect
        Attended Agile and Scrum workshop in May 2018
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        Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut also got good customer support to handle people like me.

        Sherm Rimbach

        Senior Network Architect
        Attended Certified ScrumMaster®(CSM) workshop in May 2018

        FAQs

        The Course

        Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

         Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

        • Get advanced knowledge of data science and how to use them in real life business
        • Understand the statistics and probability of Data science
        • Get an understanding of data collection, data mining and machine learning
        • Learn tools like Python

        By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

        Tools and Technologies used for this course are

        • Python
        • MS Excel

        There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

        Yes, KnowledgeHut offers virtual training.

        On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

        Your instructors are Python and data science experts who have years of industry experience. 

        Finance Related

        Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

        KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

        The Remote Experience

        In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

        Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

        Have More Questions?

        Mail Us support@knowledgehut.com

        Data Science with Python Certification Course in Seattle, WA

        A city that is enigmatically diverse in it culture, neighbourhood, aesthetics, and architecture: that?s Seattle for you. With logging being its major industry in the 19th century, the city progressed to ship building and was a major shipbuilding centre. But its fortunes changed with the development of the company Boeing that turned the place into a hub for aircraft manufacturing. Today, it is also home to major Fortune 500 companies including Microsoft and Amazon. Seattle also loves its coffee and is home to Starbucks, Tully?s and Seattle?s Best Coffee. The city has made inroads in other sectors as well such as retail, biotechnology, transport and trade. The city with its cultural heritage and modern dynamism is a great place to start a career and KnowledgeHut helps you along the way by offering courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.