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

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

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

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

• Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
• 4 GB RAM
• 10 GB of free space

Software Requirements

• Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

• 32 or 64-bit Operating System
• 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

Yes, you can switch the start date to your Data Science with Python training, with prior notice of at least 24 hrs and subject to availability in the desired batch.

Yes, we have a 100% money-back guarantee in line with our 100% Satisfaction Guarantee policy.

Yes, scholarships are available for students and veterans with grants up to 50% of the course fees.

To avail scholarships for our Data Science with Python course, please get in touch with us at support@knowledgehut.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, our panel of experts take a decision on the Grant. The entire process could take about 7 to 15 days.

Yes, group discounts are available and apply to groups as small as three (3) participants. The more participants that attend a training course, the greater the discount. By registering in groups, you can typically save up 20% to 30% on the course fee. For more details, please check out upcoming schedules.

Yes, instalment options are available for payment of course fee. To avail the instalment option, please get in touch with us at support@knowledgehut.com. The team will explain how the instalments work and provide timelines for your case.

Typically, the number of instalments vary from 2 to 3, but the full amount must be paid before you complete the course.

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.

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: 
  • FiveThirtyEight
  • BuzzFeed
  • ProPublica
  • Socrata OpenData
  • AWS Public Data sets
• 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:
  • Kaggle
  • CrowdAI
  • Codalab
  • Innocentive
  • Driven Data

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%).

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. 

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

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:

• Seattle women in Data Science
• Data Science Dojo
• Metis: Seattle Data Science
• Seattle Data Geeks
• Data Brigade

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.

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