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

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.

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: 
  • 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

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%).

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. 

• .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

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

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:

• 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.

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

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

• Build Python programs: distribution, user-defined functions, importing datasets and more
• Manipulate and analyse data using Pandas library
• Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
• Distribution of data: variance, standard deviation, interquartile range
• Calculating conditional probability via Hypothesis Testing
• Analysis of Variance (ANOVA)
• Building linear regression models, evaluating model parameters, and measuring performance metrics
• Using Dimensionality Reduction Technique
• Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
• Building KNN algorithm models to find the optimum value of K
• Building Decision Tree models for both regression and classification problems
• Build Python programs: distribution, user-defined functions, importing datasets and more
• Manipulate and analyse data using Pandas library
• Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
• Build data distribution models: variance, standard deviation, interquartile range
• Calculate conditional probability via Hypothesis Testing
• Perform analysis of variance (ANOVA)
• Build linear regression models, evaluate model parameters, and measure performance metrics
• Use Dimensionality Reduction
• Build Logistic Regression models, evaluate model parameters, and measure performance metrics
• Perform K-means Clustering and Hierarchical Clustering
• Build KNN algorithm models to find the optimum value of K
• Build Decision Tree models for both regression and classification problems
• Build data visualization models for Time Series data and components
• Perform exponential smoothing

Our program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the data science with Python course covers everything you need to know, whether you’re a novice or an expert.

Yes, our applied 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. This format is convenient when compared to other Data Science with Python courses.

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 practical Data Science with Python certification course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

Below are the technical skills that you need if you want to become a data scientist.

• Mathematics - You don't need to have a Ph.D. in math but it is important to have a basic knowledge of linear algebra, algorithms, and statistics.
• Machine Learning – Stand out from other data scientists by learning ML techniques, such as logistic regression, decision trees, supervised machine learning, etc. These skills will help in solving different data science problems.
• Coding – In order to analyze the data, the data scientist must know how to manipulate codes. Python is one of the most popular and easy languages.

Other important skills are –

• Software engineering skills (e.g. distributed computing, algorithms and data structures)
• Data mining
• Data cleaning and munging
• Data visualization (e.g. ggplot and d3.js) and reporting techniques
• Unstructured data techniques
• R and/or SAS languages
• SQL databases and database querying languages
• Big data platforms like Hadoop, Hive, and Pig 
• Proficiency in Deep Learning Frameworks: TensorFlow, Keras, Pytorch
• Cloud tools like Amazon S3 

We have listed down all the essential Data Science Skills required for Data Science enthusiasts to start their career in Data Science

Apart from these Data Scientists are also required to have the following business skills:

• Analytic Problem-Solving – In order to find a solution, it is important to first understand and analyze what the problem is. To do that, a clear perspective and awareness of the right strategies are needed.
• Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists.
• Intellectual Curiosity -  If you are not curious enough to get an answer to that "why", then data science is not for you. It’s the combination of curiosity and thirst to deliver results that offers great value to a commercial enterprise.
• Industry Knowledge – Last, but not least, this is perhaps one of the most important skills. Having solid industry knowledge will give you a more clear idea of what needs attention and what needs to be ignored. 

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 Data Science with Python certification 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.

Below is the roadmap to becoming a data scientist:

• Getting Started: Choose a programming language in which you are comfortable. We suggest Python as a suitable programming language.
• Mathematics and Statistics: The science in Data Science is all about dealing with the data (maybe numerical, textual or an image), making patterns and relationships between them. You must have a good understanding of basic algebra and statistics.
• Data Visualization: One of the most important steps in this learning path is the visualization of data. You must make it as simple as possible so that the other non-technical teams are able to grasp its contents as well. It is important to learn data visualization to communicate better with the end-users.
• ML and Deep Learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist as it is through deep learning and ML techniques that you will be able to analyze the data given to you. 

Data Science is one of the emerging fields in terms of its scope to business and job opportunities. Python is one of the most popular programming languages and has become the language of choice for Data Scientists. Learning Python with Data Science puts you in a favourable position to be hired as a skilled data scientist.

The Data Science with Python workshop at KnowledgeHut is delivered through our LMS.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the 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 our LMS to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our 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. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from our LMS 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.

We at KnowledgeHut, conduct Data Science with Python courses in all the cities across the globe, and here are a few listed for your reference:

With our 100% Satisfaction Guarantee policy, you are assured of the highest standards of quality. At KnowledgeHut, we strive to ensure that your learning experience with us is beyond reproach. However, if for any reason your expectations are not met, we will refund the Data Science with Python course fee in accordance with our 100% Satisfaction Guarantee policy.

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 Data Science with Python 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.