April 2018


Register

Statistic and Beyond 4-Week Short Course: Pre-Requisite for Data Science

Tuesday, 6:00pm - 9:00pm EDT

Data Science

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Register

AI and Data Visualization: The Beauty and The Brains

Tuesday, 6:30pm - 9:30pm EDT

Data Science

Visualizing data helps us explore structure and relationships in data, and it provides a basis for communicating information. Machine Learning can be used to systematically comb through data and quantitatively identify patterns. Combining Al and ML with visual analytics can be especially powerful. Starting with AI / ML, we can reduce high dimensional data to important variables for visualization. Starting with visualizations and visual analytics, it’s possible to identify patterns that can subsequently be tested with rigorous ML methods. Further, AI can be used inside a visual analytics environment to suggest data shaping, variables to explore, and patterns in the underlying data. This presentation, including case studies and examples, explores this combination of AI and visual analytics methods with reference to TIBCO Spotfire, TIBCO Statistica, and data science environments such as R and Python. Some topics we will explore: 1) How AI can drive BI and visual analytics for rapid insights and data discovery 2) Recent advances in AI and Machine Learning, with visual analytics 3) TIBCO Connected Intelligence - sense, learn and act on your data with Spotfire, Statistica and StreamBase Schedule for the evening: 6:30pm: Doors open/Networking 7:00pm: Presentation by Michael O'Connell 8:00pm: Live Demo 8:30pm: Raffle Meet the Speaker: Michael O'Connell Michael O'Connell is Chief Analytics Officer at TIBCO Software, where he works with TIBCO customers and product teams to develop analytic solutions and provide input for product evolution. Michael has much experience in analytic applications across Financial Services, Energy, Life Sciences, Consumer Goods & Retail, and Telco, Media & Networks. His current passion is driving Insights to Action, combining visual and predictive analytics with event streams for optimizing business operations. Michael did his Ph.D. work in Statistics at North Carolina State University and remains Adjunct Professor Statistics in the department. Michael O'Connell is the Chief Analytics Officer at TIBCO Software, developing analytic solutions across a number of industries including Financial Services, Energy, Life Sciences, Consumer Goods & Retail, and Telco, Media & Networks. He has been working on statistical software applications for the past 20 years, and has published more than 50 papers and several software packages on statistical methods. Michael did his Ph.D. work in Statistics at North Carolina State University and is Adjunct Professor Statistics in the department. [Read: Forbes Interview, Information Management, Future Banking Article, European Banking Forum Article, Current Book: A Picture is Worth a Thousand Tables, Datanami Review of Big Data Webinar, Data Science Webinar, Energy Analytics Webinar, Event Analytics in Machine Management and IoT Webinar, Retail Banking Webinar] About our Sponsor Galvanize is the premiere dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following: Education - part-time and full-time training in web development, data science, and data engineering Workspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to support your company’s growth Networking - events in the tech industry happen constantly in our campuses, ranging from popular Meetups to multi-day international conferences To learn more about Galvanize, visit galvanize.com.

Visualizing data helps us explore structure and relationships in data, and it provides a basis for communicating information. Machine Learning can be used to systematically comb through data and quantitatively identify patterns. Combining Al and ML with visual analytics can be especially powerful. Starting with AI / ML, we can reduce high dimensional data to important variables for visualization. Starting with visualizations and visual analytics, it’s possible to identify patterns that can subsequently be tested with rigorous ML methods. Further, AI can be used inside a visual analytics environment to suggest data shaping, variables to explore, and patterns in the underlying data. This presentation, including case studies and examples, explores this combination of AI and visual analytics methods with reference to TIBCO Spotfire, TIBCO Statistica, and data science environments such as R and Python. Some topics we will explore: 1) How AI can drive BI and visual analytics for rapid insights and data discovery 2) Recent advances in AI and Machine Learning, with visual analytics 3) TIBCO Connected Intelligence - sense, learn and act on your data with Spotfire, Statistica and StreamBase Schedule for the evening: 6:30pm: Doors open/Networking 7:00pm: Presentation by Michael O'Connell 8:00pm: Live Demo 8:30pm: Raffle Meet the Speaker: Michael O'Connell Michael O'Connell is Chief Analytics Officer at TIBCO Software, where he works with TIBCO customers and product teams to develop analytic solutions and provide input for product evolution. Michael has much experience in analytic applications across Financial Services, Energy, Life Sciences, Consumer Goods & Retail, and Telco, Media & Networks. His current passion is driving Insights to Action, combining visual and predictive analytics with event streams for optimizing business operations. Michael did his Ph.D. work in Statistics at North Carolina State University and remains Adjunct Professor Statistics in the department. Michael O'Connell is the Chief Analytics Officer at TIBCO Software, developing analytic solutions across a number of industries including Financial Services, Energy, Life Sciences, Consumer Goods & Retail, and Telco, Media & Networks. He has been working on statistical software applications for the past 20 years, and has published more than 50 papers and several software packages on statistical methods. Michael did his Ph.D. work in Statistics at North Carolina State University and is Adjunct Professor Statistics in the department. [Read: Forbes Interview, Information Management, Future Banking Article, European Banking Forum Article, Current Book: A Picture is Worth a Thousand Tables, Datanami Review of Big Data Webinar, Data Science Webinar, Energy Analytics Webinar, Event Analytics in Machine Management and IoT Webinar, Retail Banking Webinar] About our Sponsor Galvanize is the premiere dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following: Education - part-time and full-time training in web development, data science, and data engineering Workspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to support your company’s growth Networking - events in the tech industry happen constantly in our campuses, ranging from popular Meetups to multi-day international conferences To learn more about Galvanize, visit galvanize.com.

Register

Introduction to Statistcal Learning

Tuesday, 7:00pm - 8:30pm EDT

Data Science

• What we'll doWe will (quickly!) provide an introduction to various classical methods of statistical learning including linear regression, classification (via logistic regression and support vector machines), decision trees, random forests, and boosting. • What to bring • Important to know

Introduction to Statistcal Learning

Tuesday, 7:00pm - 8:30pm EDT

West SoHo

Data Science

• What we'll doWe will (quickly!) provide an introduction to various classical methods of statistical learning including linear regression, classification (via logistic regression and support vector machines), decision trees, random forests, and boosting. • What to bring • Important to know

Register

Statistic and Beyond 4-Week Short Course: Pre-Requisite for Data Science

Thursday, 6:00pm - 9:00pm EDT

Data Science

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Register

How to Target Consumers at Every Stage of the Buying Cycle (SEMrush Workshop)

Thursday, 6:30pm - 9:00pm EDT

Web Development

DetailsCome join fellow MarTech NYC members for networking and a talk by SEMrush speaker Marcela De Vivo on "How to Target Consumers at Every Stage of the Buying Cycle" Consumers are saturated from seeing ads and content from all of their favorite brands. How can you pull in new consumers into your buying funnel, and nurture them from awareness to consideration to conversion? How do you nurture leads across channels without breaking the bank? In this workshop, we'll discuss strategies to add new consumers to your buying funnel inexpensively, and ways to nurture those leads using a variety of content types and channels. There will be chances to network before and after the talk. Refreshments will be served. Agenda:• Refreshments and Networking (6:30-7:15)• Workshop and Q&A (7:15-8:00)• More refreshments and Networking! (8:00-9:00) The location is Galvanize in West SoHo. Galvanize is the premiere dynamic learning community for technology. We thank them for the support.

DetailsCome join fellow MarTech NYC members for networking and a talk by SEMrush speaker Marcela De Vivo on "How to Target Consumers at Every Stage of the Buying Cycle" Consumers are saturated from seeing ads and content from all of their favorite brands. How can you pull in new consumers into your buying funnel, and nurture them from awareness to consideration to conversion? How do you nurture leads across channels without breaking the bank? In this workshop, we'll discuss strategies to add new consumers to your buying funnel inexpensively, and ways to nurture those leads using a variety of content types and channels. There will be chances to network before and after the talk. Refreshments will be served. Agenda:• Refreshments and Networking (6:30-7:15)• Workshop and Q&A (7:15-8:00)• More refreshments and Networking! (8:00-9:00) The location is Galvanize in West SoHo. Galvanize is the premiere dynamic learning community for technology. We thank them for the support.

May 2018


Register

Statistic and Beyond 4-Week Short Course: Pre-Requisite for Data Science

Tuesday, 6:00pm - 9:00pm EDT

Data Science

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Register

Statistic and Beyond 4-Week Short Course: Pre-Requisite for Data Science

Thursday, 6:00pm - 9:00pm EDT

Data Science

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Register

Statistic and Beyond 4-Week Short Course: Pre-Requisite for Data Science

Tuesday, 6:00pm - 9:00pm EDT

Data Science

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

Dates 4/24/18 - 5/23/18Every Tuesday and Thursday6pm - 9pmWhere are we going?Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:Supervised learning - learn a mapping from inputs xx to outputs yyUnsupervised learning - given only xx, learn interesting patterns in xxThese tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.Machine Learning versus StatisticsWhat is the difference between statistics and machine learning? Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:Statisticsutilizes confidence intervals, hypothesis tests, and optimal estimatorsplaces paramount importance on characterizing uncertainty in estimationbases methodological development on distribution and asymptotic theoryMachine Learningutilizes nonparametric and complex models harnessed via regularizationplaces paramount importance on “out of sample” generalizability/performancebases methodological development on empirical and computational techniquesObjectivesThe purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages.Descriptive statistics - mean, median, skewness…Inferential statistics - hypothesis testing, interval estimation…Predictive analytics - supervised learning: regression, classification…Prescriptive analytics - unsupervised learning, recommender systems… Course ContentsThe materials we cover dig into the basics, introducing the areas of probability and statistics that are assessed in the statistics and machine learning interview step of the Galvanize Data Science Immersive admission process. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline. And if you are interested in pursuing data science through the Galvanize Data Science Immersive, mastery of all this material will help make your Galvanize admission process – rather than a daunting scary prospect – a breeze.Here is what we will cover over the 4 weeks.Getting startedProbability ConceptsCombinatoricsProbabilityProbability DistributionsBayesian InferenceStatistics ConceptsStatistical InferenceRegression, Classification, EvaluationData Science ImmersivePrerequisites: Desire to learn.Setup: Bring your laptop and power cable. This course is priced at $399.00. If you are enrolled in the Python Fundementals course for Data Science, please reach out to your admissions representative at admissions.nyc@galvanize.com for a special discount code for this course.Interested in the Galvanize immersive program in data science? As part of the admissions process for the Data Science immersive program, there are two interviews: Python and statistics. These materials survey the areas of probability and statistics that will be covered in the statistics interview. In addition to the overview there are resources for further study that are meant to reinforce the most important topics.You can submit your application here. For more information visit the Admissions data science FAQ pageTuition Credit: 100% of this part time course payment can be used as tuition credit for our Data Science Immersive program. Candidates must enroll in an immersive that begins within 1 year of the completion of their part time course(s).About GalvanizeGalvanize is the premier dynamic learning community for technology. With campuses located in booming technology sectors throughout the country, Galvanize provides a community for each the following:Education - part-time and full-time training in web development, data science, and data engineeringWorkspace - whether you’re a freelancer, startup, or established business, we provide beautiful spaces with a community dedicated to supporting your company’s growthNetworking - events in the tech industry happen constantly on our campuses, ranging from popular Meetups to multi-day international conferencesTo learn more about Galvanize, visit galvanize.com.Questions? Please reach out to admissions.nyc@galvanize.com

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