Data Science Fellowships NYC 2017-11-09T14:04:13+00:00

Data Science Bootcamp Training and Data Science Fellowships

Data Science Fellowships & Data Science Bootcamp Training

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Data Science Bootcamp Training and Data Science Fellowships, 

Data Science Felllowships and Bootcamp Training,

Objective of this data science fellowships:-

The objective of this data science bootcamp is to provide an introduction to data science theory and practical applications.  The data science bootcamp focuses on fundamental theory. In this data science fellowships you will receive a full training. This data science fellowships guarantees you that you will receive all tools end theory needed to become data scientists from experts in the field.

This data science fellowships takes students through the fundamentals, giving them a solid foundation that they can build upon, then moves on to more advanced knowledge, teaching them how they can apply data science theory in practical situations.

By the end of the course, students will be able to make their own data science models. They will be taught how to create their own models using different languages and different types of methodologies (data science, machine learning, deep learning and so on).

Data science training includes the topics of machine learning, real world recommender system and deep learning, and so on.

An engineer (with data science training) needs to know about all these topics in order to become a data scientist.

TRAINING METHODOLOGY

In Class: $9,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 25th Nov 2017

Online: $4,999
Next Session: 25th Nov 2017

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Data Science Fellowships & Data Science Bootcamp Training

Instructor: John Doe, Lamar George

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Data Science Bootcamp Training and Data Science Fellowships

Data Science fellowships and data science Bootcamp Training and Data Science Fellowships

Data science

Data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics. Data science uses automated methods to analyze massive amounts of data and to extract knowledge from them. Using these automated methods turning up everywhere. Data science is creating new branches of science, and influencing areas of social science and the humanities. In industry, data science is transforming everything from healthcare to media.  An engineer with data science training needs to know about all these topics in order to become a data scientist.

Machine learning is popular due to the same factors that have made data mining and Bayesian analysis more popular than ever. Actually, the data volumes are growing and there are more data types available. The computational processing is cheaper and more powerful. And the data storage is affordable.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities  (or avoiding unknown risks).

An engineer with data science training needs to know about machine learning in order to become a data scientist.

The types of Machine learning algorithms:

Labeled data: Data consisting of a set of training examples, where each example is a pair consisting of an input and a desired output value

Classification: The goal is to predict discrete values (class)

Regression: The goal is to predict continuous values (numeric).

Types of machine learning Algorithms

There are some variations of how to define the types of Machine Learning Algorithms:

Supervised learning

Unsupervised Learning

Semi-supervised Learning

Reinforcement Learning

Supervised Learning

Supervised learning is when the class or label is known. An algorithm is run and it is used the function that best describes the input data.

Nearest Neighbor

Naive Bayes

Decision Trees

Linear Regression

Support Vector Machines (SVM)

Neural Networks

Unsupervised learning is when the computer is trained with unlabeled data.

Machine learning algorithms are used to find pattern detection and descriptive modeling. However, there are no output categories or labels here based on which the algorithm can try to model relationships. These algorithms try to use techniques on the input data to mine for rulesdetect patterns, and summarize, which help in deriving meaningful insights and describe the data better to the users.

In semi-supervised learning, either there are no labels for all the observation in the dataset or labels are present for all the observations. Semi-supervised learning falls in between these two. In many practical situations, the cost to label is high. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters.

This method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. An engineer with data science training need to know about deep learning in order to become a data scientist.

Deep learning uses neural networks, which consists of an input layer, hidden layer(s) and an output layers.

Deep learning has many variants and combinations. Furthermore. Many of these networks are combined together in stack or layers to give better results than one network alone.  For example: Multilayer Perceptrons (MLP), Autoencoders (AE), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and so on.

Engineers (with data science fellowships) are using data science for decision-making. For example, Engineers (with data science fellowships) use data science to solve customer queries based on transaction history. Furthermore, financial and portfolio management institutions deploy sentiment analysis and algorithms to consistently generate handsome returns for customers. Engineers (with data science fellowships) are using data science to make it effortlessly achieved. Using quantum advances in technology, Data Science for decision-making is a huge success in the data-driven world.

Engineers (with data science fellowships) are helping corporations in sectors such as banking, financial services and insurance, ecommerce, telecom, transportation and so on. These industries and engineers (with data science fellowships) process massive amount of data to leverage their true business potential.

Actually, engineers (with data science fellowships) are having a big success. Analytics revenue is pegged at $2 billion in FY17 whereas startup funding has crossed $700 million in the last two-and-a-half years alone. Engineers (with data science fellowships) have an infinite potential.

Actually, companies admitted that they haven’t been able to leverage its benefits due to the shortfall in talent and because of this dearth of highly skilled engineers (with data science fellowships). Indeed, engineers (with data science fellowships) will command huge salaries. Engineers with data science training are required.

The 80/20 data science dilemma

Most engineers (with data science fellowships) spend only 20% of their time on actual data analysis and 80% of their time finding, cleaning, and reorganizing huge amounts of data, which is an inefficient data strategy.

The Cloud technology has led to an explosion of data that has left engineers (with data science fellowships) in high demand.

Though growing in population, engineers (with data science fellowships) are so busy. The demand for engineers (with data science fellowships) and analysts is projected to grow by 28% by 2020. The demand for engineers (with data science training) is on top of the current market need. According to LinkedIn, there are more than 11,000 data scientists job opening in the US.

Many engineers (with data science fellowships) work for companies.

The reason engineers (with data science fellowships) are hired in the first place is to develop algorithms and build machine-learning models. In most companies today, 80% of a data scientist’s valuable time is spent simply finding, cleaning and reorganizing huge amounts of data.

Hard work behind the scenes of engineers (with data science fellowships)

Engineers (with data science fellowships) must identify relevant data sets within their data storage repositories (data lakes), which is a huge task.

A disadvantage of these data lakes is that data lakes have turned into dumping grounds, and engineers (with data science fellowships) don’t have an easy way to search for data and unclear strategies and policies around what data is safe to share more broadly. Engineers (with data science fellowships) often find themselves contacting different departments for the data they need and too much for this data to be delivered, and sometimes this data doesn’t provide the information that engineers (with data science fellowships) need, and sometimes data has serious quality issues. At the same time, responsibility for data governance (or data-sharing policies) often falls on engineers (with data science fellowships), since corporate-level governance policies can often be confusing, inconsistent, or difficult to enforce.

Even when engineers (with data science fellowships) can get the right data, engineers (with data science fellowships) need to time to explore and understand it. The data may be in a format that can’t be easily analyzed, and with little to no metadata to help, the engineers (with data science fellowships) may need to seek advice from the data owner. After all this, engineers (with data science fellowships) need to prepare the data for analysis. Engineers (with data science fellowships) have different tasks such as formatting, cleaning and sampling the data. Engineers (with data science fellowships) do other tasks such as scaling, decomposition and aggregation transformations, which are required before engineers (with data science fellowships) are ready to start training the models.

Engineers (with data science fellowships) and developers traditionally work alone. This creates bottlenecks, increases the errors and dries up resources. Engineers (with data science fellowships) and developers working together could use cloud platforms to be more efficient and to help engineers (with data science fellowships) to work together.

These processes can be time-consuming and tedious, but they are very important. Engineers (with data science fellowships) can find models that generally improve as they are exposed to increasing amounts of data. Engineers (with data science fellowships) have to decide to include as much data as they can in their analysis in order to have better models.

Engineers (with data science fellowships) when don’t have enough time, they are tempted to make compromises on the used data, aiming for “good enough” rather than optimal results.

Engineers (with data science fellowships) can make hasty decisions during model development that can lead to widely different outputs and potentially render a model unusable for production Engineers (with data science fellowships.) They are constantly making judgment calls, and starting out with incomplete data can easily lead them down the wrong path.

Engineers (with data science fellowships) are generally forced to focus on one model at a time in order to balance quality against time constraints. Engineers (with data science fellowships) are forced to start all over again when something goes wrong. Engineers (with data science fellowships) are obliged to double down on every hand, turning data science into a high-stakes game of chance.

Engineers (with data science fellowships) use cloud data services to automate many of the tedious processes associated with finding. Engineers (with data science fellowships) clean data to have more time for analysis, without compromising the quality of the data they use.

A solid cloud data platform is used to help engineers (with data science fellowships) find the data they need. Integrated data governance tools also give engineers (with data science fellowships) confidence that they are permitted to use a given data set.

As a result, engineers (with data science fellowships) gain the time they need to build and train multiple models simultaneously. This is a great advantage of parallel experimentation that yields breakthroughs without focusing resources on a single approach that may turn out to be a dead end.

Engineers (with data science training) use cloud platforms to save, access and extend models, enabling them to use existing assets as templates for new projects instead of starting from scratch every time, which is called “transfer learning”. Preserving knowledge and apply model in related problems is used by Engineers (with data science fellowships). Engineers (with data science fellowships) use these models for saving time and reducing risk.

Engineers (with data science fellowships) play an essential role in pushing forward innovation and garnering competitive advantage for companies Engineers (with data science fellowships) use cloud platform tools to have better results in less time.

The Data Science Product Manager – New Roles For The New Business Reality

Engineers (with data science fellowships) have a great opportunity because new markets are emerging around data science. Data Science Product Manager is the most important role, which is the link between research and ROI. Facebook companies have startups that have heavy emphasis on results (productization) for their data science efforts. This company wants Engineers (with data science training) to work.

Engineers (with data science fellowships) and machine learning engineers are very promising.

Dataiku and data science market

The company Dataiku Inc., which manufactures Dataiku Data Science Studio (DSS), providing end-to-end advanced analytics and collaborative data science tools, has announced plans to strengthening its platform with new technologies and more engineers (with data science fellowships).

Battery Ventures, FirstMark, Serena Capital and Alven are investing $28 million. This level of investment is reflective of an increased demand by firms, across a broad range of industries, to maximize their data production and analysis. The Dataiku platform allows engineers (with data science fellowships), programmers, and analysts to collaborate more effectively both within and across organizations.

Many businesses are investing in data science and data science bootcamps. Actually, big data is increasing and methods for data collection are improved. Data science is about leveraging the information that has been collected and using analytics to use this data in more effective ways. The process is multifaceted and it includes technology, mathematics, and human insight.

Dataiku was founded in 2014, and it offers collaborative data science systems to global companies. Dataiku is a company for selecting the right data science tool for companies such as L’Oreal, NPR, AXA, and Kuka.

In these types of businesses, engineers (with data science fellowships) are using data to find and interpret rich data sources. Engineers (with data science fellowships) have task such as managing of large amounts of data, merging data sources, ensuring consistency of datasets, creating visualizations to help interpret data, building mathematical models, and communicating out data insights.

Battery Ventures said data science has become mission-critical to all types of businesses. The problem is that there are still a limited number of people with true technical, data science capabilities.

Engineers (with data science fellowships) are using Dataiku software to manipulate the dataset, which is a series of records with the same schema. Dataiku will hire 100 new Engineers (with data science fellowships) and accelerate the development of new features within their platform. The company aims to further drive through changes as it positions itself as a disruptive force in data analytics.

IBM’s Watson Data Platform aims to become data science operating system

IBM’s plan is to create a data science operating system that can bring together Engineers (with data science fellowships), analysts, and business leaders.

IBM is aiming to make Watson Data Platform a system for data science in the months ahead as it compiles various functions and parts and combines them into a cloud-based effort to make data more consumable.

Engineers (with data science training) use Watson Data Platform, which is a collection of services used to prepare, store, ingest, and analyze data and then allow customers to build applications on top of it.

Watson’s potential as a data operating system is highlighted in IBM’s Data Science Experience. Engineers (with data science fellowships) use this platform, which is a collection of tools designed to provide insights.

Engineers (with data science fellowships) use Watson Data Platform, which has been adding new functionality around cataloguing data. Actually, IBM can move data into Watson Data Platform and make it consumable the faster it can be a data science operating system.

IBM wants to be the company that best helps businesses benefit from their data.

Data science today is an aggregation of parts and roles and piecemeal applications.

Engineers (with data science fellowships) use data science and a combination of disciplines ranging from business intelligence and analysis to data science to mathematics and statistics.

Simplifying the moving technology parts of data science will enable business leaders to more easily transform their businesses.

IBM wants to make Watson Data Platform a dynamic catalog that can be modeled and shared across all teams and functions. IBM created a data science operating system and wants to play a big role in artificial intelligence.

IBM has a cloud model that allows customers to buy functions and specific services. IBM customers already have applications such as Cloudant, SPSS, and Cognos. Packaging, pricing, and look and feel of the Watson Data Platform will be critical. Engineers (with data science fellowships) use these applications.

IBM has all of the key parts. Actually, IBM is a leader in data science platforms and noted strengths included a strong customer base, commitment to open source technologies, strong model management, and governance and support for a broad range of data types. Engineers (with data science fellowships) uses IBM software.

IBM’s data science platform hurdles include overcoming confusion about its product offerings, confusing roadmaps, and customer support. Actually, IBM’s Data Science Experience (DSx) could address shortcomings.

Gartner Data Science Platform and IBM have as a good chance to form a data science operating system as anyone. Engineers (with data science fellowships) can use this platform.

Data science is a booming academic discipline, which is becoming big business. Data science bootcamp are becoming a big business.  It involves the collecting, organizing and interpreting of large sets of digital information and has the potential to transform public and private organizations.

The data science technologies and services behind it could be worth $2.7 trillion globally by 2020, according to the International Data Corporation. It is a potential driver of economic growth as a new cluster of companies emerge to exploit its potential to help firms improve their operations, understand how their own technology works and provide early warning of imminent problems.

EDF Energy was an early adopter of Data Science technologies. SSE is using it to predict faults, and thereby get its repair teams more quickly to locations where they know problems are likely to occur.

Machine learning technologies and new techniques are creating new market for data science. It is a huge opportunity to become data scientist. A company called Open Data Group is aiming to close that gap with a Docker-based model deployment framework.

Open Data Group’s CTO Stu Baily describes the company’s FastScore framework as an abstraction layer that makes it easier for enterprise IT professionals to deploy data science models into production environments. Engineers with data science training use this framework.

This company is focused on bridging analytic professionals, Engineers with data science training, quants, model builders, analytic engineers. Their solution focuses on getting models deployed as durable, cloud portable assets that will have a very long time, but can be easily changed, easily migrated. This solution is used by engineers with data science training.

FastScore can use any language or environments the analytic model is developed in. FastScore supports models developed in Python, R, SAS, H2O.ai, and Juypter and Apache Zeppelin data science notebook, among others. Once the model is created and logged in its Avro schema, FastScore can transform it into a Docker microservice that can be called with a REST API, which IT professionals are familiar with. Engineers with data science training can use any language with this framework.

The aim of this framework is the integration of model building tools, but their focus is building a very clear abstraction for handing off models from the data lab or the data science process into pre-production and production environments. Engineers with data science training can use any model with this framework.

The company is just as neutral when it comes to production environments as it is for data science development environments. Users can use whatever scheduling system they want, including Kubernetes, DC/OS, or CloudFoundry, while it supports data stores like S3, HDFS, and relational databases. Engineers with data science training can use this framework with any scheduling system.

Open Data Group wants to have their engineers with data science training together and that they get involve with the day-to-day management of machine learning models or data science models.

Open Data Group aims to streamline production deployment of analytic models with its FastScore framework

Open Data Group recently dealt with a large manufacturer that was struggling to get machine learning models (or data science models) into production.

Open Data Group also tends to the daily care and feeding of the models, which it refers to as AnalyticsOps. Their engineers with data science training offers hooks into code repositories like GitHub, model management functionality, and AB testing capabilities for comparing the effectiveness of models.

Their engineers with data science training have a simple set of abstractions, a very consumable technology stack that really makes the data science much more productive, but it’s built like IT would expect it to be built, in a modular way that’s future proof for their own journey. And then if engineers with data science training want to move their models to Google, then it would be easy. And engineers with data science training should have a very high degree of confidence that all the math is absolutely going to be the same.”

Open Data Group has been in the machine learning and applied statistics business for 18 years, and has quite a bit of experience in helping customers get real benefits out of their data.

Open Data Group’s CEO said that the timing is right for companies to get more serious about data science and how they and their engineers with data science training can use it to improve their business.

Actually, there is a transformation of large portions of the economy and industrial sector with data science in a somewhat analogous way to how computer science really started to have a huge impact in the 80s and 90s.

Bigdataguys has organized courses to help developers (or any person that wants to become data scientist) gain a greater understanding of data science. This course gives you excellent opportunities in the job market. These classes aim to bring students up to speed on data science, as well as give them practical skills that will help their careers about data science, machine learning, deep learning, and so on.

Data science bootcamp of Bigdaaguys offers qualify courses in data science to become data scientist. The best way to learn about data science is to take a course with us. Data science training covers the basic theory and practical examples.

The typical Google Data Scientist salary is $152,417. Data Scientist salaries at Google can range from $103,000 – $195,000.

CURRICULUM

Lecture1.1 Introduction to Python
Lecture1.2 Complete Syntax
Lecture1.3 Data Structures
Lecture1.4 Object oriented programming and introduction to the functional paradigm
Lecture1.5 Python idioms and things to look out for Tooling
Lecture1.6 How to use a Python IDE and text editors effectively
Lecture1.7 Basic command line tricks
Lecture1.8 Learn how to use git and some git workflows
Lecture1.9 Learn the ropes of the Python ecosystem
Lecture1.10 Virtual environments for package isolation
Lecture1.11 Build distribution packages and learn about different ways of distributing code
Lecture1.12 Writing tests
Lecture1.13 Web APIs
Lecture1.14 Design an API considering use cases in advance
Lecture1.15 Write a simple API and produce self-documentation for it
Lecture1.16 Write tests for APIs

Lecture2.1 Regression: cost functions (average squared error and friends; when to pick a non-standard cost function)
Lecture2.2 Random forests
Lecture2.3 Ensembles
Lecture2.4 SVMs
Lecture2.5 Basic idea
Lecture2.6 Kernels: understanding the basic types
Lecture2.7 Model comparison
Lecture2.8 Categorization: ROC curves
Lecture2.9 Common misconceptions, Common ways to optimize them, Best Use Cases

Once your company starts fitting models, methodology matters. It is easy to simply pile up complexity without managing it. Fortunately, we now have best practices (and libraries) that make it easy to iterate over preprocessing, model families, and parameters.
Lecture3.1 How to weight, transform, combine, or drop features
Lecture3.2 How to represent transformations, models, parameters, and the results of a run, so they can be easily managed
Lecture3.3 What feature transformations add the most performance, and how they interact with the rest of the pipeline

Lecture4.1 Preprocessing with Pandas
Lecture4.2 Reading data
Lecture4.3 Selecting columns and rows
Lecture4.4 Filtering
Lecture4.5 Vectorized string operations
Lecture4.6 Missing values
Lecture4.7 Handling time
Lecture4.8 Time series
Lecture4.9 Numpy, Spicy
Lecture4.10 Arrays
Lecture4.11 Indexing, Slicing and Iterating
Lecture4.12 Reshaping
Lecture4.13 Shallow vs deep copy
Lecture4.14 Broadcasting
Lecture4.15 Indexing (advanced)
Lecture4.16 Matrices
Lecture4.17 Matrix decompositions
Lecture4.18 Scikit-learn
Lecture4.19 Feature extraction
Lecture4.20 Classification
Lecture4.21 Regression
Lecture4.22 Clustering
Lecture4.23 Dimension reduction
Lecture4.24 Model selection

Lecture5.1 Kafka
Lecture5.2 Setup and configuration
Lecture5.3 Topics, partitions
Lecture5.4 API
Lecture5.5 Connecting to Spark
Lecture5.6 Elasticsearch
Lecture5.7 Setup
Lecture5.8 API
Lecture5.9 Kibana
Lecture5.10 Marvel Plugin
Lecture5.11 Real-time Data Pipeline
Lecture5.12 Twitter API
Lecture5.13 Spark streaming

Recommendations are often used in many industries, such as e-commerce, jobs, music, and social media. This course goes beyond the basics and emphasizes solutions to problems you will face when your business deploys a recommender system.
Lecture6.1 How recommenders work, using both content-based and collaborative filtering techniques.
Lecture6.2 How to build recommenders that scale. On platforms where both the number of users and/or items (such as movies, or products, or job openings) may be very large — i.e. in the millions — thinking about scaling is essential.
Lecture6.3 How to factor in business concerns: e.g. pricing, inventory, seasonality, new items, new users, popular products, serendipity, coverage, etc.
Lecture6.4 How to tune and evaluate a recommender.
Lecture6.5 How to generate recommendations in real-time.

Lecture7.1 R basic data types
Lecture7.2 Atomic vectors and operations on them
Lecture7.3 Lists
Lecture7.4 R as a functional programming language
Lecture7.5 Object attributes and object oriented programming
Lecture7.6 R complex data types (matrices, factors, and data frames)
Lecture7.7 Unit testing
Lecture7.8 Debugging, and code profiling
Lecture7.9 Writing R packages
Lecture7.10 String processing, dates, regular expressions (using the stringi package)
Lecture7.11 Speeding up operations on multiple files
Lecture7.12 Dynamic report generation with knitr

Lecture8.1 What are Rcpp and Cython. Why C++ for data science?
Lecture8.2 C++ introduction: scalar data types, controlling program flow
Lecture8.3 Accessing R vectors through Rcpp
Lecture8.4 Lists and R functions
Lecture8.5 C++ Standard Library – fundamental data structures and algorithms
Lecture8.6 Introduction to Cython, linking C++ libraries to Cython; accessing NumPy objects
Lecture8.7 OpenMP – multi threaded C++ made simple

Lecture9.1 Aggregations (split-apply-combine type operations)
Lecture9.2 Add/update/delete columns without any unnecessary copies (by reference)
Lecture9.3 File reader (fread)
Lecture9.4 Ordered and rolling joints
Lecture9.5 Overlapping range/interval joins
Lecture9.6 Reshaping etc.

Lecture10.1 Background on neural nets, history, performance bottlenecks
Lecture10.2 Training deep nets
Lecture10.3 Regularization (dropout)
Lecture10.4 Interpreting weights on a hidden layer

Online: $4,999
Next Batch: starts from 25th Nov 2017

In Class: $9,999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 25th Nov 2017

COURSE HIGHLIGHTS

Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming

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FAQ'S

Advanced Course like Data Science Bootcamp Training duration largely depends on trainee requirements, it is always recommended to consult one of our advisors for specific course duration.

We record each LIVE class session you undergo through and we will share the recordings of each session/class.

If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers.

You will work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready.

Our Trainers will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.

Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.

The Data Science Bootcamp Training by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential.

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John Doe
Learning Scientist & Master Trainer 
John Doe has been a professional educator
for the past 20 years. He’s taught, tutored,
and coached over 1000 students, and he
holds degrees in Physics and Literature
from Northwestern University. He has
spent the last 4 years studying how
people learn to code and develop applications.

Lamar George
Learning Scientist & Master Trainer 
He has been a professional educator for
the past 20 years. He’s taught, tutored,
and coached over 1000 students, and
he holds degrees in Physics and Literature
from Northwestern University. He has
spentthe last 4 years studying how
people learn to code and develop applications.

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