Data Science Fellowships NYC 2017-08-01T23:14:04+00:00

Data Science Bootcamp Training and Data Science Fellowships

Data Science Fellowships & Data Science Bootcamp Training

Hadoop Training, Data Engineering, IOT Products, Oracle Fusion

Data Science Bootcamp Training and Data Science Fellowships, 

Data Science Felllowships and Bootcamp Training,

This is a Fully immersive Data Science Fellowships &  Data Science Bootcamp Training in NYC, D.C, Bay Area and online 

About: Data Science Fellowships and Data Science Bootcamp Training and placement Fellowships In Data Science Fellowships and  Bootcamp Training our passion is teaching our vast experience in cutting-edge technology on machine learning, data science, and big data engineering.

We’ve dug deep into this tech territory that is why we know what works and what’s outdated. Our live in-class teaching affords you to learn rapidly in small-class sizes, with market-tested mentors, and materials that are market-synched.

Our small classes are primarily hands-on taught by senior data scientists and senior data engineers with extensive years of in-service experience.

TRAINING METHODOLOGY

In Class: $9,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 15th Jul 2017

Online: $4,999
Next Session: 15th Jul 2017

Home / All courses / Data Science / Data Science Fellowships & Bootcamp Training

 

Data Science Fellowships & Data Science Bootcamp Training

Instructor: John Doe, Lamar George

DESCRIPTION

Data Science Bootcamp Training and Data Science Fellowships

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

Data Science Fellowships and  Bootcamp Training: Become data scientist Fellowships available (Portfolio project), Fellowships

What we will deliver at the end of the course is the portfolio project where you will create this on your own utilizing your own inputs crafted with market-ready tools and relevant materials. Think of this project as your demo that will launch your career as a Data Scientist.

Think of this project as your demo that will launch your career as a Data Scientist.

Bootstrap your career with fully immersive Data Science Fellowships and  Bootcamp Training and  Fellowships – The Course Proper

The duration of the data science Fellowships & bootcamp training has been optimized and made lean so that students will be accorded the highest quality mentoring exciting immersion programs and real-job simulated platforms that will definitely make the course graduate highly demanded in the market.

Learn easily R & Python, Numpy, Pandas, and sci-kit-learn. What’s more, deep dive into Spark, Kafka, Repp, and Cython Explore these exhilarating world of data science and maximize data sets and memory usage and make them work for you! Data Science Fellowships are also available

Is this Data Science right fit for me?

If you have some basic knowledge of mathematics, statistics, and algebra coupled with a basic appreciation of machine learning, look no further! You will also want to have at least 1,000 hours of programming background even in a language that is not “data science friendly”. Programming will be the main thrust of this course using linear algebra and probability theory.

Research Data Science fellowships and  Bootcamp Training: Once you’ve learned the basics, a Data Science fellowships and Bootcamp Training can help you fill any gaps in your knowledge and get you ready for an entry-level data science job.

data science bootcamp training that teaches not just Python but also R, Hadoop, Spark and more Curriculum drawn from data science bootcamp training engagement with corporate consulting and training, hiring partners and active industry participation. Create a personal portfolio with 5 projects to showcase your skills and knowledge. In this program, students will learn beginner and intermediate levels of Data Science with R, Python and Hadoop as well as the most popular and useful packages like -learn, and more. Once the foundation of learning has been set, students work on projects throughout the boot camp. Along the way, students will have assistance in preparing for the job search through resume review and interview preparation.Much of the material in this track is similar to what we teach in our retreat for data engineers, but the emphasis is on finding answers to perennial questions about data science with cutting-edge tools toward producing the best performing model. You will also be primed up for communications to different audiences both in writing and in front of large audiences Accredited. Immersive.In-Person. Career Support.This full-time, 12-week data science experience hones and contextualizes the skills brought in by our competitive student cohorts. Incorporating traditional in-class instruction in theory and technique, students use real data to build a five-project portfolio to present to potential employers and have access to full career support throughout and after the bootcamp.

What Data Scientists Do

Data scientists utilize their knowledge of statistics and modeling to convert data into actionable insights about everything from product development to customer retention to new business opportunities.

Skills Needed To Succeed

Data scientists also possess a unique combination of technical, analytical, and presentation skills, making them hard to find.

They understand statistics and applied mathematics. They can test hypotheses with experiments they design. They know enough programming to engineering methods for sourcing, processing, and storing their data. And they communicate their findings through data visualizations and stories.

They know enough programming to engineering methods for sourcing, processing, and storing their data. And they communicate their findings through data visualizations and stories.

Some of the languages and applications they use are SQL, R, Python, SPSS, Tableau, and Hadoop.

The Life of a Data Scientist Fellowships
Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges. Fellowships available!

Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges. Fellowships available!

Data Scientist is one of the hottest jobs of this decade. fellowships.The demand for data scientists is much higher than available candidates. So, there is a lot of incentive for people to look up to data science as a career option, and that is not going to change in near future.

However, if you do one search on Google, you will see your dream vanishing. There are too many resources, advice and paths suggested by various people, which makes it impossible for a beginner to take right decisions.

If you are facing a similar problem, let’s accomplish this in 2016. If you aspire to become a data scientist, this annual plan would make things much easier and faster for you. I’ve mentioned only the best resources you should follow. This plan is designed to make you a data scientist by December 2016 (conservative pace). If you can devote more time, great. You’d achieve this feat much faster or with more depth by looking at additional resources. Fellowships Available!

If you aspire to become a data scientist, this annual plan would make things much easier and faster for you. I’ve mentioned only the best resources you should follow. This plan is designed to make you a data scientist by December 2016 (conservative pace). If you can devote more time, great. You’d achieve this feat much faster or with more depth by looking at additional resources. Fellowships Available!

This plan is designed to make you a data scientist by December 2016 (conservative pace). If you can devote more time, great. You’d achieve this feat much faster or with more depth by looking at additional resources. Fellowships Available!

Become a Data Scientist Fellowships:

Contribute to smarter decisions and better results. Data science is one of the today’s fastest-growing fields, with career opportunities in every industry.

According to glassdoor, data scientists earn an average base salary of $105,395 and were among the most in-demand and highest-paid professionals of 2015. Fellowships available

CURRICULUM

WEEK 1-GOOD ENGINEERING PRACTICES IN PYTHON, CREATING AND CONSUMING API’S
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
WEEK 2- MACHINE LEARNING OVERVIEW: PROFICIENCY WITH CORE METHODS
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
WEEK 3- ADVANCED MACHINE LEARNING: MODEL PIPELINES
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
WEEK 4- NUMPY, SPICY, PANDAS, AND SCIKIT- LEARN
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
WEEK 5-REAL TIME STREAM PROCESSING WITH SPARK, KAFKA, AND ELASTIC SEARCH
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
WEEK 6-REAL WORLD RECOMMENDER SYSTEM
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.
WEEK 7- DEEP DIVE INTO R
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
WEEK 8- SPEEDING UP R AND PYTHON MODELS: RCPP AND CYTHON
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
WEEK 9-OPTIMIZING DATA STRUCTURES AND MEMORY USAGE: ADVANCED DATA TABLE
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.
WEEK 10-DEEP LEARNING FOR IMAGE CLASSIFICATION
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 17th July 2017

In Class: $9,999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 17th July 2017

INSTRUCTORS

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 spent the last 4 years studying how people learn to code and develop applications.

COURSE HIGHLIGHTS

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

SCHEDULE YOUR FREE DEMO

TALK TO US

NEED CUSTOM TRAINING FOR YOUR CORPORATE TEAM?

NEED HELP? MESSAGE US

SOME COURSES YOU MAY LIKE

data science Bootcamp
Deep Learning with Tensor Flow In-Class or Online

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
Duration:
 
50 hours
Lectures:  25

Neural Networks Fundamentals using Tensor Flow as Example Training (In-Class or Online) 

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
Duration:
 
50 hours
Lectures:  25

Deep learning tutorial

Tensor Flow for Image Recognition Bootcamp (In-Class and Online)

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
Duration:
 
50 hours
Lectures:  25

OUR PRODUCTS

SOME OTHER COURSES YOU MAY LIKE

FAQ'S

What do I need to know before taking this Data Science Course?

A basic understanding of Python and modeling.
Familiarity with matrices and linear algebra.

Does Tensor Flow work with Python 3?

As of the 0.6.0 release time frame (Early December 2015), it does support Python 3.3+.

REVIEWS

Data Science Bootcamp Training and Data Science Fellowships
Data Science Bootcamp Training and Data Science Fellowships

Data Science Bootcamp Training and Data Science Fellowships, Data Science Bootcamp Training and Data Science Fellowships, Data Science Bootcamp Training and Data Science Fellowships, 

STUDENTS WHO VIEWED DATA SCIENCE COURSE ALSO VIEWED

[thim-courses limit=”4″ featured=”” order=”latest”]