Data Science bootcamp & Online 2017-07-18T23:01:42+00:00
$9,999.00

274 students enrolled

This is a Fully immersive Data Science bootcamp in  NYC, D.C, Bay Area and online Fellowships Data Science bootcamp online, data science training and placement, data analytics training and placement guaranteed, Bigdata training and placement, DataScience Fellowships for Phd candidates, Free Data Science Bootcamp, Online data science bootcamp, part time data science bootcamp, become data scientAbout : Data Science bootcamp  and placement Fellowships

In Data Science bootcamp 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 We’ve dug deep into this tech territory that is why we know what works and what’s outdated.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.

Data Science bootcamp Deliverable: 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.

Bootstrap your career with  fully immersive Data Science bootcamp workshop Fellowships – The Course Proper

The duration of the course 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 Scikit-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! Fellowships available?

Is this right fit for me?

If you have some basic knowledge of mathematics, statistics, and algebra coupled with 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.

data science bootcamp that teaches not just Python but also R, Hadoop, Spark and more Curriculum drawn from data science 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 & Hadoop as well as the most popular and useful packages like dplyr, scikit-learn, and more. Once the foundation of learning has been set, students work on projects throughout the bootcamp. 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 to 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 engineer 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!

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 could 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 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

  • Week 1-Good Engineering practices in python, creating and consuming API's  0/16

    • Introduction to Python
    • Complete Syntax
    • Data Structures
    • Object oriented programming and introduction to the functional paradigm
    • Python idioms and things to look out for Tooling
    • How to use a Python IDE and text editors effectively
    • Basic command line tricks
    • Learn how to use git and some git workflows
    • Learn the ropes of the Python ecosystem
    • Virtual environments for package isolation
    • Build distribution packages and learn about different ways of distributing code
    • Writing tests
    • Web APIs
    • Design an API considering use cases in advance
    • Write a simple API and produce self-documentation for it
    • Write tests for APIs
  • Week 2- Machine Learning Overview: Proficiency with core methods  0/9

    • Regression: cost functions (average squared error and friends; when to pick a non-standard cost function)
    • Random forests
    • Ensembles
    • SVMs
    • Basic idea
    • Kernels: understanding the basic types
    • Model comparison
    • Categorization: ROC curves
    • 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.

    0/3

    • How to weight, transform, combine, or drop features
    • How to represent transformations, models, parameters, and the results of a run, so they can be easily managed
    • 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  0/24

    • Preprocessing with Pandas
    • Reading data
    • Selecting columns and rows
    • Filtering
    • Vectorized string operations
    • Missing values
    • Handling time
    • Time series
    • Numpy,Spicy
    • Arrays
    • Indexing, Slicing and Iterating
    • Reshaping
    • Shallow vs deep copy
    • Broadcasting
    • Indexing (advanced)
    • Matrices
    • Matrix decompositions
    • Scikit-learn
    • Feature extraction
    • Classification
    • Regression
    • Clustering
    • Dimension reduction
    • Model selection
  • Week 5-Real Time Stream Processing with Spark, Kafka and Elastic Search  0/13

    • Kafka
    • Setup and configuration
    • Topics, partitions
    • API
    • Connecting to Spark
    • Elasticsearch
    • Setup
    • API
    • Kibana
    • Marvel Plugin
    • Real-time Data Pipeline
    • Twitter API
    • Spark streaming
  • Week 6-Real World Recommender System 

    Recommendations are often used in many industries, such as ecommerce, 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.

    0/5

    • How recommenders work, using both content-based and collaborative filtering techniques.
    • 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.
    • How to factor in business concerns: e.g. pricing, inventory, seasonality, new items, new users, popular products, serendipity, coverage, etc.
    • How to tune and evaluate a recommender.
    • How to generate recommendations in real-time.
  • Week 7- Deep Dive into R  0/12

    • R basic data types
    • Atomic vectors and operations on them
    • Lists
    • R as a functional programming language
    • Object attributes and object oriented programming
    • R complex data types (matrices, factors, and data frames)
    • Unit testing
    • Debugging, and code profiling
    • Writing R packages
    • String processing, dates, regular expressions (using the stringi package)
    • Speeding up operations on multiple files
    • Dynamic report generation with knitr
  • Week 8- Speeding up R and Python Models: Rcpp and Cython  0/7

    • What is Rcpp and Cython. Why C++ for data science?
    • C++ introduction: scalar data types, controlling program flow
    • Acessing R vectors through Rcpp
    • Lists and R functions
    • C++ Standard Library – fundamental data structures and algorithms
    • Introduction to Cython, linking C++ libraries to Cython; accessing NumPy objects
    • OpenMP – multithreaded C++ made simple
  • Week 9-Optimizing Data Structures and Memory Usage: Advanced Data Table  0/6

    • Aggregations (split-apply-combine type operations)
    • Add/update/delete columns without any unnecessary copies (by reference)
    • File reader (fread)
    • Ordered and rolling joins
    • Overlapping range/interval joins
    • Reshaping etc.
  • Week 10-Deep Learning for Image Classification  0/4

    • Background on neural nets, history, performance bottlenecks
    • Training deep nets
    • Regularization (dropout)
    • Interpreting weights on a hidden layer

Students Enrolled

and 273 students enrolled.