AI News, Data Science Accelerator Program

Data Science Accelerator Program

The in-company Data Science Accelerator Program consists of twelve lectures and twelve hands-on sessions that provide a full overview of modern day Data Science.

The public Data Science Accelerator Program consists of five lectures and five hands-on sessions (10 days total) over a period of 5 months, covering all the core components.

The lectures and hands-on sessions of the Data Science Accelerator Program are based on years of hands-on experience with implementing of Data Science applications within organizations in many industries.

It’s divided into three segments: The next Data Science Accelerator Program wil start on March 22, 2018 (Tursdays every two weeks during 5 months).

The Data Science Accelerator Program is presented by world ­class industry practitioners and provides current and aspiring data scientists an opportunity for intensive on­ and off ­site instruction, access to an extensive network of speakers, mentors, and coaching.

Data Science Accelerator Program

The in-company Data Science Accelerator Program consists of twelve lectures and twelve hands-on sessions that provide a full overview of modern day Data Science.

The public Data Science Accelerator Program consists of five lectures and five hands-on sessions (10 days total) over a period of 5 months, covering all the core components.

The lectures and hands-on sessions of the Data Science Accelerator Program are based on years of hands-on experience with implementing of Data Science applications within organizations in many industries.

It’s divided into three segments: The next Data Science Accelerator Program wil start on March 22, 2018 (Tursdays every two weeks during 5 months).

The Data Science Accelerator Program is presented by world ­class industry practitioners and provides current and aspiring data scientists an opportunity for intensive on­ and off ­site instruction, access to an extensive network of speakers, mentors, and coaching.

Data Science Accelerator Program

The next training program starts in September 2018 and includes the following dates: Day 1: September 13, 2018 - Training: The Ipython StackDay 2: September 27, 2018 - Hackathon: Web development with Flask and Postgres as a BackendDay 3: October 11, 2018 - Training: Probability StackDay 4: October 31, 2018 - Hackathon: Solve Multi-Armed Bandit ProblemsDay 5: November 15, 2018 - Training: Machine LearningDay 6: November 29, 2018 - Hackathon: Create a Predictive Model, wrap it in an API and put it into ProductionDay 7: December 13, 2018 - Training: Ensembles & Neural NetworksDay 8: January 10, 2019 - Hackathon: Use Different Methods to Recognize Hand-written DigitsDay 9: January 24, 2019 - Training: Time seriesDay 10: February 7, 2019 - Hackathon: Predict an optimal Portfolio for the Stock Market Bring your own laptop with the following requirements: Xebia Academy (based in Hilversum, Amsterdam area) is an official training partner of Cloudera, the leader in Apache Hadoop-based software and services.

Data Engineering and Data Science Training

In this training, we will show you the basics of machine learning and how to apply machine learning techniques to real-world use cases.

Contact us for more information This one-day training is the ideal way for managers and product owners to learn about the impact and potential of artificial intelligence for their own organization.

You will be able to design the organization needed to become a data-driven organization and you will understand what competencies are required to successfully design, develop, and productionize artificial intelligence solutions.

This four-day training taught in English gives you the skills you need to ingest data on a Hadoop cluster and process it with Spark, Hive, Flume, Sqoop, Impala, and other Hadoop ecosystem tools.

You’ll walk away from this training with the practical knowledge you need to tackle the real-world challenges Hadoop developers face every day.

Developed by GoDataDriven to create a benchmark in the field, the program combines in-depth lectures with hands-on hackathons and propels data scientist to a higher standard of excellence.

You will learn how to apply the tidyverse stack from Rstudio, how to create interactive ML dashboards with Shiny as well as how to integrate R with databases, Spark and the h2o machine learning tooling.

This training empowers you as a data practioner to use Spark for data manipulation, machine learning and streaming algorithms for big data.

Through hands-on experience, you’ll learn how to leverage cloud GPU resources and build deep-learning models for images, text and time series.

Despite the large number of players in the NoSQL space, graph technology has still been far and away the fastest growing category of database over the last three years according to industry monitor DB-Engines.

Beyond 'traditional' data consisting of samples of a fixed number of interpretable variables, there is data such as free text, time series (financial transactions, power usage), audio (speech), images and video.

These so-called signals typically need to be processed such that meaningful variables can be extracted and structured prior to further usage in data analyses and machine learning applications.

Inspired by tools like IPython/Jupyter and Matlab, Databricks notebooks allow attendees to code jobs, data analysis queries and generate visualizations using their own Spark cluster, accessed through a web browser.

Custom training requirements: When the standard topics do not entirely meet your requirements, with in-company it is possible to make custom arrangements with the trainer directly. -

Best Data Science Bootcamps The 2018 Comprehensive Guide

Here is a list of date science programs who have also made it onto our shortlist, but do not currently have a lot of data science related alumni reviews.

Market Growth: By 2018, data science jobs in the U.S. will exceed 490,000, with fewer than 200,000 available data scientists to fill these positions (McKinsey &

Sure, the hype might sound like an exaggeration, but there’s no question that data science job growth isn’t slowing down anytime soon.

For example, you could be working for a B2C company that is looking to better understand their customer base, or you might be working for a company that offers data as the product.

Check out one of these free online courses to get started: CS109 Data Science: A series of free lectures from Harvard University Data Science Specialization: An online course from Johns Hopkins Big Data University: Choose from 7 short learning paths 2.

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

From accounting, to risk analysis, to a/b testing, to working on government data, there’s a huge need for a data analyst’s skillset.

Once you get more experience as a data analyst, you can take more advanced courses, earn a master’s degree or consider a data-science bootcamp to jump into a more research-based, analytical role.

Things like bar charts, pie charts, trend lines, simple regression analysis, box plots, etc., will be common day-to-day tasks.

Skills and tools: While a data analyst simply may be doing work in excel to present summary statistics of small datasets, a data scientist will be managing larger data sets from different sources.

As new data comes in and new problems come up, these data scientists are employed to find ways to optimize a company’s marketing campaign, optimize a hedge fund’s trading algorithm, or come up with new ways to predict or model consumer behavior.

Data engineers essentially lay the groundwork for a data analyst or data scientist to easily retrieve the needed data for their evaluations and experiments.They focus on creating robust data systems that can aggregate, process, clean, transform, and store large amounts of data.

Instead of data analysis, data engineers are responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place.

You will need the ability to learn whatever technology the company is using to manage their data systems, and there are a wide variety of them, although the core underlying principles are very similar.

The primary job responsibility includes building robust, fault-tolerant data pipelines that clean, transform, and aggregate unorganized and messy data into databases or data sources.

The McKinsey Global Institute has predicted that by 2018 the U.S. could face a shortage of between 140,000 to 190,000 people with deep analytical skills, and a shortage of 1.5 million managers and analysts who know how to leverage data analysis to make effective decisions.

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