AI News, Hackerday – Stay Updated in your Career through Hands-On Projects

Hackerday – Stay Updated in your Career through Hands-On Projects

This happens because more importance is given to theory over practical applications in the academic setting – which is poles apart from the industry setting.Companies want a fast response to any change in the market or trends.

The multi-disciplinary nature of data science with an ocean of data science technologies to master- makes it difficult for data scientists and ‘data scientists in-training’, to get together and hack on real world data problems and learn from each other.

Schedule for Hackerday Session-Nov 21st – Predicting survival on Titanic using Data Science- Project Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew.

Takeaways from the project-“Walmart Store’s Sales Forecasting”: Scipy, ggplot, csvkit, NumPy, pandas, matplotlib World needs better Data Scientists -This is the best time to learn and upgrade your data science skills Big data is making waves in the market for quite some time, there are several big data companies that have invested in Hadoop, NoSQL and data warehouses for collecting and storing big data .With open source tools like Apache Hadoop, there are organizations that have invested in millions for storing big data.

Apache Hadoop has undoubtedly been the big elephant in the big data room - but to find an ideal solution with real-time business intelligence it needs to be combined with Apache Spark, Apache Storm, Kafka, and Flume in order to bring evolutionary changes in big data processing environments.

Check this trend.” The career opportunities and pay packages for data scientists are growing at an exponential rate and professionals need to keep pace with various data science programming technologies and programming languages like Python and R.

Hackerday is mix of teaching, coding, sharing and learning where coders and designers come together over a weekend to code in groups on various projects led by industry experts through online live coding sessions.

Stay updated through online hackathons from DeZyre Online University Hackerday will enhance your abilities to get up to speed quickly on trending software technologies related to Big Data and Data Science technologies - by providing you a hands-on experience on Python, R, Apache Hadoop, Spark, etc.

Hackerday is for all professionals with some kind of programming experience –who have the enthusiasm to learn about the trending technologies, have desire to build something new and enjoy problem solving in the vicinity of skilled experts.

Hackerday – Stay Updated in your Career through Hands-On Projects

This happens because more importance is given to theory over practical applications in the academic setting – which is poles apart from the industry setting.Companies want a fast response to any change in the market or trends.

The multi-disciplinary nature of data science with an ocean of data science technologies to master- makes it difficult for data scientists and ‘data scientists in-training’, to get together and hack on real world data problems and learn from each other.

Schedule for Hackerday Session-Nov 21st – Predicting survival on Titanic using Data Science- Project Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew.

Takeaways from the project-“Walmart Store’s Sales Forecasting”: Scipy, ggplot, csvkit, NumPy, pandas, matplotlib World needs better Data Scientists -This is the best time to learn and upgrade your data science skills Big data is making waves in the market for quite some time, there are several big data companies that have invested in Hadoop, NoSQL and data warehouses for collecting and storing big data .With open source tools like Apache Hadoop, there are organizations that have invested in millions for storing big data.

Apache Hadoop has undoubtedly been the big elephant in the big data room - but to find an ideal solution with real-time business intelligence it needs to be combined with Apache Spark, Apache Storm, Kafka, and Flume in order to bring evolutionary changes in big data processing environments.

Check this trend.” The career opportunities and pay packages for data scientists are growing at an exponential rate and professionals need to keep pace with various data science programming technologies and programming languages like Python and R.

Hackerday is mix of teaching, coding, sharing and learning where coders and designers come together over a weekend to code in groups on various projects led by industry experts through online live coding sessions.

Stay updated through online hackathons from DeZyre Online University Hackerday will enhance your abilities to get up to speed quickly on trending software technologies related to Big Data and Data Science technologies - by providing you a hands-on experience on Python, R, Apache Hadoop, Spark, etc.

Hackerday is for all professionals with some kind of programming experience –who have the enthusiasm to learn about the trending technologies, have desire to build something new and enjoy problem solving in the vicinity of skilled experts.

Big Data Learning Path for all Engineers and Data Scientists out there

The field of big data is quite vast and it can be a very daunting task for anyone who starts learning big data &

This article provides you a guided path to start your journey to learn big data and will help you land a job in big data industry.

To tackle this problem, I have explained each big data role in detail and also considering different job roles of engineers and computer science graduates.

have tried to answer all your questions which you have or will encounter while learning big data. To help you choose a path according to your interest I have added a tree map which will help you identify the right path.

One of the very first questions that people ask me when they want to start studying Big data is, “Do I learn Hadoop, Distributed computing, Kafka, NoSQL or Spark?” Well, I always have one answer: “It depends on what you actually want to do”.

The Big data engineering revolves around the design, deployment, acquiring and maintenance (storage) of a large amount of data.

The systems which Big data engineers are required to design and deploy make relevant data available to various consumer-facing and internal applications.

While Big Data Analytics revolves around the concept of utilizing the large amounts of data from the systems designed by big data engineers.

Broadly, based on your educational background and industry experience we can categorize each person as follows: (This includes interests and doesn’t necessarily point towards your college education).

 Thus, by using the above categories you can define your profile as follows: Eg 1: “I am a computer science grad with no experience with fairly solid math skills”.

In order to define your needs, you must know the common big data jargon. So let’s find out what does big data actually means?

Scenario 1: Design a system for analyzing sales performance of a company by creating a  data lake from multiple data sources like customer data, leads data, call center data, sales data, product data, weblogs etc.

Solution for Scenario 1: Data Lake for sales data (This is my personal solution, you may come up with a more elegant solution if you do please share below.) So, how does a data engineer go about solving the problem?

point to remember is that a big data system must not only be designed to seamlessly integrate data from various sources to make it available all the time, but it must also be designed in a way to make the analysis of the data and utilization of data for developing applications easy, fast and always available (Intelligent dashboard in this case).

But data sources like weblogs, customer interactions/call center data, image data from the sales catalog, product advertising data.

This is a bit different than any conventional domains like data science and machine learning where you start at something and endeavor to complete everything in the field.

Even though some of the technologies in the tree are pointed to be data scientist’s forte but it is always good to know all the technologies till the leaf nodes if you embark on a path.

But don’t worry, if you do not want to code in these languages ou can choose Python or R because most of the big data technologies now support Python and R extensively.

For a Data Scientist capable of working with big data you need to add a couple of machine learning pipelines to the tree below and concentrate on the machine learning pipelines more than the tree provided below.

And providing a definitive answer to what type of NoSQL database you need to take into account your system requirements like latency, availability, resilience, accuracy and of course the type of data that you are dealing with.

10 things I learned doing my first data science project

To make this decision data driven, I decided what defined “best” to me: things you would expect from a bearded cyclist: cafes, art, bars, etc.

spent two hours working through a couple of datasets (one with census tractsand neighborhoods and one with census tracts and zip codes).

I wanted to do a join on both of their census tract columns (census tracts are the way the census refers to chunks of land).

If I’d have looked at the data dictionary (Socrata’s TL;DR of the contents of the database) I’d have known that the one dataset used 2010 census tracts and the other one 2017 census tracts.

know, you feel a little bit like Neo when you are calling your python scripts from terminal (or, if you have Bryan in your team, from your awesomely customized Oh My ZSH skin in iTerm).

It wasn’t just me who fell into this trap — one of our engineers was helping me with the slightly awkward syntax for editing pandas data frames, and we spent about 15 minutes trying to figure out where we went wrong before we re-ran the whole script.

When you have functions that do a whole bunch of things, it is often better (and definitely more readable) to make a few functions that each do one thing, and create a master function that calls those functions.

Trying to work my way around an enormous function to fix a bug became so much easier when I split the function up politely.

It makes someone who’s working with your code have to read back to where the variable was defined to understand what that variable means.

Write comparison_value_column, and if you can’t be bothered typing, use a fancy text editor with variable autocompletion (IntelliJ does this, so does Atom and Sublime and pretty much all other code-first text editors).

If the user enters compare_boolean = True, then that OK’s the if statement that will take you through the second part of the function, counting empanadas within the dataframe.

If you ever find yourself looping through a data frame, and within that loop, looping through another one, you’re going to have a bad time.

Going through a dataset bigdata of 70k rows, whilst looping through another littledata of 1000 rows for each row of bigdata means 7 million iterations.

Don’t spend your time solving a problem that someone else has solved, ask around and get help, and focus on what makes your project interesting.

Tune in next week for my conclusive, decisive and data driven solution to the age old question: Which neighborhood would Johanan like to live in most, according to his own completely arbitrary criteria and publicly available data.

Data Science with R | Project | DeZyre Hackerday

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