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Coupon applied
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Udemy
Artificial Neural Networks (ANN) with Keras in Python and R
Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R
Coding and Aptitude Round before interview
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
👍3
Data science use cases in finance
https://www.kdnuggets.com/2018/05/top-7-data-science-use-cases-finance.html
https://www.kdnuggets.com/2018/05/top-7-data-science-use-cases-finance.html
KDnuggets
Top 7 Data Science Use Cases in Finance - KDnuggets
We have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial…
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Udemy
Online Courses - Learn Anything, On Your Schedule | Udemy
Udemy is an online learning and teaching marketplace with over 250,000 courses and 80 million students. Learn programming, marketing, data science and more.
Forwarded from Jobs | Internships | Placement | Interviews
Looking for Data Scientists to work as Teaching Associates(10 positions)1-6 years exp .
Benglore based
Please share cvs on [email protected]
9-13L CTC
Benglore based
Please share cvs on [email protected]
9-13L CTC
Today's free course for limited time
https://www.udemy.com/course/best-data-science-business-analytics-course/?couponCode=DATA24
https://www.udemy.com/course/best-data-science-business-analytics-course/?couponCode=DATA24
Udemy
Online Courses - Learn Anything, On Your Schedule | Udemy
Udemy is an online learning and teaching marketplace with over 250,000 courses and 80 million students. Learn programming, marketing, data science and more.
Today's free course for limited time
https://www.udemy.com/course/the-complete-introduction-to-data-analytics-with-tableau/?ranMID=39197&ranEAID=ZVa/fYdMEMA&ranSiteID=ZVa_fYdMEMA-X7NqtQkWtSWIso08ccHM6w&LSNPUBID=ZVa/fYdMEMA&utm_source=aff-campaign&utm_medium=udemyads&couponCode=A12927FADDC6F13D34CA
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Udemy
The Complete Introduction to Data Analytics with Tableau
Kickstart your Data Analytics Career with Tableau by solving Realistic Analytics Projects. Learn Data Visualisation!
Which is not a type of machine learning
Anonymous Quiz
3%
Supervised Learning
8%
Unsupervised
83%
Inforcement Learning
6%
Reinforcement Learning
👍2
In which machine learning algorithm, machine learn on its own
Anonymous Quiz
33%
Supervised Learning
67%
Reinforcement Learning
👍1
Which one you want to learn first?
Anonymous Poll
56%
Data analysis and visualization
44%
Machine Learning and it's algorithms
Who is Data Scientist?
He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.
A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:
Determines correct datasets and variables.
Identifies the most challenging data-analytics problems.
Collects large sets of data- structured and unstructured, from different sources.
Cleans and validates data ensuring accuracy, completeness, and uniformity.
Builds and applies models and algorithms to mine stores of big data.
Analyzes data to recognize patterns and trends.
Interprets data to find solutions.
Communicates findings to stakeholders using tools like visualization.
He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.
A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:
Determines correct datasets and variables.
Identifies the most challenging data-analytics problems.
Collects large sets of data- structured and unstructured, from different sources.
Cleans and validates data ensuring accuracy, completeness, and uniformity.
Builds and applies models and algorithms to mine stores of big data.
Analyzes data to recognize patterns and trends.
Interprets data to find solutions.
Communicates findings to stakeholders using tools like visualization.
❤4
Which of the following are important skills for a data scientist?
Anonymous Quiz
8%
Analysing data
2%
Data wrangling
4%
Communication and critical thinking skills
4%
Statistical knowledge
83%
All of the above