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How I'd Learn Data Science If I Were To Start All Over Again

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A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting. I'm aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that's ok, the important thing is to learn and enjoy it. So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.


Why So Many Data Science Projects Fail to Deliver

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This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1


Data Wrangling With Python -- Part 2

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We can delete one or more rows from a data frame. With the help of the boolean condition, we can create a new data frame that excludes rows we want to delete. We can also use drop method like df.drop([0,1],axis 0) to drop the first two rows.More practical method is simply to wrap boolean condition inside df[]. If we notice clearly, we didn't drop any rows() Every row in the data frame is unique. The duplicate() method, returns a boolean series denoting a row is duplicate or not.


Get 20 Percent Off Top-Rated Edureka Data Analytics Courses This Month

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Take advantage of these Edureka data analytics courses with the online learning platform's 20 percent off for the month of March. Data analytics skills are in high demand among organizations that are looking to use their collected data to generate valuable business insight. The pandemic and subsequent "new normal" of remote work are furthering demands for these skills. Many are turning to online learning platforms to up their game and acquire the data analytics skills most likely to help them stand out. And whether you are looking to acquire those skills for work or for play, this collection of Edureka data analytics courses will help you learn the ropes so you can pilot some of the most widely used tools in no time!


Why So Many Data Science Projects Fail to Deliver

#artificialintelligence

This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1 Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.2


The 10 data mining techniques data scientists need for their toolbox

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At their core, data scientists have a math and statistics background. Out of this math background, they're creating advanced analytics. Just like their software engineering counterparts, data scientists will have to interact with the business side. This includes understanding the domain enough to make insights. Data scientists are often tasked with analyzing data to help the business, and this requires a level of business acumen. Finally, their results need to be given to the business in an understandable fashion. This requires the ability to verbally and visually communicate complex results and observations in a way that the business can understand and act on them. Thus, it'll be extremely valuable for any aspiring data scientists to learn data mining -- the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights. Here is a simple list of reasons on why you should study data mining? There is a heavy demand for deep analytical talent at the moment in the tech industry. You can gain a valuable skill if you want to jump into Data Science / Big Data / Predictive Analytics. Given lots of data, you'll be able to discover patterns and models that are valid, useful, unexpected, and understandable. Use some variables to predict unknown or future values of other variables (Predictive). You can activate your knowledge in CS theory, Machine Learning, and Databases. Last but not least, you'll learn a lot about algorithms, computing architectures, data scalability, and automation for handling massive datasets.


Data Preprocessing Concepts with Python

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In this article, we will study some important data preprocessing methods. It is a very important step to visualize the data and make it in a suitable form so that the estimators (algorithm) fit well with good accuracy. Standardization is a process that deals with the mean and standard deviation of the data points. As raw data, the values are varying from very low to very high. So, to avoid the low performance in the model we use standardization.


Big Data Analytics for academic- Full-time program -Tutors India

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"Big data is high-volume, high velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight, and decision making" (Gartner's IT Glossary). For the majority of students, analysing big data is by far the most challenging piece of academic work that they have attempted or are ever likely to try in the future. The majority of the students do agree and would have experienced the scenario. At Tutors India, we have subject matter expertise who has capability to understand the different layers of data being integrated and the level of granularity of integration to create the holistic picture. Further the team also well equipped with advanced mathematical degrees, statistics and with multiple specialist degree.


Use Machine Learning and GridDB to build a Production-Ready Stock Market Anomaly Detector

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In this project, we use GridDB to create a Machine Learning platform where we Kafka is used to import stock market data from Alphavantage, a market data provider. Tensorflow and Keras train a model that is then stored in GridDB, and then finally uses LSTM prediction to find anomalies in daily intraday trading history. The last piece is that the data is visualized in Grafana and then we configure GridDB to send notifications via its REST Trigger function to Twilio's Sendgrid. The actual machine learning portion of this project was inspired by posts on Towards Data Science and Curiously. This model and the data flow is also applicable to many other datasets such as predictive maintenance or machine failure prediction or wherever you want to find anomalies in time series data.


House Price Predictions Using Keras

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This is a starter tutorial on modeling using Keras which includes hyper-parameter tuning along with callbacks. Creating a Keras-Regression model that can accurately analyse features of a given house and predict the price accordingly. We would be using numpy and pandas for processing our dataset, matplotlib and seaborn for data visualization, and Keras for implementing our neural network. Also, we would be using Sklearn for outlier detection and scaling our dataset. We would first see all the features having missing values. This would include data from both training and testing data.