CloudyML - Data Science & Analytics

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https://www.linkedin.com/posts/akashraj001_at-any-point-in-life-did-you-feel-that-you-activity-6830504870173294593-moVH

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CloudyML - Data Science & Analytics

Fortune Jobs

Data Analyst Jobs in Deloitte | Deloitte Data Analytics Career

Data Analyst Jobs in Deloitte Mumbai and more locations in India. Deloitte Data Analytics Career for Senior and Mid-level positions.

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CloudyML - Data Science & Analytics

1. Company: Tata Consultancy Services
Role: Data Scientist
Experience: 5- 10 Years
Job link: https://www.linkedin.com/jobs/search/?alternateChannel=remote&alternateCode=seturl&currentJobId=2674709197&f_WRA=true&geoId=102713980&origin=JOBS_HOME_REMOTE_JOBS&refId=1XgTGWHJZx5GJZhOHpCU2Q%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_job_home%3Bj%2Bo%2FnoGLQGWHEEt%2FIE64rA%3D%3D&lici=8E81SLT7Cge%2FD2Lj%2Bhc9aw%3D%3D

2. Company: Dell Technologies
Role: Junior Data Scientist
Experience: 5+ years
Job link: https://www.linkedin.com/jobs/view/2669974011/?eBP=NotAvailableFromMidTier&recommendedFlavor=IN_NETWORK&refId=Wjdu09MeKPo4MXQYNh427w%3D%3D&trackingId=5t5695e8rFlMm5dGQdmLQg%3D%3D&trk=flagship3_search_srp_jobs&lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_jobs%3BgZQLsydnSuyksd%2Btnubpaw%3D%3D&lici=5t5695e8rFlMm5dGQdmLQg%3D%3D

3. Company: EY
Role: Data Scientist - Digital Knowledge Platform
Experience: 5 years
Job link: https://www.linkedin.com/jobs/view/2673591461/?eBP=JOB_SEARCH_ORGANIC&recommendedFlavor=COMPANY_RECRUIT&refId=EnjOMzRCrlaqkEpWkrc3%2FQ%3D%3D&trackingId=KadOnGPhZiNy7UM6UmK9RQ%3D%3D&trk=flagship3_search_srp_jobs&lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_jobs%3BASb4vGrbS7W8Ixc76NdVBg%3D%3D&lici=KadOnGPhZiNy7UM6UmK9RQ%3D%3D

4. Company: Cisco
Role: Data Scientist - (New Grad) - India UHR
Experience: 4 years
Job link : https://www.linkedin.com/jobs/view/2679202845/?eBP=JOB_SEARCH_ORGANIC&recommendedFlavor=SCHOOL_RECRUIT&refId=EnjOMzRCrlaqkEpWkrc3%2FQ%3D%3D&trackingId=5XYYhhLmqsRHef6xs5bbWw%3D%3D&trk=flagship3_search_srp_jobs&lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_jobs%3BJoiaiQ2rTN6czOrtFoSwWw%3D%3D&lici=5XYYhhLmqsRHef6xs5bbWw%3D%3D

5. Company: Uber
Role: Data Analyst I, Tech
Experience: 5 years
Job link: https://www.linkedin.com/jobs/view/2679268362/?alternateChannel=search&refId=EnjOMzRCrlaqkEpWkrc3%2FQ%3D%3D&trackingId=WaWyd19S35vr%2FqjDAxj8Gw%3D%3D&trk=d_flagship3_search_srp_jobs

5,458,000+ Jobs in United States (228,679 new)

Today’s 5,458,000+ jobs in United States. Leverage your professional network, and get hired. New United States jobs added daily.

2.54K views08:38

CloudyML - Data Science & Analytics

Today's Quiz - What is the difference between Skewness and kurtosis?

Company Name - HP

Role - Data Scientist

The characteristic of a frequency distribution that ascertains its symmetry about the mean is called skewness. On the other hand, Kurtosis means the relative pointedness of the standard bell curve, defined by the frequency distribution.

Skewness is characteristic of the deviation from the mean, to be greater on one side than the other, i.e. attribute of the distribution having one tail heavier than the other. Skewness is used to indicate the shape of the distribution of data. Conversely, kurtosis is a measure to indicate the flatness or peakedness of the frequency distribution curve and measures the tails or outliers of the distribution.

Skewness is an indicator of lack of symmetry, i.e., both left and right sides of the curve are unequal, with respect to the central point. As against this, kurtosis is a measure of data, that is either peaked or flat, with respect to the probability distribution.

Skewness shows how much and in which direction, the values deviate from the mean. In contrast, kurtosis explain how tall and sharp the central peak is.

In a skewed distribution, the curve is extended to either left or right side. So, when the plot is extended towards the right side more, it denotes positive skewness, wherein mode < median < mean. On the other hand, when the plot is stretched more towards the left direction, then it is called as negative skewness and so, mean < median < mode. Positive kurtosis represents that the distribution is more peaked than the normal distribution, whereas negative kurtosis shows that the distribution is less peaked than the normal distribution.

2.55K views13:42

CloudyML - Data Science & Analytics

How would you build the algorithm for type-ahead search for Netflix?

We want a recommendation algorithm sounds like RNN which is a recurrent neural network which is not easy to setup at all, but we can try building recommendation with much simpler approach that is with a simple prefix matching algorithm and we can certainly go into expanding it on until we have something that will be on par with RNN.

We will use Lookup in this database table/ prefix table. This prefix table starts with an input string and that is your prefix and it will output the suggested string or suffixes. Example: what does “hello” prefix to and its suffixes to the model.

Scoping is very important by doing fuzzy matching, context matching like what if you were using a different language. So, if you are trying to input “big” that could output any number of suffixes example: big shot or big sky or the big year etc.

In existing search corpus of billions of searches what proportion of the time do people writing the big actually click on the big shot and what proportion of the time do they output the big sky, so you can just have a simple thing that has every possible search prefix that has ever been typed on Netflix and output that to the most common thing that they clicked on. Boom! That’s your prefix matching a recommendation algorithm for type ahead search.

Context matching is also important here, if u have string input and a user profile with various number of features into a string output. You can convert user profile into a K means clustering we can output this into either John Stamos’s fan and not John Stamos’s fan, so if you are John Stamos’s fan and if you type the big, every time Netflix is going to recommend the big shot or else other way round. Also user profile can be set to right dimensionality.

2.62K views11:57

CloudyML - Data Science & Analytics

Some Data Science Openings for freshers:

1. Company: IBM
Role: Data Analyst
Experience: 0-1 years
Job link: https://lnkd.in/eNFQs5fM

2. Company: Cognizant
Role: Trainee- Junior Data Analyst
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3. Company: Tide Software
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4. Company: Zilingo.com
Role: Data Analyst
Experience: 6 Months -2 years
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5. Company: Flipkart
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2.62K views07:27

CloudyML - Data Science & Analytics

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CloudyML - Data Science & Analytics

How to detect an anomaly in a distribution?

Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior. It can be considered the thoughtful process of determining what is normal and what is not. Anomalies are also referred to as outliers, novelties, noise, exceptions and deviations. Simply, anomaly detection is the task of defining a boundary around normal data points so that they can be distinguishable from outliers.

Anomalies can be broadly categorized as:

Point anomalies: A single instance of data is anomalous if it’s too far off from the rest. Business use case: Detecting credit card fraud based on “amount spent.”

Contextual anomalies: The abnormality is context specific. This type of anomaly is common in time-series data. Business use case: Spending $100 on food every day during the holiday season is normal, but may be odd otherwise.

Collective anomalies: A set of data instances collectively helps in detecting anomalies. Business use case: Someone is trying to copy data from a remote machine to a local host unexpectedly, an anomaly that would be flagged as a potential cyber-attack.

The different types of methods for anomaly detection are as follows:

Simple Statistical Methods

The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of a distribution, including mean, median, mode, and quantiles. When an anomalous data point deviates by a certain standard deviation from the mean, then traversing mean over time-series data isn’t exactly trivial, as it’s not static. Thus, a rolling window to compute the average across the data points and it’s intended to smooth short-term fluctuations and highlight long-term ones.

Machine Learning-Based Approaches for Anomaly Detection:

(a) Clustering-Based Anomaly Detection:

The approach focuses on unsupervised learning, similar data points tend to belong to similar groups or clusters, as determined by their distance from local centroids.

The k-means algorithm can be used which partition the dataset into a given number of clusters. Any data points that fall outside of these clusters are considered as anomalies.

(b) Density-based anomaly detection:

This approach is based on the K-nearest neighbors algorithm. It’s evident that normal data points always occur around a dense neighborhood and abnormalities deviate far away. To measure the nearest set of a data point, you can use Euclidean distance or similar measure according to the type of data you have.

(c) Support Vector Machine-Based Anomaly Detection:

A support vector machine is another effective technique for detecting anomalies. One-Class SVMs have been devised for cases in which one class only is known, and the problem is to identify anything outside this class.

This is known as novelty detection, and it refers to automatic identification of unforeseen or abnormal phenomena, i.e. outliers, embedded in a large amount of normal data.

Anomaly detections helps to monitor any data source, including user logs, devices, networks, and servers. This rapidly helps in identifying zero-day attacks as well as unknown security threats.

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2.65K views12:40

CloudyML - Data Science & Analytics

Some openings for freshers:

1. Company: Datumlyze Pvt Ltd
Role: Technical Data Analyst
Experience: 0-2 years
Job link: https://lnkd.in/eURVxHRd

2. Company: Analytos
Role: Junior Data Scientist
Experience: 0-2 years
Job Link: https://lnkd.in/ezDRZ-4B

3. Company: Hotstar
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4. Company: Tide Software
Role: Data Scientist
Experience: 0-5 years
Job link: https://lnkd.in/e4C7BPVP

5. Company:Cipla
Role: Data Scientist
Experience: 1-7 years
Link: https://lnkd.in/eF34dNgR

2.5K views14:35

CloudyML - Data Science & Analytics

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22:49

CloudyML - Data Science & Analytics

Today's Quiz - What is cross-validation and why would you use it?

Company Name - IBM

Role - Data Scientist

Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate the model.

In typical cross-validation, the training and validation sets must cross-over in successive rounds such that each data point has a chance of being validated against.

The basic form of cross-validation is k-fold cross-validation. Other forms of cross-validation are special cases of k-fold cross-validation or involve repeated rounds of k-fold cross-validation.

In k-fold cross-validation, the data is first partitioned into k equally (or nearly equally) sized segments or folds. Subsequently k iterations of training and validation are performed such that within each iteration a different fold of the data is held-out for validation while the remaining k − 1 folds are used for learning.

So, why do we use cross-validation?

It allows us to get more metrics and draw important conclusion about our algorithm and our data.

Helps to tune the hyper parameters of a given machine learning algorithm, to get good performance according to some suitable metric.

It mitigates overfitting while building a pipeline of models, such that second’s models input will be real predictions on data that our first model never seen before.

K-fold cross validation also significantly reduces bias as we are using most of the data for fitting, and also significantly reduces variance as most of the data is also being used in validation set.

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2.57K views15:37

CloudyML - Data Science & Analytics

Today's Quiz - What is multicollinearity and how to detect it in a dataset?
Company Name - Infosys
Role - Data Scientist

Let’s start with understanding correlation.

The correlation between two variables can be measured with a correlation coefficient which can range between -1 to 1. If the value is 0, the two variables are independent and there is no correlation. If the measure is extremely close to one of the extreme values, it indicates a linear relationship and highly correlated with each other. This means a change in one variable is associated with a significant change in other variables.

Multicollinearity is a condition when there is a significant dependency or association between the independent variables or the predictor variables. A significant correlation between the independent variables is often the first evidence of presence of multicollinearity.
How to test Multicollinearity?

Correlation matrix / Correlation plot: A correlation plot can be used to identify the correlation or bivariate relationship between two independent variables
Variation Inflation Factor (VIF): VIF is used to identify the correlation of one independent variable with a group of other variables.

Consider that we have 9 (assume V1 to V9) independent variables. To calculate the VIF of variable V1, we isolate the variable V1 and consider as the target variable and all the other variables(i.e V2 to V9) will be treated as the predictor variables.

We use all the other predictor variables and train a regression model and find out the corresponding R2 value.
Using this R2 value, we compute the VIF value gives as the image below.

It is always desirable to have VIF value as small as possible. A threshold is also set, which means that any independent variable greater than the threshold will have to be removed.

2.53K views10:32

CloudyML - Data Science & Analytics

How is oversampling different from under sampling?

Oversampling and under sampling are 2 important techniques used in machine learning – classification problems in order to reduce the class imbalance thereby increasing the accuracy of the model.

Classification is nothing but predicting the category of a data point to which it may probably belong by learning about past characteristics of similar instances. When the segregation of classes is not approximately equal then it can be termed as a “Class imbalance” problem. To solve this scenario in our data set, we use oversampling and under sampling.

Oversampling is used when the amount of data collected is insufficient. A popular over-sampling technique is SMOTE (Synthetic Minority Over-sampling Technique), which creates synthetic samples by randomly sampling the characteristics from occurrences in the minority class.

Conversely, if a class of data is the overrepresented majority class, under sampling may be used to balance it with the minority class. Under sampling is used when the amount of collected data is sufficient. Common methods of under sampling include cluster centroids and To meke links, both of which target potential overlapping characteristics within the collected data sets to reduce the amount of majority data.

Ex: Let’s say in a bank majority of the customers are from a specific Race and very few customers are from other races , hence if the model is trained with this data , it is most likely that Model will reject the loan for Minority Race.

So, what should we do about it?

For, oversampling we increase the number of records belonging to the “minority race” category by duplicating its presence. So that the difference between the numbers of records belonging to both of the classes will narrow down.

Under sampling we reduce the number of records belonging to the “majority race”. The records for the deletion are selected strictly through a random process and are not influenced by any constraints or bias.

To conclude, over sampling is preferable as under sampling can result in the loss of important data. Under sampling is suggested when the amount of data collected is larger than ideal and can help data mining tools to stay within the limits of what they can effectively process.

2.5K views08:08

CloudyML - Data Science & Analytics

Data Science openings for freshers:

Company: Diatoz Solutions
Role: Data Scientist
Experience: 0-1 years
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Company: Valiance Solutions
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2.49K views14:31

CloudyML - Data Science & Analytics

Today's Quiz - Difference Between Bagging and Boosting?
Company Name - Latentview Analytics

Role - Data Scientist

Bagging and Boosting are two types of Ensemble Learning, which helps to improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model.

So, let’s understand the difference between Bagging and Boosting?

Bagging(Bootstrap aggregation): It is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting: It is also a homogeneous weak learners’ model. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.

If the classifier is unstable (high variance), then we need to apply bagging. If the classifier is steady and straightforward (high bias), then we need to apply boosting.

In bagging, different training data subsets are randomly drawn with replacement from the entire training dataset. In boosting, every new subset contains the elements that were misclassified by previous models.

Bagging simplest way of combining predictions that belong to the same type. Boosting is the way of combining predictions that belong to the different types.

Each model is built independently for bagging. While in the case of boosting, new models are influenced by the performance of previously built models.

Bagging attempts to tackle the overfitting issue. Boosting tries to reduce bias.

Example: The Random Forest model uses Bagging. The AdaBoost uses Boosting techniques

2.49K views13:51

CloudyML - Data Science & Analytics

Some Data Science Internship opportunities for Freshers:

Company: MSCI Inc.
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Company: Vision Insight
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Posted 5:49:16 AM. Position Overview
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CloudyML - Data Science & Analytics

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CloudyML - Data Science & Analytics

Photo

We are introducing an assignment based Data Science course for hands-on practical learning experience to boost your confidence in this field.

Aim of this course is to make you Interview Ready for the roles of Data Scientist, Data Analyst, ML Engineer, Business Analyst, and Research Analyst.

Course is Designed by Akash Raj
Data Scientist | Founder- CloudyML
23,000+ Followers on LinkedIn

Key Features of this course :

☑️ Assignments-based learning for Hands-on experience and Confidence build-up.

☑️25+ Assignments have nicely curated questions to cover all the important topics of Python, Machine Learning, SQL, Statistics, Model Deployment and AWS (SageMaker, Lambda Functions, API Gateway).

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☑️1-1 course support mentorship available from 10 am to 10 pm every day (including weekends) to resolve Course and Assignment related queries.

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☑️Reference (YouTube videos and blogs) Collections in a structured way to learn all the Concepts.

☑️Get Data Science Interview Questions and Answers in the form of Newsletters daily.

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2.52K views14:53

CloudyML - Data Science & Analytics

Today's Quiz -  What is the difference between Rank and Dense Rank function?
Company Name - Myntra

Role - Data Analyst

An analytic function computes values over a group of rows and returns a single result for each row. This is different from an aggregate function, which returns a single result for a group of rows.

With analytic functions you can compute moving averages, rank items, calculate cumulative sums, and perform other analyses.

RANK gives you the ranking within your ordered position. Ties are assigned the same rank, with the next ranking(s) skipped.

DENSE_RANK again gives you the ranking within your ordered partition, but the ranks are consecutive. No ranks are skipped if there are ranks with multiple items.

Example below.. which is order partitioned on salary

Salary.       Rank.      Dense rank

1000          1                  1
2000           2                 2

2000           2                  2

2000           2                  2

3000           5                 3

Due to next rankings skipped in the case of RANKS, generally DENSE_RANK is preferred as it gives proper ranking when we want to calculate nth highest salary

2.45K views18:25

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