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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
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Following the emergence of technology, almost everything is evolving with time. The concept of gaining knowledge from various fields using data is inclusive hence birthing new career paths and professional communities. Data Science is the buzzword making a wave in this present millennial.
If you are looking forward to starting your journey as a Data Scientist or you want to explore this terrain, it is vital to gain mastery of the numerous skills required. To be successful in your community and job applications, listed below are possible questions you can be asked during a Data Science interview.
Big Data: It is a field that treats ways to extract, analyze, or deal with data sets that are too large and complex for a traditional data processing application software.
Data Science: is a field that comprises everything related to data cleansing, preparation, and analysis.
Data Analytics: is the science of examining raw data to reach certain conclusions. It involves applying an algorithm or mechanical process to derive insights and running through several data sets to look for meaningful correlations.
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Overfitting is a modeling error that occurs when a function is closely fit to a limited set of data points. When a model is too complex and it has too many parameters relative to the number of observations, over-fitting will occur.
Underfitting occurs when a Machine Learning algorithm cannot capture the underlying trends and does not fit the data well. When fitting a linear model to non-linear data, unfitting is expected to occur.
Both are used as a measure to check how two variables change with respect to each other.
Both classification and regression techniques are related to predictions and are supervised Machine Learning algorithms.
Regression algorithms are used to predict the continuous values such as price, salary, age, etc and it involves predicting a response i.e. a value from a continuous set of data.
Classification algorithms are used to predict or classify discrete values such as male or female, true or false, etc.
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You may have an idea already because cleansing usually means to cleanout. Data cleansing involves the detection, correction, removal of inaccurate, incorrect, and coarse data that is not required from a database or set of records. After cleansing, the quality of data is efficiently improved making the cleaned data set consistent with the other sets you have in your system.
It is a supervised Machine Learning algorithm that is used for classification and Regression.
Raw data with anomalies will always give the wrong results and analysis. There is no one way to highlight the exact procedures in cleaning your data since the techniques may differ based on the data type.
The basic steps include:
Both are commonly used in learning algorithms.
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K-NN is a supervised learning algorithm used for classification where the variable "k" represents a parameter. K-nearest neighbor requires labeled data and when the data is given, it can classify the new unlabeled data by analyzing the k number of closer data points.
K-means clustering is an unsupervised algorithm requiring unlabeled data. It can be used to understand social media trends, changes in strategic marketing, demographics among others.
P-value can be easily understood as the probability of getting results in an event such that any observation made is as extreme as the observed results of a hypothesis test called the null-hypothesis. When the p-value is smaller, it means that we can reject the test and when values are higher, the null hypothesis is accepted.
The role of statistics in Data Science cannot be undermined. As a field, statistics is the science of learning from data as it reveals information about data required in decision-making processes. It provides the methods and tools required to understand data structures and its roles include the acquisition of data, processing, and mining, making predictions, modeling, validation, and visualization of the interpreted structures.
In an observed data, linear regression models the relationship between two variables by fitting a linear equation. For a Linear regression line, the equation can be written as Y= a+bX where a is the intercept, b is the slope of the line, X is the explanatory variable and Y is the dependent variable.
Linear Regression is used when the value of a variable is to be predicted based on the value of another variable. The dependent variable is the variable to be predicted.
It is a probability distribution that describes how the values of a variable are distributed. Most of the values cluster around the central peak and the probabilities for values move away from the mean.
It is often called a bell curve because of the shape of the graph.
It is significant in statistics because it is often used in both social and natural sciences to represent random variables with real values whose distributions are not known.
By using a value_counts() function
A tensor is a mathematical object represented as arrays of higher dimensions
R is more efficient and suitable for Machine Learning than using it for text analysis. Python becomes a more suitable option for text analysis because it contains the Pandas library that gives easy to use data structures and high-performing tools for data analysis. Python also has a fast performance rate for all types of data analysis.
In determining the optimal number of clusters in a data set as in k-means clustering, three major methods that can be used are:
There are many more libraries that can be explored.
Stochastic Gradient Descent computes a gradient using a single sample and it converges faster while a Batch Gradient Descent computes a gradient using the whole data set and it takes a longer time to converge.
It is the process by which applications read and write data. A memory manager determines where to put an application's data. A Python memory manager helps to internally manage the heaps containing all Python objects and data structures.
Anonymous functions are introduced using the keyword 'lambda'. The functions are often arguments being passed to higher-order functions. A higher-order function is a function that takes one or more functions as an argument or returns a function as its result.
It is the opposite of False positives and it is also called 'Type II error'. It occurs when events are classified as non-events.
In Supervised Machine Learning, the machine is trained using data that is well labeled, such that the algorithm provides an answer key it can use to evaluate its accuracy on the training data.
On the other hand, Unsupervised learning is a Machine Learning technique that does not require labeled data and the model needs no supervision. The algorithm extracts features and patterns on its own.
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It is one of the basic Machine Learning techniques alongside supervised and unsupervised machine learning. Reinforcement learning is the training of Machine Learning models to make a sequence of decisions that include moving from a state to another.
The types of sampling bias include:
An Eigenvector is a non-zero vector of a linear transformation such that when that linear transformation is applied to it, it changes by a scalar factor.
The Eigenvalue is the factor by which the Eigenvalue is stretched or scaled. The direction of an eigenvector is reversed when the Eigenvalue is negative.
Both are statistical measures used to study population samples. However, systematic sampling engages a fixed interval in creating samples from a larger population while cluster sampling involves the breaking down of the population into clusters before taking a random sample from each cluster.
Monitor all models, Evaluate the metrics of your models, compare models and rebuild or you can add a low percentage of negative test data as part of the model result and develop an auto-encoder model with training data before deploying the model.
It is also called a Type I error and it occurs when a non-event is classified as an event.
An observation lying at an abnormal distance from others in a sample can be treated by :
In the long format, each row represents an observation that belongs to a particular category.
In the wide-format, data in categories are always grouped in rows and columns.
It is an approach used to develop warehouses consisting of one or more tables about any number of dimension tables.
A Boltzmann machine is the one used to optimize the quantity and weight of a given problem. It has a simple learning algorithm.
Ensemble learning involves the use of multiple learning algorithms to have a better predictive performance than from a constituent learning algorithm alone.
The common types of ensembles are:
It is a statistical model that uses a logistic function to model a binary dependent variable in its basic form.
It is sometimes called a recommendation system and it is a filtering system that suggests relevant and important information to users out of a large volume of information.
A hyperparameter is a parameter that the value is set before commencing a learning process and it determines how a network is structured and trained.
Both of them are Python libraries used in numerical and mathematical analysis.
Cross-Validation is a model validation technique used primarily to estimate how a predictive model can perform accurately when put to practice. The technique is used for assessing and giving insights on how a model will generalize to an independent data set.
It is sometimes called a selection effect, such that when the selection of groups, data, or individuals are done for analysis, the samples obtained do not represent the population to be analyzed.
Dropout is a method of dropping out visible units of a network that are hidden randomly to avoid overfitting of data.
Machine Learning is used to devise complex models and algorithms that are used to make predictions. It explores the study and construction of algorithms that can make predictions on data.
It is an algorithm based on the Bayes theorem which describes the probable occurrence of an event based on existing knowledge of conditions that may be related to that event.
It is a technique that involves the selection of elements from an ordered sampling frame.
Tuples
It is a two by two [2x2] table consisting of four output results as given by the binary classifier.
The law of large numbers is a theorem that states that the sample means, standard deviation, and variance converge to what they are seeking to estimate.
To validate models by using random subsets or to change labels on data points when trying to perform a significant test.
They are simple learning networks that seek to convert inputs to outputs without significant errors i.e. output should be as close as possible to the input.
Convolutional Layer, ReLU Layer, Pooling Layer, and Fully Connected Layer.
Is a method for improving the performance and stability of neural networks by normalizing the inputs so that they have a standard deviation of 1 [one] and a mean output activation of 0 [zero].
Univariate data has only a variable and patterns can be studied using statistical measures.
Bivariate data involves two different variables and the relationship between them.
Multivariate data involves three or more variables as it consists of more than one dependent variable.
An Activation Function is useful for introducing non-linearity into a neural network helping it to learn more complex functions.
Decision trees are single structures while a Random forest makes up several decision trees collected together.
Because it helps in reducing space for storage and also in reducing the time for computation.
Batch Gradient Descent, Stochastic Gradient Descent, and Mini-Batch Gradient Descent.
It is a method of creating lists based on lists that are existing before. It is usually faster and more compact.
State the difference between extend() method and append () method
append() method is used for adding a new element to a list while the extend() method links the first list together with another list.
It is a variant of the Stochastic Gradient Descent and also one of the most popular optimization algorithms.
A Numpy array of dimensions
It involves the graphical representation of data and information using visual elements like tables, maps, etc.
A uniform distribution occurs when all the observations in a data set spread across the range of distribution equally. A skewed distribution is when more data sets appear on one side of a graph than the other.
Back Propagation is a training algorithm for a multilayer neural network.
It is a problem-solving technique used in discovering the root causes of problems or faults. It is useful for businesses to understand the reasons for the results obtained.
A Box-Cox transformation is a method of transforming non-normal dependent variables into a normal shape.
It is the process of gathering data and presenting it in an organized and summarized form.
Dataframe, Panel, and Series.
Deep Learning is a subfield of Machine Learning and it deals with algorithms inspired by the structure and functions of Artificial Neural Networks.
Machine learning on the other hand is an aspect of computer science that allows computers to learn without explicitly being programmed.
It can be used to group large amounts of data and compute operations on the groups.
Use series.isin() in Pandas
It is also called ‘hypertree' and it involves the visualization of data inspired by hyperbolic geometry.
Cluster Analysis involves grouping a set of objects in a way such that similar objects are placed together in a group or clusters.
purr for Data Wrangling, dplyr for Data Manipulation, Ggplot2 for Data Visualization, Hmisc for Data Analysis
Data Mining is a subfield of computer science and it involves the extraction of information from a data set and transform it into an understandable form for reference purposes.
A treemap is a powerful visualization used for illustrating hierarchical data like tree structures and part to whole relationships.
A heat map is great for comparing categories while using colors and sizes.
Aggregation of data simply refers to the compilation and summarization of data while disaggregation involves the breaking down of data into smaller components.
The LSTM Network means Long-Short-Term Memory and it is a special kind of recurrent neural network capable of learning long term dependencies and remembering information for a long time.
The LTSM network can decide what to forget and what to remember. Then, it updates cell state values selectively and decides what part of the current state will make it to the output.
When the mean and variance of the series are constant with time.
Feature vectors are used to represent numbers or symbols of an object in a mathematical way that is easier to analyze.
No, not always.
When the underlying data source is changing, when you want the model to evolve as data streams through a system, and when it is not stationary.
Survivorship Bias occurs when attention is only paid to processes that support surviving while ignoring those that do not because of their lack of prominence. Often, it generates wrong conclusions.
They are particular sets of algorithms that have revolutionized machine learning.
By resampling the data to estimate the model accuracy and having a dataset to validate and evaluate the model.
A Confounding variable is one that influences a dependent variable alongside an independent variable.
A ROC curve is a graphical representation that shows the difference between false-positive rates and true positive rates at various threshold levels.
Cluster Sampling is an alternative that can be used to study a large population when simple random sampling cannot be effective. Samples are collected in clusters.
Epoch refers to one iteration over an entire data set and Batch refers to splitting a dataset into batches since the data set cannot be passed into a neural network at once.
It is a type of Artificial Neural Network [ANN] that is designed to recognize patterns from data sequences like time series, etc.
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