Hey, Data Science Learner!
Welcome to this Article, If you are completely beginner in the field of Data Science then there is no problem at all.
In this Article you will get to know 20 Core Data Science Concepts
Which a beginner must know.
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| 20 Data Science Concepts |
1_ Dataset
Just as the name implies, data science is a branch of science that applies
the scientific method to data with the goal of studying the relationships
between different features and drawing out meaningful conclusions based on
these relationships. Data is, therefore, the key component in data science. A
dataset is a particular instance of data that is used for analysis or model
building at any given time. A dataset comes in different flavors such as numerical
data, categorical data, text data, image data, voice data, and video data. A
dataset could be static (not changing) or dynamic (changes with time, for
example, stock prices). Moreover, a dataset could depend on space as well. For
example, temperature data in the United States would differ significantly from temperature
data in Africa. For beginning data science projects, the most popular type of
dataset is a dataset containing numerical data that is typically stored in a
comma-separated values (CSV) file format.
2_ What is Data Wrangling
Data wrangling is the process of converting data from its raw form to a
tidy form ready for analysis. Data wrangling is an important step in data
preprocessing and includes several processes like data importing, data cleaning,
data structuring, string processing, HTML parsing, handling dates and times,
handling missing data, and text mining. The process of data wrangling is a
critical step for any data scientist. Very rarely is data easily accessible in
a data science project for analysis. It is more likely for the data to be in a
file, a database, or extracted from documents such as web pages, tweets, or
PDFs. Knowing how to wrangle and clean data will enable you to derive critical
insights from your data that would otherwise be hidden.
3_ What is Data Visualization
Data Visualization is one of the most important branches of data science.
It is one of the main tools used to analyze and study relationships between
different variables. Data visualization (e.g., scatter plots, line graphs, bar
plots, histograms, qqplots, smooth densities, boxplots, pair plots, heat maps,
etc.) can be used for descriptive analytics. Data visualization is also used in
machine learning for data preprocessing and analysis, feature selection, model
building, model testing, and model evaluation. When preparing a data
visualization, keep in mind that data visualization is more of an Art than
Science. To produce a good visualization, you need to put several pieces of
code together for an excellent end result.
4_ Data Scaling
Scaling your features will help improve the quality and predictive
power of your model. In order to bring features to the same scale, we
could decide to use either normalization or standardization of features.
Most often, we assume data is normally distributed and default towards
standardization, but that is not always the case. It is important that
before deciding whether to use either standardization or normalization,
you first take a look at how your features are statistically distributed.
If the feature tends to be uniformly distributed, then we may use
normalization (MinMaxScaler). If the feature is approximately Gaussian,
then we can use standardization (StandardScaler). Again, note that whether
you employ normalization or standardization, these are also approximative
methods and are bound to contribute to the overall error of the model.
5_ What is Principal Component Analysis (PCA)
Large datasets with hundreds or thousands of features often lead to
redundancy especially when features are correlated with each other.
Training a model on a high-dimensional dataset having too many features
can sometimes lead to overfitting (the model captures both real and
random effects). In addition, an overly complex model having too many
features can be hard to interpret. One way to solve the problem of
redundancy is via feature selection and dimensionality reduction techniques
such as PCA. Principal Component Analysis (PCA) is a statistical method
that is used for feature extraction. PCA is used for high-dimensional
and correlated data. The basic idea of PCA is to transform the original
space of features into the space of the principal component. A PCA
transformation achieves the following:
a) Reduce the number of features to be used in the final model by
focusing only on the components accounting for the majority of the
variance in the dataset.
b) Removes the correlation between features.
6_ What is Data Imputation?
Most datasets contain missing values. The easiest way to dealwith missing
data is simply to throw away the data point.However, the removal of samples or
dropping of entire feature columns is simply not feasible because we might lose
too much valuable data. In this case, we can use different interpolation techniques
to estimate the missing values from the other training samples in our dataset.
One of the most common interpolation techniques is mean imputation, where we simply
replace the missing value with the mean value of the entire feature column.
Other options for imputing missing values are median or most frequent (mode),
where the latter replaces the missing values with the most frequent values. Whatever
imputation method you employ in your model, you have to keep in mind that
imputation is only an approximation, and hence can produce an error in the
final model. If the data supplied was already preprocessed, you would have to
find out how missing values were considered. What percentage of the original
data was discarded? What imputation method was used to estimate missing values?
7_ What is Outliers?
An outlier is a data point that is very different from the rest of the
dataset. Outliers are often just bad data, e.g., due to a malfunctioned sensor;
contaminated experiments; or human error in recording data. Sometimes, outliers
could indicate something real such as a malfunction in a system. Outliers are
very common and are expected in large datasets. One common way to detect
outliers in a dataset is by using a box plot. Outliers can significantly
degrade the predictive power of a machine learning model. A common way to deal with
outliers is to simply omit the data points. However, removing real data
outliers can be too optimistic, leading to non-realistic models. Advanced
methods for dealing with outliers include the RANSAC method.
8_ What is Linear Discriminant Analysis (LDA)?
PCA and LDA are two data preprocessing linear transformation techniques
that are often used for dimensionality reduction to select relevant features that
can be used in the final machine learning algorithm. PCA is an unsupervised
algorithm that is used for feature extraction in high-dimensional and correlated
data. PCA achieves dimensionality reduction by transforming features into
orthogonal component axes of maximum variance in a dataset. The goal of LDA is
to find the feature subspace that optimizes class separability and reduce dimensionality.
Hence, LDA is a supervised algorithm. An in-depth description of PCA and LDA can
be found in this book: Python Machine Learning by Sebastian Raschka, Chapter 5.
9. What is Data Partitioning?
In machine learning, the dataset is often partitioned into training
and testing sets. The model is trained on the training dataset and
then tested on the testing dataset. The testing dataset thus acts
as the unseen dataset, which can be used to estimate a generalization
error (the error expected when the model is applied to a real-world dataset
after the model has been deployed).
10_ What is Supervised Learning?
These are machine learning algorithms that perform learning by
studying the relationship between the feature variables and the known
target variable. Supervised learning has two subcategories:
a) Continuous Target VariablesAlgorithms for predicting continuous target
variables include Linear Regression, KNeighbors regression (KNR), and
Support Vector Regression (SVR).
b) Discrete Target Variables Algorithms for predicting discrete target
variables include:
• Perceptron classifier
• Logistic Regression classifier
• Support Vector Machines (SVM)
• Decision tree classifier
• K-nearest classifier
• Naive Bayes classifier
11_ What is Unsupervised Learning?
In unsupervised learning, we are dealing with unlabeled data or data of
unknown structure. Using unsupervised learning techniques, we are able to explore
the structure of our data to extract meaningful information without the
guidance of a known outcome variable or reward function. K- means clustering is
an example of an unsupervised learning algorithm.
12_ What is Reinforcement Learning?
In reinforcement learning, the goal is to develop a system (agent) that
improves its performance based on interactions with the environment. Since the
information about the current state of the environment typically also includes
a so-called reward signal, we can think of reinforcement learning as a field
related to supervised learning. However, in reinforcement learning, this
feedback is not the correct ground truth label or value but a measure of how
well the action was measured by a reward function. Through the interaction with
the environment, an agent can then use reinforcement learning to learn a series
of actions that maximize this reward.
13_ what is Bias-variance Tradeoff?
In statistics and machine learning, the bias-variance tradeoff is the
property of a set of predictive models whereby models with a lower bias
in parameter estimation have a higher variance of the parameter estimates
across samples and vice versa. The bias-variance dilemma or problem is
the conflict in trying to simultaneously minimize these two sources of
error that prevent supervised learning algorithms from generalizing
beyond their training set: The bias is an error from erroneous assumptions in
the learning algorithm. High bias (overly simple) can cause an algorithm
to miss the relevant relations between features and target outputs
(underfitting).
The variance is an error from sensitivity to small fluctuations in the
training set. High variance (overly complex) can cause an algorithm to
model the random noise in the training data rather than the intended
outputs (overfitting).
It is important to find the right balance between model simplicity and
complexity.
14_ What is Cross-validation in Data Science?
Cross-validation is a method of evaluating a machine learning model’s
performance across random samples of the dataset. This assures that any biases
in the dataset are captured. Cross-validation can help us to obtain reliable
estimates of the model’s generalization error, that is, how well the model
performs on unseen data. In k-fold cross-validation, the dataset is randomly partitioned
into training and testing sets. The model is trained on the training set and
evaluatedon the testing set. The process is repeated k-times.
The average training and testing scores are thencalculated by averaging
over the k-folds.
15_ What are Model Parameters and Hyperparameters in Data Science?
In a machine learning model, there are two types of parameters:
a) Model Parameters: These are the parameters in the model that must
be determined using the training data set. These are the fitted
parameters.
b) Hyperparameters: These are adjustable parameters that must be tuned
to obtain a model with optimal performance.It is important that during
training, the hyperparameters be tuned to obtain the model with the
best performance (with the best-fitted parameters).
16_ Evaluation Metrics
In machine learning (predictive analytics), there are several metrics that
can be used for model evaluation. For example, a supervised learning (continuous
target) model can be evaluated using metrics such as the R2 score, mean square
error (MSE), or mean absolute error (MAE). Furthermore, a supervised learning (discrete
target) model, also referred to as a classification model, can be evaluated
using metrics such as accuracy, precision, recall, f1 score, and the area under
ROC curve (AUC).
17_ Uncertainty Quantification
It is important to build machine learning models that will yield unbiased
estimates of uncertainties in calculated outcomes. Due to the inherent randomness
in the dataset and model, evaluation parameters such as the R2 score are random
variables, and thus it is important to estimate the degree of uncertainty in
the model.
18_ Math Concepts for Data Science
a) Basic Calculus:
Most machine learning models are built with a dataset having several
features or predictors. Hence, familiarity with multivariable calculus
is extremely important for building a machine learning model. Here are the
topics you need to be familiar with:Functions of several variables;
Derivatives and gradients; Step function, Sigmoid function, Logit function,
ReLU (Rectified Linear Unit) function; Cost function; Plotting of
functions; Minimum and Maximum values of a function
b) Basic Linear Algebra:
Linear algebra is the most important math skill in machine learning. A data
set is represented as a matrix. Linear algebra is used in data preprocessing,
data transformation, dimensionality reduction, and model evaluation.
Here are the topics you need to be familiar with:
Vectors; Norm of a vector; Matrices; Transpose of a matrix; The
inverse of a matrix; The determinant of a matrix; Trace of a Matrix; Dot
product; Eigenvalues; Eigenvectors
c) Optimization Methods:
Most machine learning Algorithms perform predictive modeling by
minimizing an objective function, thereby learning the weights that must
be applied to the testing data in order to obtain the predicted labels.
Here are the topics you need to be familiar with:
Cost function/Objective function; Likelihood function; Error function;
Gradient Descent Algorithm and its variants (e.g., Stochastic
Gradient Descent Algorithm)
19_ Statistics and Probability Concepts for Data Science
Statistics and Probability are used for visualization of features, data
preprocessing, feature transformation, data imputation, dimensionality
reduction, feature engineering, model evaluation, etc. Here are the topics you
need to be familiar with:
Mean, Median, Mode, Standard deviation/variance, Correlation coefficient
and the covariance matrix, Probability distributions (Binomial, Poisson,
Normal), p-value, Bayes Theorem (Precision, Recall, Positive Predictive Value,
Negative Predictive Value, Confusion Matrix, ROC Curve), Central Limit Theorem,
R_2 score,
Mean Square Error (MSE), A/B Testing, Monte Carlo Simulation
20_ Productivity Tools For Data Science.
A typical data analysis project may involve several parts, each including several
data file and different scripts with code. Keeping all the organized
can be challenging. Productivity tools help you to keep projects organized and
to maintain a record of your completed projects. Some essential productivity
tools for practicing data scientists include tools such as Unix/Linux, git and GitHub,
RStudio, and Jupyter Notebook.
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