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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Statistics for Data Science

Anant
Anant Last Updated : 06 Apr, 2021
4 min read
This article was published as a part of the Data Science Blogathon.

It is the mark of truly intelligent person to be moved by Statistics.

The most important aspect of any Data Science approach is how the information is processed. When we talk about developing insights out of data it is basically digging out the possibilities. Those possibilities in Data Science are known as Statistical Analysis.

Most of us wonder how can data in the form of text, images, videos, and other highly unstructured formats get easily processed by Machine Learning models. But, the truth is we actually convert that data into a numerical form which is not exactly our data but the numerical equivalent of it. So, this brings us to the very important aspect of Data Science.

With data in numerical format, it provides us with infinite possibilities to understand the information out it. Statistics acts as a pathway to understand your data and process that for successful results. Not only the power of statistics is limited to understanding the data it also provides methods to measure the success of our insights, getting different approaches for the same problem, getting the right mathematical approach for your data.

Importance of Statistics for Data Science

Most Data Scientists always invest more in pre-processing of data. This requires a good understanding of statistics. There are few general steps that always need to be performed to process any data.

  1. Identify the importance of features by using various statistical tests.
  2. Finding the relationship between features to eliminate the possibility of duplicate features.
  3. Converting the features into the required format.
  4. Normalizing and scaling the data. This step also involves the identification of the distribution of data and the nature of data.
  5. Taking the data for further processing by using required adjustments in the data.
  6. After processing the data identify the right mathematical approach/model.
  7. Once the results are obtained the results are verified on the different accuracy measurement scales.

The data processing from the beginning to the end of the complete cycle there is a requirement of statistics at every single step. That’s why a good statistician can be a good Data Scientist as well.

 

Guide to Learn Statistics

It is always required to understand every fundamental of Statistics. However, most people are not very clear about where to start.

These are the few key concepts that are required to pace up and understanding the fundamentals of Statistics for Data Science:

Probability

Probability is the basic need for understanding the possibilities. To start with let us take a very basic example – What are the chances that Team A is going to win the football match against Team B. To derive this answer we might require 100 people to give their respective votes – Number of Samples. Based on those votes we can have a chance of which team can win the game.

But, in this example, we come across another very important concept which is known as sampling – identifying the right set of people to vote for the results. So, the probability is the chance of whether the event will occur or not. Depending upon the scenario we can build different solutions around this.

 

Sampling

The sampling as we discussed in the above example is identifying the right set of people. The question is what are the right set of people. Let’s continue our previous example for the above scenario we need those 100 people who have a good knowledge of Football, who knows about the history of Team A and B, Who should not be biased towards a team because of their personal preference. So, identifying the right sample can be done by various statistical approaches. There are various types of sampling methods – Simple random sampling, Systematic sampling, Stratified sampling, Clustered sampling, etc.

 

Tendency and Distribution of Data

The distribution of data is a very important aspect. The famous distribution like Normal Distribution is very significant. For example, when we talk about the distribution of height and weight of the world it is normally distributed data showing the symmetry of nature. The normal distribution has to Mean, Mode, and Median all coincide at the central peak. These data are supposed to be very accurate data. So identifying the distribution and skewness of data is a very important concept.

 

Hypotheses Testing

If we know whether to perform some action or not. Will those actions give a positive result or a negative result then we can have an added advantage of doing the right things. Hypotheses Testing gives identifying the situation where the action should be taken or not based on what results will it produce. There are other tests as well like A/B Testing, Z Test, T-Test, Null Hypothesis with similar relevance.

 

Variations

When we talk about different variations in data. We talk about the distortion, error, shift in data. Along, with the variations in data the range of the data, relationship within the data. All these accounts for the variability of data. Some of the key terms to understand here are – Variance, Range, Standard Deviation, Error Deviation, Covariance, Correlation, Causality, etc.

 

Regression

The regression in simple terms is finding out a relationship between the independent and dependent variables. Regression can be of two types broadly  – Linear Regression, Multi Linear Regression.

Linear Regression – Y = aX + C

Multi Linear Regression – Y = aX + bX1 + cX2 + …. + C

Statistics is a wide concept limiting not just to what exists but what can be derived out of existing techniques to build something new. Hence, Statistics is very important for Data Science as it helps to understand existing solutions as well as digging out new developments.

There is always a way to do it better – find it and become and innovator

Statistics data science

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Anant
Anant

I am a Data Science expert with excellent understanding of NLP and its applications. I work in Finance domain supporting Data Science use cases and mentor people who are interested in Data Science and Analytics

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