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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
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
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

An Introduction to Statistics For Data Science: Basic Terminologies Explained

Ananya Chakraborty
Ananya Chakraborty Last Updated : 04 May, 2021
5 min read

This article was published as a part of the Data Science Blogathon.

Are you an aspiring data scientist who wants to learn statistics for data science purposes? Did you find statistical concepts hard in your school and you are looking for easier ways to learn the statistical concepts to improve your capabilities of understanding the data now? If your answer is yes to both then you have come to the right place. Today, we introduce you to the concept of statistics that is widely accepted in the data science domain. Before learning the concepts it’s important to know what you can expect to learn.

 

Statistics For Data Science image

Introduction to Statistics And Machine Learning:

What is Statistics? What are the types of statistical concepts you should know?

Statistics is one of the popularly known disciplines that is mainly focused on data collection, data organization, data analysis, data interpretation, and data visualization. Earlier, statistics was practiced by statisticians, economists, business owners to calculate and represent relevant data in their field. Nowadays, statistics has taken a pivotal role in various fields like data science, machine learning, data analyst role, business intelligence analyst role, computer science role, and much more.

While we are introduced to certain statistical concepts like central tendency and standard deviation much earlier. There are many more important statistical concepts that we need to learn and implement for data science and machine learning. Let’s learn about the basic terminologies of statistics and their categories.

Basic Terminologies in Statistics:

To become a master in the statistical program we should be familiar with certain terminologies. They are:

  • Population: A population is the set of resources from where we can collect data
  • Sample: A Sample is nothing but a subset of the Population which is used for sampling of data and in inferential statistics to predict the outcome.
  • Variable: A Variable can be a number, a characteristic, or a quantity that can be counted. It can be also called a data point.
  • Probability Distribution: A probability distribution is a mathematical concept that primarily gives the probabilities of occurrence of different possible outcomes generally for an experiment conducted by statisticians.
  • Statistical Parameter: Statistical or population parameter is basically a quantity that helps in indexing a family of probability distributions like the mean, median, or mode of a population.

Types of Statistics Concepts:

 

Types of Statistics Concepts data science
  1. Descriptive Statistics -Descriptive statistics is a concept that allows us to analyze and summarize data and organize the same in the form of numbers graph, bar plots, histogram, pie chart, etc. Descriptive statistics is simply a process to describe our existing data. It transforms the raw observations into some meaningful data that can be further interpreted and used. Concepts like standard deviation, central tendency are widely used around the world when it comes to learning descriptive statistics.
  2. Inferential Statistics – Inferential statistics on the other hand is an important concept that deals with drawing conclusions based on small samples collected from the entire population. For example, during an election poll, people will often want to predict the exit poll results so they will conduct a survey in various parts of state or country and record their opinion. Based on the information they have collected they tend to draw conclusions and make inferences to predict results for the entire population.

Now, that we know the types of statistics, it is quite important to admit the pivotal role of statistics concepts in data science and machine learning, and how both are two closely related areas of study. Data Science Statistics actually helps us in selecting, evaluating, and interpreting predictive models for data science use cases.

 

Statistics and Data Science

Statistics and Data Science

The core concept of machine learning & data science is entirely built around statistics. Hence, it is important to learn the fundamentals of statistics thoroughly to solve real-world problems.

If you weren’t comfortable with statistics before, then we will explain certain concepts you need to master in order to ace your data science journey. You need to comfortable while learning mathematical equations and statistical formulas and theories to know what to apply where. It is hard no doubt but it is worth learning the subject.

Starting from exploratory data analysis until designing hypothesis testing, statistics play a crucial role in solving many problems across various industries and sectors, especially for data scientists.

 

Why should you master statistics concepts?

Nowadays, almost all companies have become data-driven and are using various concepts to interpret their existing data. That’s where fundamental statistical concepts come into play & their implementations help us in describing the data that we have in hand.

To solve the ongoing problems in the company and predict a better strategy to improve the profit margin of the company we need to learn concepts that help us understand the data and categorize it according to their features. Thankfully, statistics has a set of tools that help us organize and visualize the data and provide actionable insights.

Hence, it has become crucial to master statistical concepts at this point in time. There are plenty of online courses and books that are available to help us better our knowledge and become better data scientists.

 

How to Make Sense of the Current Data

Data is essentially nothing but a collection of observations that are present in our company system. With help of descriptive statistics, we can collect, organize, categorize, sample, visualize the data to make informed decisions for the company.

We can also use inferential statistics to predict outcomes. Generally, this concept is used when we are conduct surveys or doing market research, we tend to collect samples of data, and based on that we predict the findings for the entire population of that particular location.

Here are certain concepts that you need to master to become a better data science practitioner:

✔You need to calculate and apply measures of central tendency to grouped and ungrouped data.

✔You need to be comfortable in summarizing, presenting, and visualizing data, in a way that the reports obtained are clear and provide practical insights to the stakeholders and business owners of your company.

✔You also need to perform hypothesis tests that are required to use for common data sets.

✔Conduct rigorous correlation tests, and regression analysis to make send of the data.

✔Implement statistical concepts using R & Python and demonstrate your proficiency in this program.

✔ Become proficient in tools like Excel, Tableau, Power Bi to represent the data in a proper format

 

What is the Significance of Statistics in Our Daily Life?

What is the Significance of Statistics in Our Daily Life?

Luckily for us, statistics can help us answer important questions about data like:

  • Among the available data points which features are important for us to develop the model.
  • What is the best way to conduct the experiment?
  • How to design a strategy based on the outcomes of the experiment?
  • What performance metrics should you focus on?
  • How to interpret the outcome?
  • How to differentiate between valid and noise data?

All these are common and important questions that hold statistical significance, and the data teams have to answer them to perform their tasks better.

These were some of the pointers you ought to know to get started with the statistical program. There are plenty of courses out there which help you improve your knowledge and become a better professional.

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

Ananya Chakraborty
Ananya Chakraborty

A passionate business analyst who analyzes people and data alike! I am also working my way into data science. Taking one step at a time, learning one skill at a time to be able to deliver my quality work consistently! E-Commerce, Digital Marketing, Health and Finance are some of the domains I hold expertise on!

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