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Reading list

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 Introductory Guide to Big Data Analytics

Gourav
Gourav Last Updated : 06 Sep, 2021
6 min read

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

One thing that comes to our mind after hearing Big Data Analytics is that this field might be somewhat related to Data Science right? So starting with the answer, yes it’s related to the same the only difference is, in Big Data Analytics, analysts work on a huge amount of data. Big Data Analytics refers to data insights. It is the process of finding important data, patterns, market trends, relationships, and user needs. With the advancement of technology, it is quite possible to find and study any information and get the reports very quickly. The basic purpose is to provide better decision-making and to stay safe from fraudulent activities. This technology has many characteristics like big volume, faster rate. Artificial Intelligence (AI), mobile, social media, and the Internet of Things (IoT) are operating data through new sources of data.

The concept of Big Data analytics was designed years ago. Most of the companies came to know that if they will store and collect the information related to their business, then after analyzing the data they can change their methods, to have better results. In the early 1950s, when the word big data was not even found, at that time many companies used this method. In the form of spreadsheets and in the form files which were examined by the people manually to find what is trending in the market. Speed and efficiency are the main advantages of this technology. The main benefit that companies are getting is that they have the potential to work at a faster pace.

How does Big Data Analytics work?

The working of BDA can be summarised in eight simple steps, which are as follows:

Step 1: Business case estimation– It is the first step in which the needs and goals of a company are analyzed, this step is also referred to as requirement analysis as in this stage we need to gather all the information about the requirement for which we need the BDA solution.

Big Data Analytics
Image 1 

Stage 2: Recognition of information– At this stage, many sources that can provide a lot of information are seen and recognized. This step is also called as Data Gathering stage as we have to gather all the data from different sources in order to create a solution for the problem. This is the sole stage of the pipeline without which the whole BDA solution won’t work as without data there would be no insight.

recognition of information | Big Data Analytics
Image 2

Step 3: Data refining– Here, undesired data which was collected from the previous stage is removed. Sometimes data that we collect from multiple sources may not be compatible and also may not be of any use to us so we need to remove that data to tackle the compatibility issues in the future.

Data refining | Big Data Analytics
Image 3

Stage 4: Data production– The data which is not appropriate is taken and then it is changed to a worthy form. Here the data is cleaned according to the usecases and algorithms.

data production | Big Data Analytics
Image 4 
 

Step 5: Data collection– Same data from different sheets are combined to remove the extra space.

data collection | Big Data Analytics
 Image 5

Stage 6: Data scanning– Here comes the role of logical and mathematical tools. Now all the data is examined to extract useful information.

Data scanning
Image 6

Step 7: Perceiving data– The company strategist now puts the data into a graphical and systematic form using tools like Tableau, etc.

perceiving data
Image 7

Stage 8: Final examination outcome– This is the final step, in which the final report is given to the business stakeholders and owner who can take mandatory steps.

Benefits of the Technology

This technology helps organizations and companies to collect the data of their competitors. As to make new strategies to beat the competition. Also, it guides to perform best business moves. Moreover, this can increase the profit and will be more convenient.

Advantages
Image 8

More Advantages are as follows:

  1. Risk management: A banking company in San Nicolas “Banco de Oro” uses this technology to protect their money from fraudulent activities. This bank also reduces fraud activities by analyzing many aspects and finds the root cause.
  2. Product Development and Innovations: Everyone in the world has heard about “Rolls-Royce”. One of the biggest producers of jet engines, cars, and armed vehicles uses this to find the capacity and capability of their engines. Also, they examine whether any changes are needed or not.
  3. Quicker and Better Decision Making Within Organizations: Starbucks is one of the popular brands that provide the best quality drinks also uses this to make smart moves to increase their sales. For example, if Starbuck wants to open a new outlet, they will study the location where they can open. They will also examine some different elements like population, landmark, and many more.
  4. Improve Customer Experience: Delta airlines is a major airline company in the United States that uses these stats to improve its customer experience. This company scan and studies the tweets of all the passengers that traveled in their airlines. Whenever they find any negative tweet related to the staff and journey of the passenger they take necessary steps to solve that issue.

Applications of Big Data Analytics

With the advancement of technology, the demand for data management experts has increased in companies like Software AG, Oracle Corporation, IBM, Microsoft, SAP, and Dell, etc. These companies have spent more than $15 billion on organizations having experience in data analytics and management. In 2010, this industry was having a worth almost $100 billion and was growing at a faster pace with an increment of approximately 10 percent every year. It is almost twice as fast as the software work. Industry specialists, academicians, and many other important contributors accepted that this technology is a big game-changer.

Applications
Image 9

This is the list of the top 10 industries that are using Big Data Analytics:

  1. Government
  2. Education
  3. Healthcare Provider
  4. Banking and Securities
  5. Communications, Media and Entertainment
  6. Manufacturing and Natural Resources
  7. Insurance
  8. Retail and Wholesale trade
  9. Transportation
  10. Energy and Utilities

Popular Tools for Big Data Analytics

Here is a list of tools that are useful in big data analytics:

  1. Hadoop- This tool helps in reserving and studying information.
  2. MongoDB- It is used on datasets that change constantly.
  3. Talend- It is helpful in data combining and controlling.
  4. Cassandra– It is an assigned database use to manage chunks of data.
  5. Spark– It is helpful in immediate changing and studying a large quantity of information.
  6. STORM– This is an open-source actual computing arrangement.
  7. Kafka– This is a distributed running program that keeps fault-tolerant data.

Conclusion:

This was the brief intro about BDA, which can help in the improvement of multiple real-life sectors. The process of BDA is similar to those of Data Science and Machine Learning, the only difference here is that we get data from multiple sources and in huge amounts.

Hope this article gave you a head start in order to go deeper into the concept of Big Data Analytics.

Thanks for reading this article do like if you have learned something new, feel free to comment See you next time !!! ❤️ 

References

  1. Image 1 – https://www.workflowmax.com
  2. Image 2 –  https://www.eff.org/
  3. Image 3 – https://www.linkedin.com
  4. Image 4 -https://analyticsindiamag.com
  5. Image 5- https://medium.com
  6. Image 6- https://www.shutterstock.com
  7. Image 7- https://tdwi.org
  8. Image 8- https://www.promptcloud.com
  9. Image 9- https://www.scientificworldinfo.com
The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.
Gourav
Gourav

Applied Machine Learning Engineer skilled in Computer Vision/Deep Learning Pipeline Development, creating machine learning models, retraining systems, and transforming data science prototypes to production-grade solutions. Consistently optimizes and improves real-time systems by evaluating strategies and testing real-world scenarios.

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