Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

20 Powerful Images which perfectly captures the growth of Data Science

A
Analytics Vidhya Last Updated : 19 Jan, 2016
6 min read

Introduction

Data can’t make your past better. However, it you surely can create an awesome future.

In recent years, companies have invested millions of dollars in the field of data science. They have shown immense faith in its potential to create a better world, a better life and a better future.

The powerful trio of mathematics, computer science and domain expertise has re-defined the process of decision making. Intuition or gut no longer remains the key to make complicated decisions.

What was considered to be path breaking invention few years back, has now become obsolete. Data Science has empowered us with possibilities beyond imagination. Over the years, lots of things have decayed & evolved. Still, the best of technology is yet to come. I’m excited to see it in front of my eyes!

20 powerful images which perfectly captures growth of data science

 

What do these images show ?

We all know images are easy to comprehend and conveys more information than text. Basically, these images depict the journey of data science as a field. These include the developments, inventions, achievements and everything that has made an impact on our daily lives.

You talk about politics, economics, science, life, sports, almost everything is blessed by services of data science. And, I’ve tried to captured the best of them in these pictures.

Hope you like them!

 

20 Pictures

1. This event marked the beginning of ‘data scientist’ revolution. After this research report got published, the world immediately acknowledged the ‘potential’ needed to make sense out of the data.

data scientist 21st century

 

2. Mckinsey further fueled this revolution with their research report on growing imbalance in demand and supply of talent with analytical talent. Though, this was focused only on US market, but its ripples were felt in worldwide industries. (Image Source: McKinsey Report)

mckinsey_chart

 

3. This came as the first breakthrough in Artificial Intelligence. IBM created its first artificially intelligent computer IBM Watson. It was created to compete in the popular game show ‘Jeopardy’. Eventually, Watson defeated two of these greatest Jeopardy champions (Ken & Brad). This marked the beginning of next level artificial intelligence.17jeopardy_337-span-articleLarge

 

4. Data science plays a significant role in baseball. This caused sudden up surge in the demand and availability of players data near 2000. It is said that the years after 2000 marked the beginning of data revolution in baseball. (Image Source : Datanami )baseball

 

5. In 2009, Netflix organized a competition to improve the accuracy and relevance of its content recommended system. This team solved the mystery. Won $1,000,000 prize money. Surely, this is one of the most notable event in data science history.netflix-prize1

 

6. This is statistics at its best. Peter Brand (Moneyball) explains statistically the number game which can get them wins in the upcoming baseball season. His popular dialogue “In baseball, the goal shouldn’t be to buy players. The goal should be to buy wins. In order to buy wins, you need to buy runs !”

moneyball

 

7. In 2012 Presidential Elections, Nate Silver correctly predicted 50 out of 50 states. He used probability, graph theory, bayes theorem techniques to achieve this feat. This level of accuracy brought using statistics completely changed the way making political predictions.
nate

natesilver2012

 

8. Will this be the decade of Deep Learning ? This image shows the number of deep learning projects taken by google in last 4 years. (Image Source : Bloomberg)

googledeep

 

9. It’s a competition between Google Image Recognition Software and a Soduku Champion (human). While the champion does her analysis and figures out the right numbers, google goggles solves it within seconds.

googlegoggle

 

10. Industrial Robots in action in a car manufacturing factory. These robots provide high precision, low error margin and faster response rate than humans. Auto manufacturing industry is pacing towards this automated form of labor.

indrobo

 

11. Record Stress. Yes! Stress can also be recorded in form of data. Now, there are several devices and applications available in the market which can record stress level and predict probable health issues.

data

 

12. We talk about new methods of data collection, here’s another one. Companies have created apps & software to track your fitness levels and recommend health products accordingly. Everything you do today, generates data.

data1

 

13. Expedition made easier. This Image recognition software translates text instantly. You no longer need to face troubles of unfamiliar languages which travelling to a new country.

tech

 

14. In 2012, another breakthrough research happened in the field of data science. At google, a computer learnt to identify cats using a Neural Network created using 16,000 computer processors.

cat

 

15. Unmanned Aerial Vehicle (UAV), also known as Drones are aircrafts which are pre-programmed for a certain mission. It can be security (spy camera), delivery of goods, monitoring etc. It’s an advanced way of producing data which was earlier considered to be difficult to capture.

drones

 

16. Self Driving Cars. Researches from Google, Baidu, Ford are diligently working on this project. This is a perfect example of a machine which learns from its surroundings. You see, this car detects a person crossing the road ?

self-driving

 

17. Every moment, artificial intelligence is becoming better to challenge human intuition. Robots can now look & think like humans. However, extra sensory perception yet remains a challenge. Don’t get baffled if you see lot of these species in our surroundings in near future.

ex-machina

 

18. Internet of Things promises an incredible future for human beings. Creating a network of connected devices will make human lives faster and more convenient. (Image Source: The Connectivist)

R437LRP

iot2

 

19. Machine Learning will now promote life. Google continues to inspire humans with their projects. This is google project Verily. It aims to make robots better surgical assistant using machine learning and advanced image processing. (Image Source: Verily)

machine learning for life science

 

20. Google Trends shows a promising growth of Data Science in 2016 also.

trendgoogle

 

End Notes

This article is about 20 pictures which depicts the remarkable growth of data science and machine learning. Nobody wondered that the blend of data and technology could design a fantastic future. Yet, this decade will experience this future.

One thing that I learnt while compiling this article is, if someone wants to enter data science and analytics, you can’t wait for a better time. This is it. Do it now. Companies are furiously hunting for talented candidates. Be the one.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

 

deep learningdronesibm watsonimage recognitionInternet of thingsmachine learningnate silvernetflixNeural networkself driving carsstatistics
A
Analytics Vidhya

Analytics Vidhya Content team

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

hossein
hossein

I think the images were selected intelligently in all area like industry,farmacy ,... so everyone would be satisfy to pick data science as a future career thank u Manish Hossein

123

Analytics Vidhya Content Team
Analytics Vidhya Content Team

It's good to know. Welcome Hossein :)

123
Susrenitha Tummala
Susrenitha Tummala

Hello Analytics Vidhya Team, Greetings! Iam Susrenitha, a Data Science professional. I deligently go through all your posts / articles / discussions. They are Fantastic, The way you are paying attention to every detai is highly commendable. Kudos to your team. Have a quick feedback- I had already signed up for AV. but still everytime I open this site, I keep getting the pop-up to signup. although it is ignorable, I being a regular find it little uncomfortable. Hope you'll look into this Best Regards, Susrenitha

123

Saïd
Saïd

I love the content and the format of this post. An image is worth a thousand words. What about 20 images?

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
What Data Science Future Looks Like?
What Data Science Future Looks Like?
Understanding Data Science from a Beginner’s Lens
Understanding Data Science from a Beginner’s Lens

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details