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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

What Data Science Future Looks Like?

Anant
Anant Last Updated : 16 Apr, 2021
6 min read
This article was published as a part of the Data Science Blogathon.

” Opportunities don’t happen you create them”

Data Science isn’t something new anymore. Data Science is the result of facts where the insights out of data and mathematical approach combined together to form an automatic solution for existing problems. Another important aspect of Data Science is whether Data Science is sufficient to provide enough opportunities for everyone.

 

Data Science for Future

When someone first thought about something which can help everyone to communicate with each other miles away with just a few clicks. People all-around him must think of him to be a lunatic at that time. Similarly, when we talk about the possibilities of Data Science it might seem to be impossible or maybe a mad man to think about those but that’s how the future is built.

 

Data Science for Future

Data Science has tremendous applications not just limited to one field. Its applications are distributed across various sectors. Let’s talk about few major future developments in Data Science:

  1. Automobile Industry: Automobile Industry saw a major shift within the last few years and still in the development stage. Self Driving cars, Autopilot flying cars, Fixed Destination Cabs, Automatic Public Transport, and various other applications.

    These things are possible in the coming future. However, such developments require a large group of passionate people to not just build code but also think of, added advantages that Data Science can bring which earlier was not there. Hence Automobile Industry is a new powerhouse to jobs and opportunities in Data Science.

  2. IT: Most people confuse Data Science with IT and its services. But the fact is Data Science is pure mathematical capabilities combined with the wonders of Software Engineering to developing what we call today – Machine Learning. IT sector has shown enormous growth in the world’s GDP. However, when we talk about the IT sector as a whole Data Science is now becoming a key aspect of any successful data-driven company.

    When we talk about whether introducing new changes to the website or existing app will bring new customers or will lose its customers. Then, Data Science becomes a very important part to identify what impact will new changes have. Data Science has various other applications in the IT sector, including Network Safety also.

  3. Healthcare: The biggest application or wonder of Data Science is in Healthcare. With the availability of large datasets of patients, we can use that to build a Data Science approach to identify the diseases at very early stages. Healthcare is one of the biggest sectors for providing opportunities for the professional who can use their medical expertise with Data Science and provide immediate help to the suffering patients. Healthcare provides various other opportunities as well by combining a Data Science approach to identify required organs and their availability in the region of the world.
  4. Army and Weapons: Every Nation has built stronger from a stronger army strength. It’s a wise man’s sayings that power isn’t something that should be used for making mankind slaves. It must be used to free mankind from any threats. Justifying the fact Data Science can help in building various automated solutions to identify any attack in a very early stage helping to stop the cause. Other than that Data Science can help in building automated weapons that will be smart enough to identify when to fire and when not to.
  5. Power and Energy: With the rapid growth in population the demand for energy has increased exponentially. That requires Nuclear Energy to be handled to such a level that without exhausting the existing Natural Resources we must be capable to provide energy demands fulfilled. Data Science can help in predicting the effects of Nuclear Power sources.
    Data Science can predict the maximum safest potential. Data Science can help in building AI bots that can easily handle enormous power sources.
  6. Banking and Finance: When we talk about the security of our money we always think of the bank. But with the introduction of online transactions, fraud had increased as well. Banking and financial data along with security require stable systems to identify fraud activities before they can actually cause damage. Another aspect of Data Science in the Banking and Finance sector is managing the money effectively to have invested in the right places based on Data Science predictions for best results.

    The biggest innovation of time is Cryptocurrency. With cryptocurrency in the market, the demands for managing data online have become a major challenge. Data Science offers various techniques to identify a similar group of people and providing them the best possible security from fraud activities as well.

Is Data Science a beginning or and end ?

When it comes to choosing right and wrong people often get confused about whether to take a step forward or not. In this confusion they lose the most precious thing- Time. So, to correct their myth that with enormous automatically driven Data Science solutions out in the market it will cost lots of layoffs. It is something which takes us back instead of taking us step ahead in the future.

With Data Science-based solutions we need regular maintenance. We need brains to identify the correct changes in the existing solutions for further betterment. Along with the opportunities Data Science might create. It brings us to another part that it can also ease our job by providing complete support. We can explore our Space and also can find out what the mystery of our universe is. So data science isn’t the end, it’s a beginning of a new era.

data science future data science beginning

Deep Learning

Most people argue that Data Science isn’t in a very stable position to bring up a new future. But have they ever thought about Deep Learning. Deep Learning is a very important part of Data Science. It brings the reality closer to the Virtual reality.

 Let’s talk about the complex actions occurring in our brain.

For Example: If we talk about a man who always learns from his mistakes and tries not to do them again. That’s exactly how Deep Learning works. Your Machine Learning might make some mistakes, but Deep Learning helps in rectifying with time leading to something called a closeness to reality.

deep learning data science future

The significance of Deep Learning is exactly the same for any Data Scientist as the importance of the Logical Unit within our brains.

Data Science In Industry

At the current stage. Data Science has been already in action and to the levels from where we cannot think of taking a step backward. From searching your favorite series on Netflix and getting similar recommendations for getting similar advertisement for whatever you are looking for on the Internet.

Our world is driven by Data Science because in every single Google search we trigger a Data Science process. With the recommendations of what to buy based on other similar users to the recommendations based on products we bought in the past, we all are captured with the Data Science solutions.

Data Science In Industry

Data Science is not just limited to IT but also its applications our presence in the Automatic Vehicles that are running in some places. Along with that Data Science bring completeness in Telecommunication sector as well. Nowadays we see most of the tickets being raised at regular intervals get solved immediately in a very minimal time. That’s how the Data Science helps in driving the world to the next levels.

 

Conclusion

We are already aware of the fact that the innovators are not the ones who see the shortcomings of something. They are the ones who see the future and try to adapt accordingly. With so much to explore Data Science brings a huge lot of opportunities in almost every sector, making not just a big bubble but also able to solidify the actions with the scope of future improvements.

Mathematics is the key to Data Science because only someone who understands the science behind the number can identify the future of what’s coming next. Hence, Data Science is not just for Data scientists but everyone who is ready to contribute in the future.

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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