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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

Data Science trends 2015 to help you plan your learning!

Kunal Jain
Kunal Jain Last Updated : 25 Jun, 2020
4 min read

2014 is coming to an end shortly. What a glorious year it has been for technology and data science! Constant change, better products, faster solutions, new technologies – all of this happened with breath taking pace. If activities in recent months are any thing to go by, next 12 months are going to be nerve wrecking as well.

While there are many posts talking about technology trends in 2015, there is none focusing on learning data science! I usually review my learning plan every few months and start of new year is definitely one such point. So, I thought it might be a good idea to pen down the trends on changing data science landscape for 2015. You can use them to prepare your learning agenda for 2015.

 

Whom are these trends for?

Let’s be clear – these trends are not tech trends. So, I am not predicting which technologies are going to grow or stay and which are going out.

I am listing trends on how I expect the demand for various skill sets will change over the next 12 months. The aim is to help people draft their learning agendas for the next 12 months.

Infographic depicting 2015 trends:

Data Science trends 2015

1.  Hadoop will become mainstream

Developments in Hadoop ecosystem were probably the technology highlight of 2014. I expect Hadoop skills to stay and become more hot as 2015 arrives and evolves. It will also continue to be a highlight for 2015, with a wide range of startups trying to make Hadoop more mainstream and accessible.

The shortage of Hadoop resources is set to increase further as more and more Organizations test out Hadoop eco-system. A must have on your learning agenda, especially so, if you hold some experience on data science under your belt. These skills might be the ones which propel your career at a different pace.

 

2.  Python will gain more momentum

Python has gained significant momentum in data science community over last couple of years. Libraries like NumPy, SciPy and Pandas have transformed applications of Python for data science. I expect this momentum to grow further this year. Python is very versatile and hence I expect the trend of increased momentum for Python is here to stay.

Please note that I am not sparking a Python vs. R discussion here. If you want to have a look at that, you can read my views here. I expect that R will also gain prominence, but the growth of Python adaption would be higher. So, if you are looking to learn one open source language for data science, my recommendation would be Python over R.

 

3.  Usage of NoSQL and in-memory databases will increase in popularity

The database technology has been evolving continuously over time. I still remember firing my initial queries on a Teradata powered database and being surprised at it’s speed over some of the traditional databases we were using at that time. Today NoSQL databases like MongoDB and Neo4j are revolutionizing this space again. These databases allow smarter and faster searches like never before.

This is further going to change with in memory databases like Aerospike, Mem-SQL and VoltDB are changing the way we think about data retrieval. What should it mean for your learning agenda? I would definitely include MongoDB or Neo4j, if you don’t know them and keep a close eye on how in memory database evolve in 2015.

 

4.  Ensemble modeling & deep learning will become more popular

Take any competition on Kaggle and the chances are that the winning algorithm was either an ensemble model or a deep learning algorithm. What bigger proof do you need about the applications of these techniques. A must learn, if you already don’t know them. If there is an inch of doubt on their effectiveness, go and check the Kaggle forums for yourself!

 

5.  Data Visualization and story telling will become a necessity

Gone are those days when you could rely only on a few people to narrate data stories. Today, every analyst needs to be a story teller. He needs to be able to think of best ways to visualizing and representing information such that huge data becomes easy to digest. Thankfully, tools like Tableau and QlikView / QlikSense come in handy here. Both of them have added a story telling feature to their product suite in 2014.

 

End Notes:

These were the trends I expect will play out in 2015. With less than 15 days remaining before 2014 ends, it might be the right time to have a look at what you need to learn in 2015. For the people, who like to keep (or try to keep) resolutions, there is enough food for thought here!

What do you think about these trends? Do you have any other suggestions, which should make it to this list? If yes, do share them with us and if they are actually relevant, we will add it to this list.

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.

20142015data science trendsEnsemble modelsHadoopNoSQLpython
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Datta
Datta

Hi Kunal, While mentioning R vs Python discussion, I guess you forgot to link the article there.

123

Datta
Datta

And by the way that's again great curation as always. :)

123

Kunal Jain
Kunal Jain

Glad you liked it and thanks for pointing it out the missing link. Have added the link now!

123
Varun Rajasekaran
Varun Rajasekaran

Hi Kunal, Am I right in saying that demand for SAS/Tableau/Spotfire/Microstrategy will grow in India atleast? Because based on my experience in interviews, companies want people with experience in multiple tools which I have mentioned above. Unfortunately, this experience hurdle and lack of knowledge in Tableau/Spotfire is preventing newbies like me to enter into this field. Could you please provide some tips on the same?

123

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