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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning LifecycleIntroduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data InsightsDescriptive StatisticsInferential StatisticsHow to Understand Population Distributions?
Reading Data Files into PythonDifferent Variable Datatypes
Probability for Data ScienceBasic Concepts of ProbabilityAxioms of ProbabilityConditional Probability
Central Tendencies for Continuous VariablesSpread of DataKDE plots for Continuous VariableOverview of Distribution for Continuous variablesNormal DistributionSkewed DistributionSkeweness and KurtosisDistribution for Continuous Variable
Central Tendencies for Categorical VariablesUnderstanding Discrete DistributionsPerforming EDA on Categorical Variables
Dealing with Missing ValuesUnderstanding OutliersIdentifying Outliers in DataOutlier Detection in PythonOutliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and PopulationCentral Limit TheoremConfidence Interval and Margin of Error
Bivariate Analysis Introduction
CovariancePearson CorrelationSpearman's Correlation & Kendall's TauCorrelation versus CausationTabular and Graphical methods for Bivariate AnalysisPerforming Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical VariablesIntroduction to Hypothesis TestingP-valueTwo sample Z-testT-testT-test vs Z-testPerforming Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares TestBivariate Analysis on Categorical Categorical Variables
Multivariate AnalysisA Comprehensive Guide to Data ExplorationThe Data Science behind IPL
Supervised Learning vs Unsupervised LearningReinforcement LearningGenerative and Descriminative ModelsParametric and Non Parametric model
Machine Learning PipelinePreparing DatasetBuild a Benchmark Model: RegressionBuild a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should knowConfusion MatrixAccuracyPrecision and RecallAUC-ROCLog LossR2 and Adjusted R2
Dealing with Missing ValuesReplacing Missing ValuesImputing Missing Values in DataWorking with Categorical VariablesWorking with OutliersPreprocessing Data for Model Building
Understanding Cost FunctionUnderstanding Gradient DescentMath Behind Gradient DescentAssumptions of Linear RegressionImplement Linear Regression from ScratchTrain Linear Regression in PythonImplementing Linear Regression in RDiagnosing Residual Plots in Linear Regression ModelsGeneralized Linear ModelsIntroduction to Logistic RegressionOdds RatioImplementing Logistic Regression from ScratchIntroduction to Scikit-learn in PythonTrain Logistic Regression in pythonMulticlass using Logistic RegressionHow to use Multinomial and Ordinal Logistic Regression in R ?Challenges with Linear RegressionIntroduction to RegularisationImplementing RegularisationRidge RegressionLasso Regression
Introduction to K Nearest NeighboursDetermining the Right Value of K in KNNImplement KNN from ScratchImplement KNN in Python
Bias Variance TradeoffIntroduction to Overfitting and UnderfittingVisualizing Overfitting and UnderfittingSelecting the Right ModelWhat is Validation?Hold-Out ValidationUnderstanding K Fold Cross Validation
Introduction to Feature SelectionFeature Selection AlgorithmsMissing Value RatioLow Variance FilterHigh Correlation FilterBackward Feature EliminationForward Feature SelectionImplement Feature Selection in PythonImplement Feature Selection in R
Introduction to Decision TreePurity in Decision TreeTerminologies Related to Decision TreeHow to Select Best Split Point in Decision Tree?Chi-SquaresInformation GainReduction in VarianceOptimizing Performance of Decision TreeTrain Decision Tree using Scikit LearnPruning of Decision Trees
Introduction to Feature EngineeringFeature TransformationFeature ScalingFeature EngineeringFrequency EncodingAutomated Feature Engineering: Feature Tools
Introduction to Naive BayesConditional Probability and Bayes TheoremIntroduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!Working of Naive BayesMath behind Naive BayesTypes of Naive BayesImplementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification ProblemEvaluation Metrics: Multi Class Classification
Introduction to Ensemble TechniquesBasic Ensemble TechniquesImplementing Basic Ensemble TechniquesFinding Optimal Weights of Ensemble Learner using Neural NetworkWhy Ensemble Models Work well?
Introduction to StackingImplementing StackingVariants of StackingImplementing Variants of StackingIntroduction to BlendingBootstrap SamplingIntroduction to Random SamplingHyper-parameters of Random ForestImplementing Random ForestOut-of-Bag (OOB) Score in the Random ForestIPL Team Win Prediction Project Using Machine LearningIntroduction to BoostingGradient Boosting AlgorithmMath behind GBMImplementing GBM in pythonRegularized Greedy ForestsExtreme Gradient BoostingImplementing XGBM in pythonTuning Hyperparameters of XGBoost in PythonImplement XGBM in R/H2OAdaptive BoostingImplementing Adaptive BoosingLightGBMImplementing LightGBM in PythonCatboostImplementing Catboost in Python
Different Hyperparameter Tuning methodsImplementing Different Hyperparameter Tuning methodsGridsearchCVRandomizedsearchCVBayesian Optimization for Hyperparameter TuningHyperopt
Understanding SVM AlgorithmSVM Kernels In-depth Intuition and Practical ImplementationSVM Kernel TricksKernels and Hyperparameters in SVMImplementing SVM from Scratch in Python and R
Introduction to Principal Component AnalysisSteps to Perform Principal Compound AnalysisComputation of Covariance MatrixFinding Eigenvectors and EigenvaluesImplementing PCA in pythonVisualizing PCAA Brief Introduction to Linear Discriminant AnalysisIntroduction to Factor Analysis
Introduction to ClusteringApplications of ClusteringEvaluation Metrics for ClusteringUnderstanding K-MeansImplementation of K-Means in PythonImplementation of K-Means in RChoosing Right Value for KProfiling Market Segments using K-Means ClusteringHierarchical ClusteringImplementation of Hierarchial ClusteringDBSCANDefining Similarity between clustersBuild 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 DaskWorking with CuML
Introduction to Machine Learning InterpretabilityFramework and Interpretable Modelsmodel Agnostic Methods for InterpretabilityImplementing Interpretable ModelUnderstanding SHAPOut-of-Core MLIntroduction to Interpretable Machine Learning ModelsModel Agnostic Methods for InterpretabilityGame Theory & Shapley Values
Introduction to AutoMLImplementation of MLBoxIntroduction to PyCaretTPOTAuto-SklearnEvalML
Pickle and JoblibIntroduction to Model Deployment
Deploying Machine Learning Model using StreamlitDeploying ML Models in DockerDeploy Using StreamlitDeploy on HerokuDeploy Using NetlifyIntroduction to Amazon SagemakerSetting up Amazon SageMakerUsing SageMaker Endpoint to Generate InferenceDeploy on Microsoft Azure CloudIntroduction to Flask for ModelDeploying ML model using Flask
Model Deployment in AndroidModel Deployment in Iphone

The Ultimate Plan to Become a Data Scientist in 2016

Kunal Jain
Kunal Jain Last Updated : 31 Jan, 2017
< 1 min read

Introduction

Data Scientist is one of the hottest jobs of this decade. The demand for data scientists is much higher than available candidates (Source). So, there is a lot of incentive for people to look up to data science as a career option, and that is not going to change in near future.

However, if you do one search on Google, you will see your dream vanishing. There are too many resources, advice and paths suggested by various people, which makes it impossible for a beginner to take right decisions.

If you are facing a similar problem, let’s accomplish this in 2016. If you aspire to become a data scientist, this annual plan would make things much easier and faster for you. I’ve mentioned only the best resources you should follow. This plan is designed to make you a data scientist by December 2016 (conservative pace). If you can devote more time, great. You’d could achieve this feat much faster or with more depth by looking at additional resources (orange bullet).

ultimate path to become a data scientist in 2016

 

Click Here –> Link to Resources

 

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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

Arun
Arun

This sets a clear path for people who would like to become data scientists. But do data scientists also need an adavnced degree in Computer science / statistics?

123

RAKESH KUMAR
RAKESH KUMAR

Thanks Kunal for charting out a plan to become a Data Scientist by end of 2016. The Skill sets you have mentioned to become a Data Science are as below: R, Python,Machine Learning, Qlikview Tableau, D3.J, Adobe Insight (Omniture) and Matlab, etc Are SAS and SPSS skills have any scope to be a part of Data Scientist? Please advise. Regards, Rakesh

123
Kunal Jain
Kunal Jain

Hi Arun, You don't need an advanced degree in Computer Science / statistics necessary. Infact any degree in a quant background is good - I did aerospace engineering. I have seen people from Economics background do well in industry. Hope this helps. Kunal

123
David Smith
David Smith

Not necessarily. And you can also download materials to learn SAS skills for free - http://www.sas.com/en_us/learn/analytics-u.html

123
Sudhindra
Sudhindra

Awesome and training plan for the year 2016 especially who are looking for getting into Data Sciences role. What about a training plan for people in the functional role?

123

Kunal Jain
Kunal Jain

Sudhindra, Thanks for the suggestion. We do have something in pipeline one similar lines. However, the breadth of functional roles make it difficult to chart out a plan like this. Regards, Kunal

123
Sudhindra
Sudhindra

Thanks will await for the excitement!

123
Niranjan Patil
Niranjan Patil

Excellent article. Thanks Kunal. Could you please suggest some books for Statistics,Puzzles and Probability.

123

Kunal Jain
Kunal Jain

Hi Niranjan, Thanks for your appreciation. We plan to publish an article on stats and probability. For now, you can check out this thread: http://discuss.analyticsvidhya.com/t/which-is-the-best-statistics-and-analytics-book/275 Also, you can check out articles on puzzles through our search. Regards, Kunal

123

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