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

Your Ultimate Learning Path to Become a Data Scientist and Machine Learning Expert in 2020

Pranav Dar
Pranav Dar Last Updated : 21 Apr, 2020
4 min read

Introducing the Learning Path to become a Data Scientist in 2020!

Learning paths are easily one of the most popular and in-demand resources we curate at the start of the new year. We’ve received a ton of queries recently asking when we would be releasing the learning paths for 2020.

And here we are – we are thrilled to present the first learning path of 2020 for our community!

data science learning path

New Feature
Get Personalized Learning Path! Set your goal and timeline. Get a path—under 2 mins.

The learning path for 2020 is the ultimate and most comprehensive collection of resources put together in a structured manner. This learning path is for anyone who wants to make a career in data science. So whether you are a fresher, have a few years of work experience, or are a mid-level professional – this data science learning path is for you.

This year, based on the wonderful reception we received last year, we have expanded the scope of these learning paths. We will be releasing 4 different learning paths, each focused on where you stand in your learning journey:

 

Why you Should Enroll in this Data Science Learning Path

There are way too many learning resources out there. I’m sure anyone who has tried to study from different sources will understand this. It can be overwhelming for anyone starting out, regardless of where you stand in your career.

And hence we created these learning paths.

We take away the pain, time and effort of going through hundreds of resources on data science, machine learning, deep learning, Natural Language Processing (NLP) and Computer Vision. Our aim is to bring forward the best learning resources for you to help you streamline your data science learning journey.

 

You can access the complete and most comprehensive learning path to become a data scientist in 2020 here. You would need to register yourself on the Courses platform to enroll yourself. This will enable you to keep track of what you have covered as you progress in your machine learning journey.

 

Summary – Learning Path to become a Data Scientist in 2020

Here’s a summary of what you can expect to learn (and the steps you should follow) using this learning path:

  • Getting Started with Data Science and Python: The start of your journey to becoming a data scientist! Understand what a data scientist does, the various terms associated with data science, and start getting acquainted with the Python programming language
  • Statistics and Mathematics: The backbone of data science. Some of the key concepts you’ll cover are probability, inferential statistics, and get a hang of how to perform exploratory data analysis (EDA). This will also include the basics of linear algebra (another core machine learning topic)
  • Machine Learning Basics: Welcome to the world of machine learning! This section is all about introducing you to the basic machine learning algorithms and techniques, including linear regression, logistic regression, decision trees, Naive Bayes, support vector machines (SVM), among others
  • Ensemble Learning: Time to deep dive into advanced machine learning topics. Understand what ensembling is, the different ensemble techniques, and start working on datasets to gain a hands-on practical experience
  • Time Series: One of the more complex topics in this space. This deserves an entire section by itself so we have also included a hands-on project to get you familiar with the various time series concepts and how they work in the real-world
  • Matrix Algebra and Recommendation Systems: Why matrix algebra, you ask? Well, you can’t really get serious about learning how recommendation engines work without it! So this section, covered in June in the learning path, is all about these two trending and relevant concepts. This will cover dimensionality reduction techniques like Principal Component Analysis (PCA) along with recommendation engine projects
  • Neural Networks (and Deep Learning): Yes, deep learning is part of the data science learning path. Given the rapid rise and adoption of deep learning applications, this is potentially a very relevant part of your role as a data scientist. You would learn about neural networks and will also pick up a popular deep learning framework called Keras (you can choose others based on your preference – such as PyTorch)
  • Computer Vision: Computer vision is easily the most in-demand deep learning field in the industry. After you’re familiar with the above section, dive into different kinds of computer vision problems and learn as you go
  • Natural Language Processing (NLP): The hottest field in any industry. Businesses are tripping over each other to land the best NLP talent – this is a great time to start working in NLP! From Google’s BERT to Facebook’s RoBERTa, start getting acquainted with the state-of-the-art NLP frameworks

 

As mentioned above, you can access the full data science learning path here. Register yourself and begin your machine learning journey today! You can track your progress throughout the year as you check off milestones and inch closer to your dream role.

We have also provided an illustrated version of this data science learning path below which paints a month-by-month picture. You can print this out and use this as a checklist. And if you put forward your best efforts and follow this learning path – you’d be in a great position to start cracking data science interviews by the end of 2020.

You can download a high definition version of the infographic here.

data science learning path

Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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