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thedatascienceportalData Science Learning PathRight from basics to complex problem-solving level. Sign up for multiple courses at the same time and do them just asyou would in a university – My suggestion is to take 2-3 items for a 4 months period. Following are the topicscomprising of the whole Data Science journey – Under each main topic are several important concepts you must cover,along with good reference material from the most widely chosen education platforms. Happy Learning! Algebra and Matriceso Linear Equations and Graphso Functionso Transformationso Reference Material Statistics – Io Probabilityo Modelling and Sampling data distributionso Mean, Median, Mode, Variance, Standard Deviationo Confidence Intervalso Reference Material Udacity – Statistics by San Jose State UniversityedX – Probability and Statistics I- IV by Georgia TechBook - Introduction to Probability and Statistics Principles and Applications for Engineeringand the Computing Sciences by J. Milton and J. ArnoldAnalytical Thinkingo Inference Buildingo Reading and understanding datao Correlations and Similaritieso Reference Material Coursera - Mathematics for Machine Learning: Linear AlgebraedX – Linear Algebra I - IV by Georgia TechBook – Elementary Linear Algebra by Stephen Andrilli and David HeckerUdacity – Intro to Inferential StatisticsCoursera – Statistical Inference by John Hopkins UniversityedX – Statistical Thinking for Data Science and Analytics by Columbia UniversityCalculuso Differentiationo Integrationo Chain Ruleo Optimizationo Reference Material Coursera - Mathematics for Machine Learning: Multivariate Calculus by Imperial College ofLondonCoursera – Calculus and Optimization for Machine Learning by NRUHSEBook – Thomas’ Calculus
thedatascienceportal Programming – Concepts and Implementationo Introduction to programmingo Variables, Functions, Loopso Object Oriented Principles and Designo Introduction to Data Structures: Arrays and Listso Reference Material Book – Structure and Interpretation of Computer Programs by Harold Abelson, Gerald Jay Data Structures and Algorithmso Introduction to Algorithmso Concepts: Sorting, Searching, Divide and Conquer, Shortest Path, Greedy Algorithmso Data Structures Implementation: Arrays, Lists, Queues, Stacko Algorithmic Complexities and Constraintso Advanced Data Structures and Algorithms: Trees, Graphs, String-based, Special case-basedo Reference Material Udacity – Data Structures and Algorithms Coursera – Data Structures and Algorithms Specialization by UC San Diego Coursera – Algorithms Part 1 and 2 by Princeton University edX – Algorithms and Data Structures by UC San Diego Book - Introduction to Algorithms is a book on computer programming by Thomas H. Sussman, Julie SussmanedX – CS50’s Introduction to Computer Science by Harvard UniversityCoursera – Object Oriented Programming in Java Specialization by Duke UniversityedX – Introduction to Python Programming by Georgia TechCoursera – Python 3 Programming by University of MichiganCoursera – R Programming by John Hopkins UniversityUdacity - Object Oriented Programming in JavaBook – The Pragmatic Programmer: Your Journey to Mastery by Andrew Hunt, DavidThomasCormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford SteinBook – Algorithms by Robert SedgewickDatabase Managemento Introduction to Database and DBMSo Data Models: Terminology and Ruleso SQL: Query Formulationo Data Models: Entity Relationship Diagramso Data Models: Schema Buildingo Normalizationo Reference Material Coursera – Introduction to Structured Query Language by University of MichiganedX – Databases I – V by Stanford UniversityBook – Database Systems – The Complete Book by Hector G. Molina, Jeffrey D. Ullman, andJennifer Widom
thedatascienceportal Python / R for Machine Learningo Python / R Programming Fundamentalso Advanced Python: Data Structures and Comprehensionso Advanced R: R Functions, Debugging and Profilingo Focus Python Libraries: Numpy, Pandas, Matplotlibo Focus R packages: Dplyr, DT, Careto Reference Material Exploratory Data Analysiso Introduction to Data Analysiso Statistical Inferenceo Data Exploration: Forming Insightso Reference Material edX – Python Basics for Data Science by IBMedX – Python for Data Science by UC San DiegoCoursera – IBM Data Science Professional Certificate courses 1 – 5Coursera – Introduction to Data Science in Python by University of MichiganCoursera – Data Science: Foundations using R Specialization by John Hopkins UniversityUdacity – Intro to Descriptive StatisticsCoursera – Developing Data Products by John Hopkins UniversityCoursera – Data Analysis with Python by IBMBook – Exploratory Data Analysis by John TukeyData Visualizationo Visualization Best Practiseso Focus Python Libraries: Matplotlib, Seaborn, Bokeho Focus R packages: Ggplot2, Esquisse, Shinyo Reference Material Coursera – Data Visualization with Python by IBM edX – Data Science: Visualization by Harvard University Book – Storytelling with Data: Visualization Guide for Business Professionals by ColeNussbaumer Knaflic Statistics – IIo Linear Data Modellingo Multivariate Analysiso Regression Analysiso Time Series Analysiso Factor and Path Analysiso Reference Material Book – Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert TibshiraniBook – The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, JeromeFriedman
thedatascienceportal Artificial Intelligenceo Problem Solving with Search Algorithmso Probability with Bayes Rule and Bayesian Networko Knowledge Representation, Logico Planningo Constraint Satisfactiono Introduction to Machine Learningo Introduction to Natural Language Processingo Reference Material Machine Leaning – Io Basics of Machine Learningo Types of Machine Learning Algorithmso Supervised v/s Unsupervised Learningo Cost Function and Regularizationo Optimization Algorithmso Classification v/s Regressiono Perceptron and Logistic Regressiono Different Machine Learning Algorithmso Dimensionality Reductiono Working with different types of data and underlying challengeso Introduction to Deep Learningo Reference Material edX – Artificial Intelligence by Columbia UniversityedX – Artificial Intelligence by Georgia TechedX – CS50’s Introduction to Artificial Intelligence with PythonUdacity – Intro to Artificial IntelligenceBook – Artificial Intelligence: A Modern Approach by Peter Norvig, Stuart J. RusselledX – Machine Learning by Georgia TechedX – Machine Learning by Columbia UniversityCoursera – Machine Learning with Python by IBMCoursera – Machine Learning by Stanford UniversityNatural Language Processingo Basics of Natural Language Processingo Stop word elimination, Stemming, Lemmatization, Regexo Feature Extraction and Encoding from Texto Term Frequency (TF) and Inverse Document Frequency (TF-IDF)o Word Embeddingso Vector Space Models, N-gram Language Models, Sequence Modelso Attention Modelso Applications: Sentiment Analysis and Machine Translationo Reference Material Coursera – Natural Language Processing Specialization by deeplearning.aiedX – NLP and NLU by University of Texas Arlington
thedatascienceportal Deep Learningo Basics of Neural Network and Perceptron Modelo Neural Network Design and Architectureo Parameter and Hyperparameter Tuningo Regularization and Optimizationo Advanced: Convolutional Neural Networks, Recurrent Neural Networks, LSTMo Reference Material Coursera – Deep Learning Specialization by deeplearning.aiCoursera – Applied Text Mining in Python by University of MichiganedX – Deep Learning by IBMBook – Deep Learning by Aaron Courville, Ian Goodfellow, Yoshua BengioMachine Learning – IIo Machine Learning Approach and Processo TensorFlow Developmento Advanced Algorithms: Recommender Systems, Reinforcement Learningo Structuring Machine Learning Projectso Deploying Machine Learning Modelso Reference Material Coursera – Advanced Machine Learning Specialization by NRUHSECoursera – Reinforcement Learning Specialization by University of AlbertaCoursera – Recommender Systems Specialization by University of MinnesotaCoursera – TensorFlow Developer by deeplearning.aiCoursera – TensorFlow: Data and Deployment by deeplearning.ai Machine Learning Problemso Kaggle – Datasets and Competitions – Practise! Wrapping upo Reference Material Coursera – Data Science Specialization by John Hopkins University Coursera – Data Warehousing for Business Intelligence Specialization by University ofColorado Specific software and platformso Reference Material edX – Introduction to Data Analysis using Excel by Microsoft edX – Analyzing and Visualizing Data with Excel by Microsoft edX – Analyzing and Visualizing Data with Power BI by Microsoft Coursera – Data Visualization with Tableau by UC Davis Coursera – Data Engineering, Big Data and Machine Learning on GCP Specialization dataiku - Academy
o Advanced R: R Functions, Debugging and Profiling o Focus Python Libraries: Numpy, Pandas, Matplotlib o Focus R packages: Dplyr, DT, Caret o Reference Material edX - Python Basics for Data Science by IBM edX - Python for Data Science by UC San Diego Coursera - IBM Data Science Professional Certificate courses 1 - 5
