Python, Introduction to PySpark, Spark Basics, Spark Features,Spark Components,Spark Architecture,Spark Installation, Spark RDDs & Pair RDDs, RDD Operations(Action and Transformation),Spark Application Deployment, Parallel Processing, Spark SQL, Spark Data Frames, Spark Streaming, Kafka, Spark Advanced Concepts, Spark Project.

Introduction to Amazon Web Services, AWS EC2, AWS EMR, AWS Simple Storage Service (S3).

Introduction to Python, PyCharm, Language Fundamentals, Conditional Statements, Looping,Control Statements, String Manipulation, Lists, Tuple, Dictionaries, Functions, Modules, Input-output, Exception Handling, OOPS Concepts, Regular Expressions, Multithreading, Functional Programming, Map, Reduce, Filter, list comprehenssion,Iter tools, Python to DB..

Linear Algebra: Vector spaces, subspaces, span, basis and dimension, Matrices and linear transformations- Linear map as a matrix, rank and nullity of a matrix, matrix multiplication,inverse and transpose, eigen values, eigen vectors Introductory statistics: Mean, median, mode, variance and standard deviation, co-variance and correlation Probability concepts: Permutations and combinations, unions and intersections, random experiment, sample space, events, probability axioms, conditional probability, Bayes’ theorem, random variables, Discrete and continuous distributions-Uniform, Binomial, Poisson and Normal distributions (sampling, central limit theorem)

A-Z of Python(Core and Advanced), Numpys, Pandas, Data Frame, Sci-kit, Exploratory Data Analytics using Python (EDA), Data Wrangling, Data Visualization, Matplotlib, Seaborn, Machine Learning, Supervised Learning - Regression (Simple Linear Regression, Logistic Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Evaluating Regression Model Parameters), Classification ( K Nearest Neighbors ( KNN ), Naive Bayes Classifier, Decision Tree Algorithm, Random Forest Algorithm, SVM), Unsupervised Machine Learning - Introduction To Clustering Algorithms, K-Means Clustering, Elbow Method for the optimal value of k in K-Means, Hierarchical Clustering, Capstone Project, Dimensionality Reduction, Principal Component Analysis.

Natural Language Processing(NLP), NLTK, Neural Networks, CNN, CNN Alexnet, RNN, LSTM, TFIDF, Keras, Tensorflow, Speech recognition, Transfer Learning, Object Detection, Open Computer Vision (OpenCV), Optical Character Recognition (OCR), Capstone Project.

Working with Tableau Public, Connecting Data with Tableau, Relationships in Tableau, Filters in Tableau, Adding Dimensions in Tableau, Granularity in Tableau Analysis and Calculations in Tableau, Plotting in Tableau, Logical Operations on Tableau, Visualisations in Tableau Dashboard and Stories.

Projects (one live project and two course projects).