Module 1: Introduction to Statistics

• Descriptive and Inferential Statistics. Definitions , terms, types of data

Chapter 2: Harnessing Data

• Types of Sampling Data. Simple random sampling, Stratified, Cluster sampling. Sampling error.

Module 3: Exploratory Analysis

• Mean, Median and Mode, Data variability, Standard deviation, Z-score, Outliers

Module 4: Distributions

• Normal Distribution, Central Limit Theorem, Histogram, Normalization, Normality tests, skewness, Kurtosis.

Module 5: Hypothesis & computational Techniques

• Hypothesis Testing, Null Hypothesis, P-value, Type I & II errors, parametric testing: t- tests, anova test, non-parametric testing

Module 6:

• Correlation & Regression

Module 1: Machine Learning Introduction

• What is ML? ML vs AI. ML workflow, statistical modeling of ML. Application of ML

Module 2: Machine Learning Algorithms

• Popular ML algorithms, clustering, classification and regression, supervised vs unsupervised. Choice of ML

Module 3:

• Supervised Learning Simple and Multiple Linear regression, KNN, and more.

Module 4:

• Linear Regression and Logistic Regression Theory of Linear regression, hands on with use cases

Module 5:

• K-Nearest Neighbour (KNN)

Module 6:

• Decision Tree

Module 7:

• Naïve Bayes Classifier

Module 8:

• Unsupervised Learning
• K-means Clustering.

Module 1:

• Advanced Machine Learning Concepts Tuning with Hyper parameters. Popular ML algorithms, clustering, classification and regression, supervised vs unsupervised. Choice of ML

Module 2:

• Random Forest – Ensemble Ensemble theory, random forest tuning

Module 3:

• Support Vector Machine (SVM) Simple and Multiple Linear regression, KNN,

Module 4:

• Natural Language Processing (NLP) Text Processing with Vectorization, Sentiment analysis with TextBlob, Twitter sentiment analysis.

Module 5:

• Naïve Bayes Classifier Naïve Bayes for text classification, new articles tagging

Module 6:

• Artificial Neural Network (ANN) Basic ANN network for regression and classification

Module 7:

• Tensorflow overview and Deep Learning Intro Tensorflow work flow demo and intro to deep learning.

Module 1:

• Scala and Java - which to use, when and why,Overview of Scala development tools (Eclipse, Scalac, Sbt, Maven, Gradle, REPL, ScalaTest),Overview of Scala Frameworks

Module 2:

• Scala Syntax Fundamentals :Data types,Variables,Operators,Functions and lambdas,Scala,Statements / Loops / Expressions,Extending Builtins,Easy I/O in Scala

Module 3:

• Functional Programming with Scala: What is functional programming?,Using "Match",Case Classes,Wildcards,Case Constructors and Deep Matching,Using Extractors,Pure and First Class Functions,Anonymous Functions,Higher Order Functions,Currying, Closures and Partials.

Module 4:

• Collections and Generics: Java and Scala Collections,Mutable and immutable collections,Using generic types,Lists, tuples and dictionaries,Functional programming and collections,map, fold and filter,Flattening collections and flatMap,The "For Comprehension"Pattern Matching with Scala

Module 1:

• Introduction to Big Data. Challenges to old Big Data solutions,Batch vs Real-time vs in- Memory processing,MapReduce and its limitations,Apache Storm and itslimitations,Need for a general purpose solution - Apache Spark What is Apache Spark? Components of Spark architecture,Apache Spark design principles,Spark features and characteristics,Apache Spark ecosystem components and their insights

Module 2:

• Setting up the Spark Environment,Installing and configuring prerequisites,Installing Apache Spark in local mode,Working with Spark in local mode,Troubleshooting encountered problems in Spark Installing Spark in standalone mode,Installing Spark in YARN mode,Installing & configuring Spark on a real multi-node cluster,Playing with Spark in cluster mode,Best practices for Spark deployment, Playing with the Spark shell,Executing Scala and Java statements in the shell,Understanding the

Module 3:

• Spark context and driver,Reading data from the local filesystem,Integrating Spark with HDFS,Caching the data in memory for further use,Distributed persistence,Testing and troubleshooting, What is an RDD in Spark,How do RDDs make Spark a feature-rich framework,Transformations in Apache Spark RDDs,Spark RDD action and persistence,Spark Lazy Operations -Transformation and Caching,Fault tolerance in Spark,Loading data and creating RDD in Spark,Persist RDD in memory or disk,Pair operations and key-value in Spark,Spark integration with Hadoop,Apache Spark practicals and workshops

Module 4:

• The need for stream analytics,Comparison with Storm and S4,Real-time data processing using,Spark streaming,Fault tolerance and check-pointing,Stateful stream processing,DStream and window operations,Spark Stream execution flow,Connection to various source systems,Performance optimizations in Spark What is Spark SQL,Apache Spark SQL features and data flow,Spark SQL architecture and ,omponents,Hive and Spark SQL together,Play with Data-frames and data states,Data loading techniques in Spark,Hive queries through Spark,Various Spark SQL DDL and DML operations,Performance tuning in Spark

Module 5:

• Why Machine Learning is needed,What is Spark Machine Learning,Various Spark ML libraries,Algorithms for clustering, statistical analytics, classification etc.What is GraphX,The need for different graph processing engines,Graph handling using Apache Spark Pyspark for Data engineering, Data Science and Big data AnalyticsModule 1 What is PySpark?,Installing and Configuring PySpark,Interactive Use of PySpark, Standalone Programs,PySpark RDD With Operations and Commands,Pyspark Mlib Algorithms & Parameters,Pyspark Profiler – Methods and Functions,Pyspark SparkContext and its parameters.