Data Science Training in Chennai

Syllabus of Data Science Course in Chennai

• Introduction to Data Science
• Significance of Data Science
• R Programming basics

• Python Introduction
• Indentations in Python
• Python data types and operators
• Python Functions

• Data Structures Overview
• Identifying the Data Structures
• Allocating values to the Data Structures
• Data Manipulation Significance
• Dplyr Package and performing different data manipulation operations

• Introduction to Data Visualisation
• Various kinds of graphs, Graphics grammar
• Ggplot2 package
• Multivariant analysis by using geom_boxplot
• Univariant analysis
• Histogram, barplot, multivariate distribution, and density plot
• Bar plots for the categorical variables through geop_bar() and the theme() layer

• Statistics Importance
• Statistics classification, Statistical terminology
• Data types, Probability types, measures of speed, and central tendency
• Covariance and Correlation, Binary and Normal distribution
• Data Sampling, Confidence, and Significance levels
• Hypothesis Test and Parametric testing

• Machine Learning Fundamentals
• Supervised Learning, Classification in Supervised Learning
• Linear Regression and mathematical concepts related to linear regression
• Classification Algorithms, Ensemble Learning techniques

• Logistic Regression Introduction
• Logistic vs Linear Regression, Poisson Regression
• Bivariate Logistic Regression, math related to logistic regression
• Multivariate Logistic Regression
• Building Logistic Models
• False and true positive rate
• Real-time applications of Logistic Regression

• Classification Techniques
• Decision Tree Induction Algorithm
• Implementation of Random Forest in R
• Differences between classification tree and regression tree
• Naive Bayes, SVM
• Entropy, Gini Index, Information Gain

• Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
• Unsupervised learning, Clustering algorithm, K-means clustering algorithm
• K-means theoretical concepts, k-means process flow, and K-means implementation
• Implementing Historical Clustering in R
• PCA(Principal Component Analysis) Implementation in R

• DoS/DDoS Concepts
• Botnets
• DoS/DDoS Attack Techniques
• DDoS Case Study
• DoS/DDoS Countermeasures

• Natural language processing and Text mining basics
• Significance and use-cases of text mining
• NPL working with text mining, Language Toolkit(NLTK)
• Text Mining: pre-processing, text-classification and cleaning

• Numpy Basics
• Numpy Mathematical Functions
• Probability Basics and Notation
• Correlation and Regression
• Joint Probabilities
• Bayes Theorem
• Conditional Probability, sum rule, and product rule

• Scipy Introduction and characteristics
• Integrate, Cluster, Signal, Fftpack, and Bayes Theorem

• Pyspark basics
• Uses and Need of pyspark
• Pyspark installation
• Advantages of pyspark over MapReduce
• Pyspark applications

• Machine Learning effect on Artificial Intelligence
• Deep Learning Basics, Working of Deep Learning
• Regression and Classification in the Supervised Learning
• Association and Clustering in unsupervised learning
• Basics of Artificial Intelligence and Neural Networks
• Supervised Learning in Neural Networks, multi-layer network
• Deep Neural Networks, Convolutional Neural Networks
• Reinforcement Learning
• Recurrent Neural Networks, Deep learning graphics processing unit
• Deep Learning Applications, Time series modeling

• Tensorflow Basics and Tensorflow open-source libraries
• Deep Learning Models and Tensor Processing Unit(TPU)
• Graph Visualisation, keras
• Keras neural-network
• Define and Composing multi-complex output models through Keras
• Batch normalization, Functional and Sequential composition
• Implementing Keras with tensorboard
• Implementing neural networks through TensorFlow API

• Basics of Autoencoders and rbm
• Implementing RBM for the deep neural networks
• Autoencoders features and applications

• Big Data and Hadoop Basics
• Hadoop Architecture, HDFS
• MapReduce Framework and Pig
• Hive and HBase
• Basics of Scala and Functional Programming
• Kafka basics, Kafka Architecture
• Kafka cluster and Integrating Kafka with Flume
• Introduction to Spark
• Spark RDD Operations, writing spark programs
• Spark Transformations, Spark streaming introduction
• Spark streaming Architecture, Spark Streaming Features
• Structured streaming Architecture, Dstreams, and Spark Graphx

• Data Visualisation Basics
• Data Visualisation Applications
• Tableau Installation and Interface
• Tableau Data Types, Data Preparation
• Tableau Architecture
• Getting Started with Tableau
• Creating sets, Metadata and Data Blending
• Arranging visual and data analytics
• Mapping, Expressions, and Calculations
• Parameters and Tableau prep
• Stories, Dashboards, and Filters
• Graphs, charts
• Integrating Tableau with Hadoop and R

• MongoDB and NoSQL Basics
• MongoDB Installation
• Significance of NoSQL
• CRUD Operations
• Data Modeling and Management
• Data Indexing and Administration
• Data Aggregation Schema
• MongoDB Security
• Collaborating with Unstructured Data

• SAS Enterprise Guide
• SAS functions and Operators
• SAS Data Sets compilation and creation
• SAS Procedures
• SAS Graphs
• SAS Macros
• PROC SQL
• Advance SAS

• Entering Data
• Logical Functions
• Conditional Formatting
• Validation, Excel formulas
• Data sorting, Data Filtering, Pivot Tables
• Creating charts, Charting techniques
• File and Data security in excel
• VBA macros, VBA IF condition, and VBA loops
• VBA IF condition, For loop
• VBA Debugging and Messaging