Big Data Analytics Training and Certification
The Big Data Certification courses offered in
Kolkata are designed to cover the basic as well as advanced level training programs.
Our trainers are highly dedicated with hands on professional experience in Big
Data projects.
Big Data Training Curriculum
MODE OF TRAINING: 22 weeks part-time program with the
evening & weekend classes furnished for both the qualified freshers and
working professionals.
- BIG DATA AND DATA BASE CONCEPTS
- Relational Database, Normalization, Primary Key, Connecting Data Tables, Data Base Schema, SQL Queries etc
- Introduction to Big Data and its concept
- Introduction to Basic Statistics
- Probability and Theroretical Distribution, Naive Bayes
- Inferential Statistics, Bias v/s Variance, Z, t, Chi-square, F test, ANOVA, MANOVA, ANOCOVA
- Time Series, ARIMA, Box Jenkins Model, Holt Winters Method
- Data Mining, Linear Regression, Logistic Regression, Correlation
- Multivariate Analysis, Discriminant, Factor, Cluster and Conjoint Analysis
- Artificial Neural Networks, Learning Rate, Motivation, Perception and Single Layer, Hand Calculations
- Multi Layered Neural Net, Conjugant Gradient Techniques, Restricted Boltzman Machines
- K Nearest Neighbour, Naïve Bayesian classifier, etc.
- Association Rules
- Support Vector Machines, Mathemetical Intuition
- Ensemble method - Bagging, boosting, adaboost, Gradient boosting, Random Forest.
- Optimization, Linear Programming, Goal Programming, Quadratic Programming, Evolutionary Serach, Portfolio Analysis
- Non Parametric Tests, Run, Sign, Kruskal Wallis, Mann Whitney U test, Kolmogorov Smirnov two sample test
- DATA PRE-PROCESSING, VISUALIZATION CONCEPT AND TOOLS
- Missing Value Analysis, identification of outliers in Univariate and Multivariate Data sets
- Standardisation, Normalization, Dimensionality Reduction, PCA, SVD approaches
- Data Exploration, Bar Chart, Histogram, ggplot, bubble chart, Tree Map, Heat Map
- Analysis through Excel, Navigation, Formatting, SUMPRODUCT, if, countif, statisitcal functions
- Excel, Goal seek, Pivot table wizard, All formulae and functions, Sensitivity Analysis, Business Applications
- R, Basic Operations, Data structure, subsetting, data import and export and Charting
- Statistical Analysis - I, Predictive Modeling, Time Series Analysis, Linear Discriminant Function, Decision Tree
- Statistical Analysis - II, CHAID, SVM, Neural Networks, k-means and hierarchical clustering
- Statistical Analysis - III, Market Basket Analysis, Association rules and lift, Principal components, Reduction of dimensions
- Text Mining, Sentiment and Social Nework Analysis, Unstructured and Semi-structuredData
- Fuzzy Logic, Fuzzy Decision Trees, Stochastic Search
- NOSQL, HBASE, MONGODB, CASSANDRA, Data model, Architecture and Application
- CQL, Integrating CASSANDRA with Hadoop, HDFS, Architechture, Commands, Map Reducing, YARN, Hadoop Ecosystem and Configuration
- Hive, Streaming with Python, PIG, Integrating with Enterprise Workflow
- OOZIE, ZOOKEEPER, MAHOUT
- SQOOP & FLUME, AVRO, Chukwa, Whirr
- Apache Spark, Spark Applications v/s Spark Shell, Logging, Spark streaming, Map Reducing, Iterative algorithms, Graph Analysis, Improving Spark Performance
- Application into different management streams, Contribution in Strategic Management
- Live Projects and Handling Big Data