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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IBMBelhaven University, Mississippi |
The Syllabus
- Module 01 – Introduction to Data Science with R
- Module 02 – Data Exploration
- Module 03 – Data Manipulation
- Module 04 – Data Visualization
- Module 05 – Introduction to Statistics
- Module 06 – Machine Learning
- Module 07 – Logistic Regression
- Module 08 – Decision Trees and Random Forest
- Module 09 – Unsupervised Learning
- Module 10 – Association Rule Mining and Recommendation Engines
Self-paced Course Content
- Module 11 – Introduction to Artificial Intelligence
- Module 12 – Time Series Analysis
- Module 13 – Support Vector Machine (SVM)
- Module 14 – Naïve Bayes
- Module 15 – Text Mining
- Module 01 – Introduction to Data Science using Python
- Module 02 – Python basic constructs
- Module 03 – Maths for DS-Statistics & Probability
- Module 04 – OOPs in Python (Self paced)
- Module 05 – NumPy for mathematical computing
- Module 06 – SciPy for scientific computing
- Module 07 – Data manipulation
- Module 08 – Data visualization with Matplotlib
- Module 09 – Machine Learning using Python
- Module 10 – Supervised learning
- Module 11 – Unsupervised Learning
- Module 12 – Python integration with Spark (Self paced)
- Module 13 – Dimensionality Reduction
- Module 14 – Time Series Forecasting
- Module 01 – Introduction to Machine Learning
- Module 02 – Supervised Learning and Linear Regression
- Module 03 – Classification and Logistic Regression
- Module 04 – Decision Tree and Random Forest
- Module 05 – Naïve Bayes and Support Vector Machine (self-paced)
- Module 06 – Unsupervised Learning
- Module 07 – Natural Language Processing and Text Mining (self-paced)
- Module 08 – Introduction to Deep Learning
- Module 09 – Time Series Analysis (self-paced)
- Module 01 – Introduction to Deep Learning and Neural Networks
- Module 02 – Multi-layered Neural Networks
- Module 03 – Artificial Neural Networks and Various Methods
- Module 04 – Deep Learning Libraries
- Module 05 – Keras API
- Module 06 – TFLearn API for TensorFlow
- Module 07 – DNNs (deep neural networks)
- Module 08 – CNNs (convolutional neural networks)
- Module 09 – RNNs (recurrent neural networks)
- Module 10 – Gpu in deep learning
- Module 11 – Autoencoders and Restricted Boltzmann Machine (RBM)
- Module 12 – Deep learning applications
- Module 13 – Chatbots
- Module 01 – Hadoop Installation and Setup
- Module 02 – Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
- Module 03 – Deep Dive in MapReduce
- Module 04 – Introduction to Hive
- Module 05 – Advanced Hive and Impala
- Module 06 – Introduction to Pig
- Module 07 – Flume, Sqoop, and HBase
- Module 08 – Writing Spark Applications Using Scala
- Module 09 – Use Case Bobsrockets Package
- Module 10 – Introduction to Spark
- Module 11 – Spark Basics
- Module 12 – Working with RDDs in Spark
- Module 13 – Aggregating Data with Pair RDDs
- Module 14 – Writing and Deploying Spark Applications
- Module 15 – Project Solution Discussion and Cloudera Certification Tips and Tricks
- Module 16 – Parallel Processing
- Module 17 – Spark RDD Persistence
- Module 18 – Spark MLlib
- Module 19 – Integrating Apache Flume and Apache Kafka
- Module 20 – Spark Streaming
- Module 21 – Improving Spark Performance
- Module 22 – Spark SQL and Data Frames
- Module 23 – Scheduling/Partitioning
The following topics will be available only in self-paced mode:
- Module 24 – Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2
- Module 25 – Hadoop Administration – Cluster Configuration
- Module 26 – Hadoop Administration – Maintenance, Monitoring and Troubleshooting
- Module 27 – ETL Connectivity with Hadoop Ecosystem (Self-Paced)
- Module 28 – Hadoop Application Testing
- Module 29 – Roles and Responsibilities of Hadoop Testing Professional
- Module 30 – Framework Called MRUnit for Testing of MapReduce Programs
- Module 31 – Unit Testing
- Module 32 – Test Execution
- Module 33 – Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
- Module 01 – Introduction to Data Visualization and The Power of Tableau
- Module 02 – Architecture of Tableau
- Module 03 – Charts and Graphs
- Module 04 – Working with Metadata and Data Blending
- Module 05 – Advanced Data Manipulations
- Module 06 – Working with Filters
- Module 07 – Organizing Data and Visual Analytics
- Module 08 – Working with Mapping
- Module 09 – Working with Calculations and Expressions
- Module 10 – Working with Parameters
- Module 11 – Dashboards and Stories
- Module 12 – Tableau Prep
- Module 13 – Integration of Tableau with R
- Module 01 – Entering Data
- Module 02 – Referencing in Formulas
- Module 03 – Name Range
- Module 04 – Understanding Logical Functions
- Module 05 – Getting started with Conditional Formatting
- Module 06 – Advanced-level Validation
- Module 07 – Important Formulas in Excel
- Module 08 – Working with Dynamic table
- Module 09 – Data Sorting
- Module 10 – Data Filtering
- Module 11 – Chart Creation
- Module 12 – Various Techniques of Charting
- Module 13 – Pivot Tables in Excel
- Module 14 – Ensuring Data and File Security
- Module 15 – Getting started with VBA Macros
- Module 16 – Ranges and Worksheet in VBA
- Module 17 – IF condition
- Module 18 – Loops in VBA
- Module 19 – Debugging in VBA
- Module 20 – Dashboard Visualization
- Module 21 – Principles of Charting
- Module 22 – Getting started with Pivot Tables
- Module 23 – Statistics with Excel
- Module 01 – Introduction to NoSQL and MongoDB
- Module 02 – MongoDB Installation
- Module 03 – Importance of NoSQL
- Module 04 – CRUD Operations
- Module 05 – Data Modeling and Schema Design
- Module 06 – Data Management and Administration
- Module 07 – Data Indexing and Aggregation
- Module 08 – MongoDB Security
- Module 09 – Working with Unstructured Data
- Module 01 – Introduction to SQL
- Module 02 – Database Normalization and Entity-Relationship Model
- Module 03 – SQL Operators
- Module 04 – Working with SQL: Join, Tables, and Variables
- Module 05 – Deep Dive into SQL Functions
- Module 06 – Working with Subqueries
- Module 07 – SQL Views, Functions, and Stored Procedures
- Module 08 – Deep Dive into User-defined Functions
- Module 09 – SQL Optimization and Performance
- Module 10 – Advanced Topics
- Module 11 – Managing Database Concurrency
- Module 12 – Programming Databases Using Transact-SQL
- Module 13 – Microsoft Courses: Study Material
Instructors
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