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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 – Python Environment Setup and Essentials
- Module 02 – Python language Basic Constructs
- Module 03 – OOP concepts in Python
- Module 04 – Database connection
- Module 05 – NumPy for mathematical computing
- Module 06 – SciPy for scientific computing
- Module 07 – Matplotlib for data visualization
- Module 08 – Pandas for data analysis and machine learning
- Module 09 – Exception Handling
- Module 10 – Multi Threading & Race Condition
- Module 11 – Packages and Functions
- Module 12 – Web scraping with Python
- 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 – Overview of Natural Language Processing and Text Mining
- Module 02 – Text Mining, Cleaning, and Pre-processing
- Module 03 – Text Classification
- Module 04 – Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling
- Module 05 – Introduction to Semantics and Vector Space Models
- Module 06 – Dialog Systems
- Module 01 – RBM and DBNs & Variational AutoEncoder
- Module 02 – Object Detection using Convolutional Neural Net
- Module 03 – Generating images with Neural Style and Working with Deep Generative Models
- Module 04 – Distributed & Parallel Computing for Deep Learning Models
- Module 05 – Reinforcement Learning
- Module 06 – Deploying Deep Learning Models and Beyond
- Module 01 – Introduction to Big Data and Data Collection
- Module 02 – Introduction to Cloud Computing & AWS
- Module 03 – Elastic Compute and Storage Volumes
- Module 04 – Virtual Private Cloud
- Module 05 – Storage – Simple Storage Service (S3)
- Module 06 – Databases and In-Memory DataStores
- Module 07 – Data Storage
- Module 08 – Data Processing
- Module 09 – Data Analysis
- Module 09 – Data Visualization and Data Security
- 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 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
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