Duration
6 months
Mode
Online/Offline
Batch Size
10
Modules
4
LMS Access
Yes
Certification
IBM/Microsoft*
Timing
Weekday/Weekends
Introduction
It is designed to address the core knowledge of data science field by focusing on required statistics, mathematics, computer science, and analytics to allow students to discover the fascinating world of data science.
The program includes theoretical and practical applied approaches preparing students to enter the field of data science profession or continue their education in a professional graduate degree program.
It also guidepost to spur your understanding of data science. This programme will help you manage and maximize a company’s data assets, integrate analytics and machine learning into decisions and processes, and power innovation for businesses. This programme’s focus on real-world examples, case studies, and practical sessions will ensure that you build a strong foundation in business analytics and make high-output business decisions.
Goal of Course
- Demonstrate ability to explore data and identify the best statistical and mathematical model to apply for its analysis.
- Demonstrate an ability to articulate, assess, and apply appropriate theories and principles of Machine Learning.
- Develop and implement data analysis strategies based on theoretical principles, ethical considerations, and detailed knowledge of the underlying data.
- Develop meaningful reports and visualization of data analytics appropriate to a technical and non-technical audience.
Key Modules
- Effective usage of MS Excel
- Querying Database with MS SQL Server
- Understanding Statistical Values
- Data Handling with Python Programming
- Reading data from API
- Image Processing
- Machine Learning with Python Programming
- Deep Learning with Python Programming
- Natural Language Processing
Introduction to Data Science - Advanced Analytics
- Relevance in industry & need of the hour
- Types of analytics – Marketing, Risk, Operations, etc
- Business & Technology drivers for analytics
- Future of analytics & critical requirement
- Types of problems and business objectives in various industries
- Different phases of Analytics Project
Effective usage of MS Excel
- Working with Math, Text, and Date functions
- Working with IF based Conditions
- Working with VLookup/HLookup Function
- Working with Data Validation, Sparklines
- Working with Conditional Formatting
- Working with What-If Analysis
- Working Pivot Table for Data Summarization
- Implementing Security in Excel File
- Working with Chart & Dashboard
Querying Database with MS SQL Server
- SQL Overview, and building the Database Schema
- Protecting data integrity with constraints
- Manipulating Data
- Writing Single & Multi Table Queries
- Combining results with set operators
- Employing Functions in Data Retrieval
- Performing analysis with aggregate functions
- TSQL Programming, Triggers , Exception Handling
Understanding Statistical Values
- Statistical Fundamentals
- Descriptive statistics
- Measures of Central Tendency
- Measures of Dispersion
- Understanding distributions and histograms
- Types of Data
- Univariate statistical plots and usage
- Bivariate and Multivariate Statistics
- Introduction to Probability
- Addition and multiplication rule
- Marginal Probability
- Bayes theorem
- Spam not spam problem using bayes theorem
Data Handling with Python Programming
- Statistics Fundamentals Recap
- Working with Collections
- Conditional Statements, Loops, Functions
- File Handling , Managing MySQL Database
- Exception/Error Handling
- Python Library - Pandas, NumPy
- Python Library - Matplotlib, Seaborn, Plotly
- API Connectivity, Web Scraping
Machine Learning with Python Programming
- Machine Learning Fundamentals
- Orientation of Machine Learning and Industry Use-cases.
- Different types of Machine Learning Techniques.
- Understanding the SKLEARN package for Machine Learning
- Machine Learning –
- Linear Regression
- Linear Regression Assignment
- Logistic Regression
- Naive Bayes
- Advanced Regression
- Support Vector Machine
- Tree Models - Decision Tree, Decision Forest Tree
- K Nearest Neighbors
- Unsupervised learning: Clustering - K Means
- Model Selection - Practical Considerations
- Model Performance Matrix
- Unsupervised Learning: Principal Component Analysis
Image Processing with Python Programming
- Introduction to OpenCv Image processing Library
- Processing real-time images with OpenCv
- Industrial case study on use case of OpenCv
Natural Language Processing with NLTK
- Introduction to NLP
- Text Tokenization, chunking, pos-tagging using NLTK.
- Syntactic Parsing in Python
- Entity Recognition from document
- Text Mining in Python
- Sentiment Analysis
Deep Learning with Python
- Introduction to Neural Networks
- Mathematics behind Neural Network
- Artificial Neural Networks
- Convolutional Neural Networks
- Convolutional Neural Networks - Industry Applications
- Recurrent Neural Networks
