Module 1-Introduction to course

• Introduction to corporate training
• Software life cycle
• Methodologies
• Soft skills
• Professional Ethics
• Make you strength
• Where I Stands?

Module 2-Data science introduction

• What is data science
• Need for data science?
• Data science vs Business Intelligence
• Prerequisite for learning data science
• What does a data scientist do?
• Data science life cycle with example
• Demand for data science

Module 3-Installations

• Installations required for data science

Module 4-Python programming

• Introduction to python
• Operators
• Data Types
• Control Statements
• Functions
• Data Structures – Lists, Sets, Tuples, Strings, Dictionaries,
• OOPS Concept

Module 5-Libraries

• Numpy
• Pandas
• Scipy
• Scikit-Learn
• Keras
• Matplotlib
• Seaborn
• Cufflinks
• NLTK

Note: Installations and work end to end. As per requirements going to work with different libraries.

Module 6-Data exploration

• Collecting data from different sources
• Analyzing data
• Data preprocessing
• Data munging
• Data mining
• Data manipulation
• Data visualization
• Feature Selection
• Feature Scaling
• Dimensionality reduction

Module 7-Statistics

• Basics of Statistics
• Descriptive Statistics
• Inferential Statistics
• Qualitative vs. Quantitative Analysis
• Hypothesis Testing
• Data Distribution

Module 8- Other Mathematics Concepts

• Probability
• Calculus
• Linear algebra

Module 9-Machine Learning

• Introducing Machine Learning models
• Supervised learning
• Regression and Classification models
• Unsupervised learning
• Clustering and Aggregation models
• Semi supervised learning
• Over fitting and under fitting (Linear, Logistic, Navi Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forest …..)

Note: Deals with Mathematics and Programming part

Module 10-Deep Learning and other algorithms

• Introduction to Deep learning
• OpenCV
• Artificial Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Tensor flow
• Over fitting and under fitting

Module 11-Natural Language Processing

• Introduction to NLP
• Text Preprocessing
• Lemmatization
• Stemming
• Text to features
• Important Tasks in NLP
• Important Libraries for NLP

WORKING ON DATA SCIENCE PROJECTS

• Examine your problem
• Prepare your data (raw data, feature extraction, feature engineering, etc.)
• Spot-check a set of algorithms
• Examine your results
• Double-down on the algorithms that worked best

Note: Deals ‘n’ number of real time projects

WORK ENVIRONMENT

• Status meetings
• Meetings with stockholders
• Participating in gathering requirements
• Working on client projects
• Coordinating team

System Requirements

• Ram 4GB Min
• Hard Disk 250GB
• Processor i3 Min

Course Highlights

1. A Dedicated Portal For Practicing.
2. 1-1 Mentorship
3. Internship Offers for Freshers.
4. Weekly Assignments.
5. Weekly Doubt Sessions
6. Advanced Curriculum
7. Certificates On successful Completion of Project .
8. Resume Preparation Tips
9. Interview Guidance And Support.
10. Dedicated HR Team for Job Support And Placement Assistance.