DATA SCIENCE
Syllabus
DATA SCIENCE SYLLABUS
WHAT YOU WILL LEARN IN DATA SCIENCE – ML AND AI
- Data Science Mathematics – Revising School Level Math
- Python Programming Language
- Python Data Science Libraries
- Data Science Techniques
- Basic Of Artificial Intelligence
- Machine Learning
- Data Visualization Tools
- Deep Learning
DATA SCIENCE INTRODUCTION – MODULE I
- Data Science and It’s Concepts
- Scope Of Data Science
- Data Science Business and Business Intelligence (BI) Use Cases
- Data Science Field Discussions
- Data Science Artificial Intelligence (AI) and AI Subset Machine Learning (ML) and ML Subset Deep Learning (DL) Involvements
- Analytics – Introduction
- Understanding Data, Types Of Data
- Understanding Dataset – Structured, Unstructured and Semi Structured
DATA SCIENCE PROGRAMMING LANGUAGE (PYTHON)– MODULE II
- Python – Introduction and installation
- Python – Setup and Interpreter
- Python – Keywords, Statements and Statements Syntax
- Python – Variables, Literals, Data Types and Data Structure
- Python – Operators
- Python – Functions
- Python – Input and Output (IO)
- Python – Errors and Exceptions
- Python – Modules
- Python – classes
- Python – Threading and Multi-threading
- Python – Batteries
- Python – Package Management Tools: pip and conda
- Python – Virtual Environments
DATABASES MODULE- III
- Structure Query Language (SQL)
- SQL – Introduction
- SQL – Data Definition Language(DDL)
- SQL – DDL Operations – create tables or views, alter tables or views etc.
- SQL – Data Manipulation Language(DML)
- SQL – DML Operations – insert, update and delete etc.
- SQL – Select
- SQL – Constraints
- SQL – Normalizations
- SQL – Joins and indexes
DATA SCIENCE LIBRARIES: Numpy, Pandas, Scipy, Scikit-learn, Matplotlib MODULE -IV
Introduction to Anaconda/Jupyter Notebook
Numpy:
- Introduction to numpy
- Difference between Python Lists and Numpy
- Creating arrays
- Using arrays and Scalars
- Indexing Arrays
- Array Random functions
- Array Search,Sort
- Array Filter
- Array Input and Output
- Exercise on Numpy
Pandas:
- Introduction to pandas?
- Where it is used?
- Index objects
- Data Structure of Pandas
- Reindex
- Drop Entry
- Selecting Entries
- Data Alignment
- Rank and Sort
- Loc and iloc indexing
- Summary Statics
- Handling Missing Data
- Index Hierarchy
- Exercise on Pandas
Matplotlib: Data Visualization
- Introduction of Data Visualization
- Introduction to Matplotlib
- Types of plots in Matplotlib
- 3D plotting with Matplotlib
- Basic and Specialized Visualization Tools
Scikit-learn
- Machine learning Process Flow
- Feature selection and extraction in machine learning
PROBABILITY AND STATISTICS MODULE – V
- Probability, Mean, Median, SD, Variance
- Probability distributions, Poisson distribution, Binomial distribution.
ARTIFICAL INTELLIGENCE AND MACHINE LEARNING (ML) – MODULE VI
- What is Machine Learning (ML)?
- Introducing Supervised ML
- Introducing Unsupervised ML
- Introducing Reinforcement Or Semi Supervised ML
- Supervised ML Algorithms (Regression and Classification)
- Unsupervised ML Algorithms (Association and Clustering)
- Reinforcement ML Algorithms
MACHINE LEARNING Algorithms – MODULE VII
- Linear Regression
- Polynomial Regression
- Multinomial Regression
- Train/Test method
- KNN
- K Means
- Logistic Regression
- Support Vector Machine 2.5.6
- Decision Tree
- Naïve Bayes
- Ensemble Methods – Random Forest, Boosting and Optimization
- Clustering and PCA
- Recommendation system
- Time Series Analysis
Deep Learning – MODULE VIII
- Introduction to Deep Learning
- Importance of Deep Learning
- Types of Deep Learning
- Introduction to Tensor flow & Keras
- Artificial Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Introduction to Computer Vision, Open CV library
- Recurrent Neural Network
- Introduction to Natural Language Processing
- Using Regex in NLP
- Category of Techniques for NLP
- Spacy vs NLTK
- Tokenization in Spacy
- Stemming and Lemmatization
- Name Entity Recognition