Course Structure:
Module 1: Introduction to Data Science
• What is Data Science
• Evolution of Analytics
• Data Science Components
• Data Scientist Skillset
• Types of Data Scientists
• Introduction to Machine Learning
• Data Science Process
Module 2: Introduction to Python
• Python Environment Setup and Essentials
• Anaconda Python Distribution – Windows, Mac OS, Linux
• Jupyter Notebook Installation
• Variable Assignment
• Understanding Data Types: Integer, Float, String, None, Boolean, Typecasting
• Tuples: Create, Access, and Slice
• Dictionary: Create, View, Access, and Modify
• Studying Basic Operations: ‘in’, ‘+’, ‘*’
Module 3: Computing with Python – NumPy and SciPy
• Mathematical Computing with Python - NumPy
• Understanding NumPy
• ndarray: Purpose, Properties, Types
• ndarray: Class and Attributes
• How to Access Array Elements?
• Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Studying Universal Functions
• What is Shape Manipulation?
• Linear Algebra
• Scientific Computing with Python – SciPy
• Understanding SciPy
• Studying SciPy Sub-packages
• Sub-Packages: Integration and Optimize
• Sub-Packages: Statistics, Weave, I O
• Linear Algebra
Module 4: Data Manipulation and Machine Learning with Python
• Data Manipulation and Machine Learning with Python
• Data Manipulation with Python – Pandas
• Understanding Pandas
• Defining Data Structures
• Data Operations and Data Standardization
• Pandas: File Read and Write Support
• SQL Operation
• Machine Learning with Python – Scikit
• Supervised Learning Models: Linear and Logistic Regression
• Unsupervised Learning Models: Clustering and Dimensionality Reduction
• Model Persistence and Model Evaluation
• Natural Language Processing with Scikit
• NLP Environment Setup & Applications
• NLP Sentence Analysis & Libraries
• Scikit – Built-in Modules & Feature Extraction
• Scikit – Grid Search & Parameters
Module 5: Data Visualization and Web Scraping
• Data Visualization and Matplotlib
• Python Libraries
• Features of Matplotlib
• Line Properties Plot with (x, y)
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple Plots and SubPlots
• Python Web Scraping and Data Science
• The Parser
• Searching & Modifying the Tree
• Printing, Formatting, Encoding
• Practical Hands-on exercises
Module 6: Introduction to Statistics
• Probability Distributions
• Probability Distributions Sampling
• Inferential Statistics
• Hypothesis Testing
• Tests of Hypothesis
• Anova
Module 7: Machine Learning
• Fundamentals of Machine Learning
• Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement learning
• Machine Learning Concepts & Terminologies
Module 8: Supervised Learning
• Predictive Models (Simple/Multiple/Logistic Regression)
• Simple Linear Regression and its Analysis
• Multiple Linear Regression and its Analysis
• Logistic Regression Analysis
• Decision Trees
• Random Forests
• Support Vector Machine
• Bayesian Theory
• K-Nearest Neighbor (K-NN)
• Intro to Dimensionality Reduction
• Real world Project on each concept
Module 9: Unsupervised Learning
• K Means Clustering
• Time Series
• Real world Project on each concept
Module 10: Bonus Course on Power BI
• Data Visualization with Power BI
• Dashboard Integration
ALONG WITH THIS COURSE YOU ALSO GET:
• Interview Preparation
• Resume Building
• Post-Training Engagement