Data Science With Python

• Duaration - 3 Months
Overview

It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.

Python Basic Training

It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.

Python Development

It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.

Data Science Overview
• Data Science
• Data Scientists
• Examples of Data Science
• Python for Data Science

Data Analytics Overview
• Introduction to Data Visualization
• Processes in Data Science
• Data Wrangling, Data Exploration, and Model Selection
• Exploratory Data Analysis or EDA
• Data Visualization
• Plotting
• Hypothesis Building and Testing

• Introduction to Statistics
• Statistical and Non-Statistical Analysis
• Some Common Terms Used in Statistics

Data Distribution: Central Tendency, Percentiles, Dispersion
• Histogram
• Bell Curve
• Hypothesis Testing
• Chi-Square Test
• Correlation Matrix
• Inferential Statistics

Python: Environment Setup and Essentials ?
• Introduction to Anaconda
• Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
• Jupyter Notebook Installation
• Jupyter Notebook Introduction
• Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
• Creating, accessing, and slicing tuples
• Creating, accessing, and slicing lists
• Creating, viewing, accessing, and modifying dicts
• Creating and using operations on sets
• Basic Operators: 'in', '+', '*'
• Functions
• Control Flow

Mathematical Computing with Python (NumPy)
• NumPy Overview
• Properties, Purpose, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Copy and Views
• Universal Functions (ufunc)
• Shape Manipulation
• Linear Algebra

Scientific computing with Python (Scipy)
• SciPy and its Characteristics
• SciPy sub-packages
• SciPy sub-packages –Integration
• SciPy sub-packages – Optimize
• Linear Algebra
• SciPy sub-packages – Statistics
• SciPy sub-packages – Weave
• SciPy sub-packages - I O

Data Manipulation with Python (Pandas)
• Introduction to Pandas
• Data Structures
• Series
• DataFrame
• Missing Values
• Data Operations
• Data Standardization
• Pandas File Read and Write Support
• SQL Operation

Machine Learning with Python (Scikit–Learn)
• Introduction to Machine Learning
• Machine Learning Approach
• How Supervised and Unsupervised Learning Models Work
• Scikit-Learn
• Supervised Learning Models - Linear Regression
• Supervised Learning Models: Logistic Regression
• K Nearest Neighbors (K-NN) Model
• Unsupervised Learning Models: Clustering
• Unsupervised Learning Models: Dimensionality Reduction
• Pipeline
• Model Persistence
• Model Evaluation - Metric Functions

Natural Language Processing with Scikit-Learn
• NLP Overview
• NLP Approach for Text Data
• NLP Environment Setup
• NLP Sentence analysis
• NLP Applications
• Major NLP Libraries
• Scikit-Learn Approach
• Scikit - Learn Approach Built - in Modules
• Scikit - Learn Approach Feature Extraction
• Bag of Words
• Extraction Considerations
• Scikit - Learn Approach Model Training
• Scikit - Learn Grid Search and Multiple Parameters
• Pipeline

• Importing and exporting relational information with Sqoop

Data Visualization in Python using Matplotlib
• Introduction to Data Visualization
• Python Libraries
• Plots
• Matplotlib Features:
• Line Properties Plot with (x, y)
• Controlling Line Patterns and Colors
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple Plots
• Subplots
• Types of Plots and Seaborn

Data Science with Python Web Scraping
• Web Scraping
• Common Data/Page Formats on The Web
• The Parser
• Importance of Objects
• Understanding the Tree
• Searching the Tree
• Navigating options
• Modifying the Tree
• Parsing Only Part of the Document
• Printing and Formatting
• Encoding

R Overview (Basic - R)
• R - Environment Setup
• R - Basic Syntax
• R - Data Types
• R - Variables
• R - Operators
• R - Decision Making
• R - Loops
• R - Functions
• R - Strings R - Vectors R - Lists
• R - Matrices
• R - Arrays
• R - Factors
• R - Data Frames
• R - Packages
• R - Data Reshaping

Introduction to R - Data Interfaces
• R - CSV Files
• R - Excel Files
• R - Binary Files
• R - XML Files
• R - JSON Files
• R - Web Data
• R - Database

R - Charts & Graphs
• R - Pie Charts R - Bar Charts R - Boxplots
• R - Histograms R - Line Graphs R - Scatterplots

R - Statistics Analysis (Data Science)
• R - Different types of data
• R - Data summarization
• R - Frequency table
• R - Frequency Distributions
• R - Histogram
• R - Measures of central tendency and dispersion
• R - Skewness and kurtosis
• R - Basic Probability
• R - Conditional Probability
• R - Normal Distribution
• R - Sampling methods
• R - Point and Interval estimation
• R - Central Limit Theorem
• R - Nul and alternative hypothesis
• R - Level of significance
• R - P value
• R - Types of errors
• R - Hypothesis Testing
• R - Simple and Multiple Linear Regression
• R - ANOVA, Interpretation of coefficients
• R - Dummy Variables
• R - Residual Analysis
• R - Outliers
• R - Logistic Regression

Introduction
• Introduction to SPSS
• Data analysis with SPSS: general aspects, workflow, critical issues
• SPSS: general description, functions, menus, commands
• SPSS file management

Input and data cleaning
• Defining variables
• Manual input of data
• Automated input of data and file import

Data manipulation
• Data Transformation
• Syntax files and scripts
• Output management

• Frequencies
• Descriptives
• Explore
• Crosstabs
• Charts

Statistical Tests
• Means
• T - test
• One
• way ANOVA
• Non parametric tests
• Normality tests

Correlation and regression
• Linear correlation and regression
• Multiple regression (linear)

Multivariate analysis
• Factor analysis
• Cluster analysis

Introduction
• Introduction to Artificial Intelligence
• Applications of Artificial Intelligence
• Keras
• Tensorflow

Dimensionality Reduction
• Linear Discriminant Analysis (LDA)
• Principle component Analysis (PCA)
• Practical approach in python

Artificial Neural Network
• Plan of attack
• Activation function
• Backpropagation
• Connectionism
• Practical approach with python

Convolution Neural Network
• Introduction of Convolution Neural Network
• Plan of attack
• Convolution Operation
• ReLU Layers
• Pooling
• Flattening
• Different Layers

Reinforcement Learning
• Agent environment problem
• Reinforcement Process
• Q-learning
• Practical approach with python

Natural Language Processing
• Introduction of NLP
• Application of Natural Language Processing
• Regular expression
• Characters
• Method and function
• Sets
• Example
• Feature Extraction
• Text Mining
• NLTK: Tokanizer, CountVectorizer
• Practical approach with python

Computer vision
• Introduction of computer vision
• How a computer reads an Image
• Application of Computer Vision
• What is OpenCV
• Image Detection with OpenCV
• Practical approach with python