Data Science
- Foundations of programming:
Python built-in Data types- Understanding Data Types in Python
- Control flow statements:
- If, Elif and Else
- Definite and Indefinite loops:
- For and While loops
Functions
- Built In Functions in Python
- Paramerized functions
- Writing user-defined functions in Python
Oops
- Class
- Object
- Constructor
Arrays
- List
- List comprehensions and Lambda
- Parsing information with Python
- Dictionaries
- Tuples
Python Programming Language
- Statistical Hypothesis
- Testing Python Hypothesis
- Testing Matplotlib Numpy
Pandas Scipy Python
Lambdas Python
Regular Expressions
Introduction to NumPy
- The Basics of NumPy Arrays
- Computation on NumPy Arrays:
- Universal Functions Aggregations:
- Min, Max
- Everything in Between Computation on Arrays:
- Broadcasting Comparisons,
- Masks, Boolean Logic Fancy
- Indexing Sorting
Arrays Structured Data:
NumPy’s Structured Arrays
Advanced NumPy
Introducing Pandas Objects
- Data Indexing and Selection
- Operating on Data in Pandas,
- Handling Missing Data
- Hierarchical Indexing
Combining Datasets:
- Concat and Append Combining Datasets:
- Merge and Join
- Aggregation and Grouping and Pivot Tables
- Vectorized String
Operations Working with Time Series
High-Performance Pandas:
- eval() and query()
- Visualization with Matplotlib
- Simple Line Plots
- Simple Scatter Plots
- Visualizing Errors,
- Density and Contour Plots
- Histograms,
- Binnings, and Density
- Customizing Plot Legends
- Data Loading, Storage, and File Formats
- Data Cleaning and Preparation Data Wrangling Plotting and Visualization
- Data Aggregation and
- Group Operations
- Time Series
Advanced pandas
- Introduction to Modeling Libraries in Python Data Analysis Examples
- Customizing Colorbars
- Multiple Subplots Text
- and Annotation Customizing Ticks
Customizing Matplotlib:
- Configurations and Stylesheets
- Three-Dimensional Plotting in Matplotlib
- Geographic Data with Basemap Visualization with Seaborn
What Is Machine Learning?
- Introducing Scikit
- Learn Hyper parameters and Model Validation
- Feature Engineering
- Naive Bayes Classification
- Linear Regression Support
- Vector Machines
- Decision Trees and Random Forests Principal Component Analysis
- Manifold Learning k-Means Clustering
- Gaussian Mixture Models
- Kernel Density Estimation
- A Face Detection Pipeline
Tensorflow
- Variables and Placeholders
- TensorFlow – A Neural Network
- TensorFlow Regression
- TensorFlow Classification
- TF Classification
- Saving and Restoring Models
- Convolutional Neural Networks
Introduction to Convolutional Neural Network Section
- Review of Neural Networks
- MNIST
- MNIST Data Overview
Kera
- Deep Nets with Tensorflow
- Abstractions API – Keras
Sql
Tableau

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