Data Science training course in Erode with Python

Data Science training course in Erode with Python

Learn to use Python as your Data Science tool of choice This course teaches you Python as a tool for data science, and specifically for implementing an advanced Machine Learning algorithm with Python

I. Introduction and Setting Up Your Integrated Analysis Environment

Setting Up Your Integrated Analysis Environment & Tools Overview

IPython Shell

Custom environment settings

Jupyter Notebooks

Script editor

Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib, Seaborn, etc.

Once you complete this module, you will understand some of the unique benefits of using Python for data science / what features make Python particularly well-suited for data science, you will be able to set up a fully functioning Python-based analysis environment, and you will know what each tool is used for in the data science workflow.

II. Using Python to Control and Document Your Data Science Processes

Python Essentials

Data types and objects

Loading packages, namespaces

Reading and writing data

Simple plotting

Control flow

Debugging

Code profiling

Once you complete this module, you will be able to use the Python standard library plus Canopy tools to write, run, debug, and profile programs that control your data science processes (which draw on the scientific packages).

III. Accessing and Preparing Data

Data, Data, Everywhere...

Acquiring Data with Python

Loading from CSV files

Accessing SQL databases

Cleansing Data with Python

Stripping out extraneous information

Normalizing data

Formatting data

Once you complete this module, you will know how to load data from common types of data sources, including structured text files and SQL databases. and you will know some of the common tools used in Python to cleanse and prepare your data for analysis.

IV. Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays, 
Matplotlib, and Seaborn

NumPy Essentials

The NumPy array

N-dimensional array operations and manipulations

Memory mapped files

Data Visualization

2D plotting with Matplotlib

Advanced data visualization with Seaborn

Once you complete this module, you will understand how to use NumPy arrays for efficient numerical processing and how to use NumPy methods such as slicing to write code that is both compact and easy to read and understand. You will know how to use Matplotlib, Seaborn, and NumPy together to explore and visualize your data.

V. Exploring Data with Pandas

Searching for Gold in a Pile of Pyrite

Data manipulation with Pandas

Statistical analysis with Pandas

Time series analysis with Pandas

At the end of this module, you will know how to access some of the core tools used for statistical analysis and data exploration in Python.

VI. Machine Learning with scikit-learn

Predicting the Future Can Be Good for Business

Input: 2D, samples, and features

Estimator, predictor, transformer interfaces

Pre-processing data

Regression

Classification

Model selection

At the end of this module you will have a working understanding of what machine learning tools are available in scikit-learn and how to use them.

Trainer

K.BoopathiKumar

919698548633