Python With Data Science


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Python With Data Science Overview

UpScale's Core Java Training enables you to master the complete Python With Data Science

Modes of Training

Curriculum

Introduction to Data Science with Python

What is analytics & Data Science?p>

Need of Data Science In the Industry

Analytics vs. Data warehousingp>

OLAP, MIS Reporting

Overview of analytics tools & their popularity

Analytics Methodology & problem solving framework

Why Python for data science?

Python Essentials

Starting with Python

Introduction to installation of Python

Introduction to Python Editors & IDE's

Concept of Packages/Libraries - Important packages

List and Dictionary Comprehensions

Basic Operations - Mathematical - string - date

Scientific Distributions used in Python for Data Science

Numpy

Scify

Pandas

Scikitlearn

Statmodels

Nltk

Importing and Exporting Data

Importing Data from various sources

Database Input (Connecting to database)

Viewing Data objects - subsetting, methods

Exporting Data to various formats

Important python modules: Pandas, beautifulsoup

Data Manipulation- Cleansing- Munging

Cleansing Data with Python

Data Manipulation steps

Data manipulation tools

Python Built-in Functions

Python User Defined Functions

Stripping out extraneous information

Normalizing and Formatting of Data

Important Python modules for data manipulation

Data Analysis

Introduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis

Creating Graphs

Important Packages for Exploratory Analysis

Introduction to Statistics

Basic of Statistics

Measures of Central Tendencies and Variance

Probability Distributions

Normal distribution

Central Limit Theorem

Inferential Statistics

Statistical Methods - Z/t-tests

Introduction to Predictive Modelling

Concept of model in analytics and how it is used?

Common terminology used in analytics & modelling process

Popular modelling algorithms

Mapping of Techniques

Different Phases of Predictive Modelling

Data Preparation and Segmentation

Need of Data preparation

Consolidation/Aggregation

Outlier treatment

Flat Liners

Missing values

Missing values

Dummy creation

Variable Reduction

Variable Reduction Techniques

Introduction to Segmentation

Types of Segmentation

Sedmentation Techniques

Cluster evaluation and profiling

Various approaches

Linear Regression

Logistic Regressions

Solving Regression Problem

Solving Classification Problem

Building Linear Regression Model

Understanding standard metrics

Validation of Models

Standard Business Outputs

Time Series Compon

Classification of Techniques

Basic Techniques

Advanced Techniques

Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc.

Projects

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