Data Science
Data Science in its less complex terms is tied in with producing basic business esteem from the information by different inventive ways. It can likewise be characterized as a blend of data research, calculations and innovation so as to understand complex logical issues. Data is being by produced by Companies at an exponential pace. The usable Data structure can be distinctive for an alternate segments of individuals working in an association. Data Science Training in Pune encourages us to investigate the information to the granular structure and locate the required bits of knowledge. Information Science is tied in with being explanatory or curious wherein posing new inquiries, doing new investigations and continue learning is a piece of the activity for Data Scientists.

Introduction to Data Science

Data Science Overview
2. Basic Python for Data Science

Introduction to Python

Understanding Operators

Variables and Data Types

Conditional Statements

Looping Constructs

Functions

Data Structure

Lists

Dictionaries

Understanding Standard Libraries in Python

Reading a CSV File in Python

Data Frames and basic operations with Data Frames

Indexing Data Frame
3. Understanding Statistics of Data Science

Introduction to Statistics

Measures of Central Tendency

Understanding the spread of data

Data Distribution

Introduction to Probability

Probabilities of Discreet and Continuous Variables

Central Limit Theorem and Normal Distribution

Introduction to Inferential Statistics
4.Understanding the Confid

Understanding the Confidence Interval and margin of error

Hypothesis Testing

T tests

Chi Squared Tests

Understanding the concept of Correlation
4.1. Predictive Modeling and the basics of Machine Learning

Introduction to Predictive Modeling

Understanding the types of Predictive Models

Stages of Predictive Models

Hypothesis Generation

Data Extraction

Data Exploration

Reading the data into Python

Variable Identification

Univariate Analysis for Continuous Variables

Univariate Analysis for Categorical Variables

Bivariate Analysis

Treating Missing Values

How to treat Outliers

Transforming the Variables

Basics of Model Building

Linear Regression

Logistic Regression

Decision Trees

Kmeans
5. Final Project