### 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.

1. 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

• K-means

5. Final Project