**Data science** is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.

- Create a firm grip on Python and it's applications in Data Science domain
- Understand the various elements of Data Science
- Learn the art of processing large pile of Data to make sense out of it
- Hands-on experience on Data Manupulation and Machine Learning

**What is Data Science?**

What does Data Science involve?

Application fields

Data Analyst, Data Scientist

Getting Started with Jupyter Notebook

Introduction to the Open Data Science

Tools of Data Science

Introduction to Python

**Introduction to the Basics of Python Programming**

Operators

Data Types (Numbers, String, List, Tuple, Dictionary)

Loops: while & for

Conditionals: if-else

Functions: Defining Functions, Anonymous Functions

**Scientific Computing with Python - Numerical Python (NumPy)**

Importance of Numpy

Array Creation

Data Types

Array Methods

Array operations

**Pandas Data Structure**

Pandas Data Structures

Series & Data Frame

Basic Functions on Data Frame

Indexing & Selecting Data

**Analysis with Pandas**

Fetch data and information stored in a dataset

Handling Missing Data

Managing data & analysis

Data Analysis Scenarios

**Simple & Multi-line Plots, Multiple Figures with Matplotlib**

Linestyles and Color

Mutiple Lines on Same Plot

Controlling Line Properties

Adding Lables, Gridlines, Annotations

X and Y Ticks and Rotations

Legends

Working with Multiple Figures and Axes

Share X and Y Axis

Adding Subplots

**Creating Different Types of Plots**

Line Graphs

Bar Plots

Histograms

Box Plot

Stacked Plots

Scatter Plot

Pie Chart

**Introduction and Statistics**

What is Machine Learning

Machine Learning Real World Example

Statistics

Bias and Variance

Covariance and Correlations

Standard Deviations

Probability

Scikit-learn

**Introduction**

Loading Datasets

Feature Selection

Split Train and Test Data

Types of ML Algorithms

**Supervised Learning**

Supervised Learning Introduction

Supervised Learning Algorithms

Classification

SVM

SVC

Regression

SVR

KNN

**Unsupervised Learning**

Unsupervised Learning Introduction

Unsupervised Learning Algorithms

Clustering

K-Means Clustering

Naïve Bayes

Random Forest

- Eagerness to learn
- Laptop

2-2.5 hours/day

Total 20 days (40-45 hours)

**Fee per head**

Start your growth with us today!