Data Science using Python

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.


Outcome
  • 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

Course Overview

Introduction

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

Introduction to Pandas

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

Data Visualization

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

Applied Statistics

Introduction and Statistics
What is Machine Learning
Machine Learning Real World Example
Statistics
Bias and Variance
Covariance and Correlations
Standard Deviations
Probability
Scikit-learn

Data Processing & Machine learning

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


Prerequisites
  • Eagerness to learn
  • Laptop
Duration

2-2.5 hours/day
Total 20 days (40-45 hours)


Fee per head

₹ 6500

*fee discounts available at selective locations.

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