Price  25,000.00  22,000.00

Course Features
Language: English
35 Hours of Training
10 Lessons
Study Level: Advanced
Certificate of Completion


Data Science with Python Course training

Data Science with Python Course

  • 23rd Feb 2019 (5 Weekends) | Sat-Sun Training Mode: Online | 02:00 PM – 05:00 PM IST
  • 23rd March 2019 (5 Weekends) | Sat-Sun Training Mode: Online | 02:00 PM – 05:00 PM IST

Data Science with Python Course

Data Science with Python course would teach you from the onset how to handle data. Data Science with Python certified course would teach you the best of data science with the Python programming language. While it is true that data analysis with python is quite easy, this course would help break down the process, ensuring that you understand all. This course would discuss the basics of python and how it can be efficiently used for data science. You would also learn about the various functions and control flows of python. Also, this course would present you with data visualizations and examples that would help you create your own visuals.

Main Features

  • Intensive instructor-led teaching, assistance, supervision, and examples
  • Would help you gain expertise with numerous hands-on examples and assignments
  • Would help you master the various algorithms of python and how it can be applied to data science.
  • Real world application of data science with python

Learning Objectives

For Individuals
  • Would offer self-paced learning contents, designs, and models that have been created by expert data scientists.
For Businesses
  • Would offer them cooperate solutions that would help solve certain organizational problems
  • Would offer advanced and enhanced individual and team data analysis
  • Would offer enhanced individual and team reporting

Data Science with Python Course Description

First and foremost, python is an efficient programming language that has been used for different applications. This has made more people look into how it could be used to improve data science.

  • Would teach you the basics of data science
  • Would teach you the various algorithms and applications of python
  • Would teach you ways to create amazing visuals
  • Would also teach you the various control flows as well as functions of the Python programming language.
  • This course was designed for anyone that has a deep passion for data science.
  • Was designed for anyone that seeks to venture into the data science industry
  • Anyone that seeks to learn more about python and its real-world application.
  • Customize plots for real world application
  • How to handle the various structures like; Numpy arrays, python list etc.

Data Science with Python Curriculum

1. Introduction

1.1 Introduction to Data Science


Key Elements of Data Science
Data Warehousing
Business Intelligence
Data Visualization
Data Mining
Machine Learning
Artificial Intelligence
Cloud Computing
Big Data

1.2 Artificial Intelligence: A preview


What is Artificial Intelligence & its importance
Artificial Intelligence vs Machine Learning

2. Getting Started with Machine Learning

2.1 Overview of Machine Learnin

Quick tour of the types of machine learning


What is Machine Learning (ML)?
How machines learn
Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement.
Basics of Classification, Regression and Clustering algorithms
Creating your first Prediction Model
Training & Model evaluation
Choosing Machine Learning algorithm

3. Back to Basics (Maths with Statistics)

3.1 A quick refresh on basic intermediate maths:


A quick refresh on basic intermediate maths:

Linear Algebra (Vectors, Matrix, Eigen Values)
Probability and Statistics
Hypothesis testing

4. Getting Started with Python

4.1 A quick crash course on basics of Python


A quick crash course on basics of Python

What is Python
Working with Python
Basic scripts on
Read, write, data handling
Conditions (if-else)
Code modularization
Scikit-Learn package
Basic visualization

5. Data Processing for Machine Learning

5.1 Introduction to Data Processing


Data Collection & Preparation
Data Mugging
Outlier Analysis
Missing value treatment
Feature Engineering
Data Transformation
Normalization vs Standardization
Creating Dummies
Dimensionality Reduction
Principal Component Analysis

6. Advanced Machine Learning Algorithm

6.1 Overview to Machine Learning Algorithms


Supervised Machine Learning algorithms
Linear Regression
Logistic Regression
Decision/Classification Tree
Ensemble Models
Random Forest
K-Nearest Neighbours (KNN)
Naive Bayes
Neural Network (Deep Learning)
Support Vector Machine
Unsupervised Machine Learning algorithms
Clustering with K-means Clustering
Bias-Variance Trade off
Parameter tuning & grid search optimization


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