Quantitative Data management, analysis and Visualization with Python

Introduction

This comprehensive course will be your guide to learning how to use the power of Python to analyze big data, create beautiful visualizations, and use powerful machine learning algorithms. This course is designed for both beginners with basic programming experience or experienced developers looking to make the jump to Data Science and big data Analysis. Python has been one of the most adaptable, and robust open-source languages that are easy to learn and uses powerful libraries for data manipulation and analysis. For many years now, Python has been used in scientific computing and mathematical domains such as physics, finance, oil and gas, and signal processing.This Big Data Analytics with Python course provides a complete overview of data analysis techniques using Python. A Data Scientist is one of the strongest professions today and Python is a crucial skill for such roles.The Big Data Analytics with Python course teaches you to master the concepts of Python programming. Through this training, you will gain knowledge of the essential tools of Data Analytics with Python.

DURATION

10 Days

Course Objective

• Research Design
• Python for Data Science and Machine
• Spark for Big Data Analysis
• Programmatically download and analyse data
• Practise techniques to manage various types of data – ordinal, categorical, encoding
• Learn data visualisation
• Master the art of performing step-by-step data analysis
• Gain information on the roles of a Machine Learning Engineer
• Describe Machine Learning
• Work with real-time data
• Learn tools and techniques for predictive modelling
• Discuss Machine Learning algorithms and their implementation
• Validate Machine Learning algorithms
• Explain Time Series and its related concepts
• Perform Text Mining and Sentimental analysis

WHO SHOULD ATTEND?

• Analytics Team Managers
• Business Analysts who want to comprehend Machine Learning concepts
• Information Architects who want to gain proficiency in Predictive Analytics
• Programmers, Developers, Technical Leads, Architects
• Individuals aspiring to be Machine Learning Engineers
• Professionals who want to develop automatic predictive models using data

Course content

Module1: Basic statistical terms and concepts

·         Introduction to statistical concepts

·         Descriptive Statistics

·         Inferential statistics

Module 2:Research Design

·         The role and purpose of research design

·         Types of research designs

·         The research process

·         Which method to choose?

·         Exercise: Identify a project of choice and developing a research design

Module 3: Survey Planning, Implementation and Completion

·         Types of surveys

·         The survey process

·         Survey design

·         Methods of survey sampling

·         Determining the Sample size

·         Planning a survey

·         Conducting the survey

·         After the survey

·         Exercise: Planning for a survey based on the research design selected

MODULE 4: DATA SCIENCE OVERVIEW

·         Introduction to Data Science

·         Different Sectors Using Data Science

·         Purpose and Components of Python

MODULE 5: DATA ANALYTICS OVERVIEW

·         Data Analytics Process

·         Knowledge Check

·         Exploratory Data Analysis (EDA)

·         EDA-Quantitative Technique

·         EDA – Graphical Technique

·         Data Analytics Conclusion or Predictions

·         Data Analytics Communication

·         Data Types for Plotting

·         Data Types and Plotting

MODULE 6: STATISTICAL ANALYSIS AND BUSINESS APPLICATIONS

·         Introduction to Statistics

·         Statistical and Non-statistical Analysis

·         Major Categories of Statistics

·         Statistical Analysis Considerations

·         Population and Sample

·         Statistical Analysis Process

·         Data Distribution

·         Dispersion

·         Histogram

·         Correlation and Inferential Statistics

MODULE 7 PYTHON ENVIRONMENT SETUP AND ESSENTIALS

·         Anaconda

·         Installation of Anaconda Python Distribution

·         Data Types with Python

·         Basic Operators and Functions

MODULE 8: MATHEMATICAL COMPUTING WITH PYTHON (NUMPY)

·         Introduction to NumPy

·         Activity-Sequence it Right

·         Creating and Printing an nd array

·         Class and Attributes of nd array

·         Basic Operations

·         Copy and Views

·         Mathematical Functions of NumPy

·         Evaluate the datasets containing GDPs of different countries

·         Evaluate the datasets of Summer Olympics 2012

MODULE 9: SCIENTIFIC COMPUTING WITH PYTHON (SCIPY)

·         Introduction to SciPy

·         SciPy Sub Package – Integration and Optimisation

·         SciPy Sub package

·         Demo – Calculate Eigenvalues and Eigenvector

·         Use SciPy to solve a linear algebra problem

·         Use SciPy to define 20 random variables for random values

MODULE 10: DATA MANIPULATION WITH PANDAS

·         Introduction to Pandas

·         Understanding DataFrame

·         View and Select Data Demo

·         Missing Values

·         Data Operations

·         File Read and Write Support

·         Pandas SQL Operation

·         Analyse the Federal Aviation Authority (FAA) dataset using Pandas

·         Analyse the dataset in CSV format given for fire department

MODULE 11: MACHINE LEARNING WITH SCIKIT–LEARN

·         Machine Learning Approach

·         Understand data sets and extract its features

·         Identifying problem type and learning model

·         How it Works

·         Train, test and optimising the model

·         Supervised Learning Model Considerations

·         Scikit-Learn

·         Supervised Learning Models – Linear Regression

·         Supervised Learning Models – Logistic Regression

·         Unsupervised Learning Models

·         Pipeline

·         Model Persistence and Evaluation

·         Analyse a dataset to find the features and response label of it