Business Analytics using Python Training Course

  Lecture-1 Introduction to Business Analytics
Live Lecture 
·      Business Analytics

·      Describe the evolution of analytics

·      Describe the differences between analytics and analysis

·      Explain the concept of insights

·      Describe the broad types of business analytics

·      Describe how organisations benefit from using analytics

·      Practical Exercise

  Lecture-2 Understanding Data
Live Lecture 
·      Importance of data in business analytics

·      Differences between data, information and knowledge

·      The various stages that an organization goes through in terms of data maturity

·      Practical Exercise

  Lecture-3 Business Analytics, Business Intelligence and Data Mining
Live Lecture 
·      Differences between Business Analytics and Business Intelligence

·      Describe the two major components within Business Analytics and Business Intelligence

·      Data Mining technique helps both Business Intelligence and Business Analytics

·      Analytical Decision-Making Process

·      Analysing Business Problems

·      Practical Exercise

  Lecture-4 Social Media Analytics
Live Lecture 
·      Capabilities social media analytics

·      Common goals of social media analytics

·      Practical Exercise

  Lecture-5 Python for Analytics
Live Lecture 
·      Introduction to Python Installation

·      Jupyter Notebook Introduction

·      Practical Exercise

  Lecture-6 Python Basics
Live Lecture 
·      What is Python?

·      Progress of Python

·      Success of Python

·      Programming Model of Python

·      Python Programming Features

·      Commands for common tasks and control

·      Essential Python programming concepts & language mechanics

·      Python Installation

·      Introduction to Python using Jupyter Notebook

·      Simple Input/Output

·      Basic Data Types

·      Control Structures

·      Arithmetic Operators

·      Logical Operators

·      Practical Exercise

  Lecture-7 Python Programming
Live Lecture 
·      Strings,

·      Lists

·      Tuples

·      Dictionaries

·      Functions

·      Parameters

·      Arguments

·      Recursion

·      Data Processing using Pandas and Nampy

·      Introduction to Modules & Packages

·      Generators

·      Errors & Exception Handling

·      Practical Exercise

  Lecture-8 FILE Input/Output
Live Lecture 
·      Path and Directory

·      File Operations

·      Reading and Writing to Files

·      Advance File I/O

·      Practical Exercise

  Lecture-9 Pandas
Live Lecture 
·      Pandas Introduction

·      Series, Data Frames and csvs

·      Data from urls

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Selecting and Viewing Data with Pandas Part 2

·      Manipulating Data

·      Manipulating Data 2

·      Manipulating Data 3

·      Practical Exercise

  Lecture-10 numpy
Live Lecture 
·      Mathematical Computing with Python (numpy)

·      Numpy Introduction

·      Numpy datatypes and Attributes

·      Creating numpy Arrays

·      Numpy Random Seed

·      Viewing Arrays and Matrices

·      Manipulating Arrays

·      Standard Deviation and Variance

·      Reshape and Transpose

·      Dot Product vs Element Wise

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into numpy Arrays

·      Practical Exercise

  Lecture-11 Basic Statistical Concepts and Types of Data
Live Lecture 
·      Statistics and its use in business

·      Types of data

·      Basic statistical concepts

·      Various techniques for sampling

·      Frequency distributions

·      Various measures of central tendency

·      Different measures of dispersion

·      Different measures of shape

·      Practical Exercise

  Lecture-12 One-way Analysis of Variance
Live Lecture 
·      Explain the concept of ANOVA

·      Calculate ANOVA using Python

·      Test a hypothesis using ANOVA

·      Practical Exercise

  Lecture-13 Correlation
Live Lecture 
·      Statistical relationships

·      Understand the measure of correlation

·      Correlation between two datasets using Python

·      Concepts of correlation versus causation

·      Practical Exercise

  Lecture-14 Linear Regression
Live Lecture 
·      Two data series using linear regression

·      To forecast values using linear regression in Python

·      K-Means Clustering

·      What is clustering?

·      K-Means Clustering using python

·      NbClust

·      Practical Exercise

  Lecture-15 Time series
Live Lecture 
·      Introduction to time series data

·      Time series forecasting using Moving Average

·      Time series forecasting using Naïve forecasting

·      Practical Exercise

  Lecture-16 Linear Programming
Live Lecture 
·      Explain the concept of linearity

·      Describe linear programming

·      Formulate a linear programming problem

·      Linear Programming – Allocation Models

·      Describe allocation models in linear programming

·      Solve allocation model problems in linear programming using Python

·      Practical Exercise

  Lecture-17 Linear Programming – Covering Models
Live Lecture 
·      Describe covering models in linear programming

·      Solve covering model problems in linear programming using Python

·      Practical Exercise

  Lecture-18 Text Mining
Live Lecture 
·      The concepts of text-mining

·      Use cases

·      Text Mining Algorithms

·      Quantifying text

·      TF-IDF

·      Beyond TF-IDF

·      Data Mining vs. Text Mining

·      Text Mining and Text Characteristics

·      Predictive Text Analytics

·      Text Mining Problems

·      Prediction & Evaluation

·      Python as a Data Science Platform

·      Practical Exercise

  Lecture-19 Text mining modeling using NLTK
Live Lecture 
·      Text Corpus

·      Sentence Tokenization

·      Word Tokenization

·      Removing special Characters

·      Expanding contractions

·      Removing Stopwords

·      Correcting words: repeated characters

·      Stemming & lemmatization

·      Part of Speech Tagging

·      Feature Extraction

·      Bag of words model

·      TF-IDF model

·      Text classification problem

·      Building a classifier using support vector machine

·      Practical Exercise

  Case Studies