INTRODUCTION TO DATA SCIENCE

  • The Data Science Overview
  • Brief Introduction to Big data and Data Analytics
  • Life cycle of data science
  • what does Data scientist Do
  • Tools and Technologies used in data Science.

STATISTICS

  • Mean, Median, Mode,
  • Variance, Standard deviation
  • Probability
  • Permutations
  • Combinations
  • Bayes theorem
  • Null Hypothesis
  • Quartile
  • Interquartile
  • Covariance
  • correlation
  • causality
  • Sample and Population
  • Hypothesis, Types of Hypothesis
  • Types of tests based on features of random variables
  • Chi square test

PYTHON PROGRAMMING BASICS

  • Python Overview
  • Python 3 Overview
  • Python Identifiers
  • Various Operators
  • Getting input from User
  • Comments and Multi line Comments

MAKING DECISIONS AND LOOP CONTROL

  • Simple if Statement
  • if-else Statement
  • if-else-if Statement
  • Introduction to while Loops
  • Introduction to For Loops
  • continue, break and pass

 

DATA TYPES: LIST, TUPLES AND DICTIONARIES

  • Python Lists
  • Tuples, Dictionaries
  • Accessing Values
  • Basic Operations
  • Indexing, Slicing and Matrices
  • Built-in Functions & Methods
  • Exercises on List
  • Tuples and Dictionary

FUNCTIONS AND MODULES

  • Functions
  • Why Defining Functions
  • Calling Functions with Multiple Arguments
  • Anonymous Functions
  • Lambda Using Built-In Modules
  • User-Defined Modules,
  • Decorators Iterators and Generators

FILE I/O AND EXCEPTIONAL HANDLING

  • Opening and Closing Files,
  • Open Function
  • File Object Attributes
  • Close Method, Read
  • Exception Handling
  • the try-finally Clause
  • Raising an Exceptions

NUMPY

  • Array Creatio
  • Printing Arrays
  • Basic Operations- Indexing
  • Slicing and Iterating Shape Manipulation – Changing shape, splitting of array

PANDAS

  • Importing data into Python
  • Pandas Data Frames
  • Indexing Data Frames
  • Basic Operations with Data frame
  • Renaming Columns
  • Subletting and Filtering a data frame

MATPLOTLIB

  • Plot, Controlling Line Properties
  • Working with Multiple Figures and Histograms

MySQL FOR DATA SCIENCE

  • Introduction to SQL, Retrieving Data
  • Updating Data, Inserting Data, Deleting Data
  • Sorting and Filtering Data
  • Create connection to the data base using python
  • Creating a data base, Check if data base exists
  • Creating a table
  • Check if table exists and Select records from the table with python

MACHINE LEARNING

INTRODUCTION TO MACHINE LEARNING

  • Machine Learning?
  • What is the Challenge?
  • Supervised Learning and Unsupervised Learning

SUPERVISED LEARNING

LINEAR REGRESSION

  • Linear Regression with Multiple Variables
  • Disadvantage of Linear Models
  • Interpretation of Model Outputs
  • Case study on Application of Linear Regression

LOGISTIC REGRESSION

  • Why Logistic Regression
  • Classification Cost function for logistic regression
  • Application of logistic regression to multi-class classification
  • Confusion Matrix
  • Case study on to classify using logistic Regression

DECISION TREES

  • Decision Tree
  • data set, how to build decision tree?
  • Understanding Kart Model,
  • Classification Rules- Over fitting Problem, Model a decision Tree.

RANDOM FOREST

  • Random Forest
  • data set, how to build Random Forest?
  • Ensemble Techniques – Boosting, Bagging
  • Gradient Boost, XG Boost
  • Classification Rules
  • Regression Rules

K NEAREST NEIGHBOURS

  • K Nearest Neighbors
  • data set, how to build K Nearest Neighbors?
  • Data Set
  • Nearest Neighbors
  • Distance Between Two Points
  • Euclidian Distance and Manhattan Distance, Methods
  • Choosing the Best K Value Classification Rules
  • Regression Rules

NAÏVE BAYES

  • Naïve Bayes, data set
  • how to build Naïve Bayes?
  • Data Set, Types of Events
  • Conditional Probability
  • Bayes Theorem Classification Rules
  • Practical Example of Bayes Theorem