Fundamentals of Data Science
and Machine Learning

Introduction to Data Science

Need of Data Science

BigData and Data Science’

Data Science and machine learning

Data Science Life Cycle

Data Science Platform

Data Science Use Cases

Skill Required for Data Science

Mathematics For Data Science

Linear Algebra

Vectors

Matrices

Optimization

Theory Of optimization

Gradients Descent

Introduction to Statistics

Descriptive vs. Inferential Statistics

Types of data

Measures of central tendency and dispersion

Hypothesis & inferences

Hypothesis Testing

Confidence Interval

Central Limit Theorem

Probability and Probability Distributions

Probability Theory

Conditional Probability

Data Distribution

Distribution Functions

Normal Distributio

Binomial Distribution 

2: RDBMS: SQL2: RDBMS: SQL

An Introduction to
RDBMS & SQL

Data Retrieval with
SQL

Pattern matching with
wildcards

Basics of sorting

Session summary

Order by clause

Aggregate functions

Group by clause

Having clause

Nested queries

Inner join

Multi join

Outer join

Adding and Deleting
columns

Changing column name
and Data Type

Creating Table from
existing Table

Changing Constraints
Foreign key

Introduction to Stored
Procedures.

3: Python for Data Science: Python for Data Science

An Introduction to Python

Why Python , its
Unique Feature and where to use it?

Python environment
Setup/shell

Installing Anaconda

Understanding the
Jupyter notebook

Python Identifiers,
Keywords

Discussion about
installed module s and packages

Conditional Statement ,Loops and File Handling

Python Data Types and
Variable

Condition and Loops in
Python

Decorators

Python Modules &
Packages

Python Files and
Directories manipulations

Use various files and
directory functions for OS operations

Python Core Objects and Functions

Built in modules
(Library Functions)

Numeric and Math’s
Module

String/List/Dictionaries/Tuple

Complex Data
structures in Python

Python built in
function

Python user defined
functions

4. Introduction to NumPy

Array Operations

Arrays Functions

Array Mathematics

Array Manipulation

Array I/O

Importing Files with
Numpy

5. Data Manipulation with Pandas

Data Frames

I/O

Selection in DFs

Retrieving in DFs

Applying Functions

Reshaping the DFs –
Pivot

Combining DFs

Merge

Join

Inverse, Transpose, Trace,   Norms , Rank etc

Eigen Values & vectors

SVDs

Data Alignment 

6. SciPy

Matrices Operations

Create matrices

Matrices Decomposition

7.Visualization with Seaborn 

Seaborn Installatio

8. Visualization with Matplotlib

Matplotlib
Installation

Matplotlib Basic Plots
& it’s Containers

Matplotlib components
and properties

Pylab & Pyplot

Scatter plots

2D Plots-

Histograms

Bar Graphs

Pie Charts

Box Plots

Customization

Store Plots

9. SciKit Learn

Basics

Data Loading

Train/Test Data
generation

Preprocessing

Generate Model

Evaluate Models

10. Descriptive Statistics

. Data understanding

Observations,
variables, and data matrices

Types of variables

Measures of Central
Tendency

Arithmetic Mean /
Average

Merits & Demerits
of Arithmetic Mean and Mode Merits
& Demerits of Mode and Median

Merits & Demerits
of Median Variance

11. Probability Basics

Notation and
Terminology

Unions and
Intersections

Conditional
Probability and Independence

12. Probability Distributions

Random Variable

Probability
Distributions

Probability Mass
Function

Parameters vs.
Statistics

Binomial Distribution

Poisson Distribution

Normal Distribution

Standard Normal
Distribution

Central Limit Theorem

Cumulative
Distribution function

13.  Tests of Hypothesis

Large Sample Test

Small Sample Test

One Sample: Testing
Population Mean

Hypothesis in One
Sample z-test

Two Sample: Testing
Population Mean

One Sample t-test –
Two Sample t-test

Paired t-test

Hypothesis in Paired
Samples t-test

Chi-Square test

14. Data Analysis

Case study- Netflix

Deep analysis on
Netflix data

 

Numpy: Machine Learning & Scientific Computing

Pandas: Real-World Data Analysis

SciPy: Machine Learning & Scientific Computing

Theano: ML & DL

Scikit-learn: ML & Data Mining

Apache Spark: Next-Generation Big Data Framework

Natural Language Processing:NLP

R Programming:Data Science

MongoDB: NoSQL

Cassandra: NoSQL

Hadoop: Hadoop Architecture & HDFS

Hadoop: Distributed Processing MapReduce Framework

Hadoop: HBase

Hadoop: Hive

Hadoop: Apache Pig

Hadoop: Sqoop

Kafka: Distributed Streaming

Scala: Functional Programming

Apache Spark: Next-Generation Big Data Framework

 

Introduction to Data Science

Exploratory Data Analysis and the
Data Science Process

Three Basic Machine Learning
Algorithms

More Machine Learning Algorithm
and Usage in Applications

Motivating application: Filtering
Spam

Why Linear Regression and k-NN are
poor choices for Filtering Spam

Naive Bayes and why it works for
Filtering Spam

Data Wrangling: APIs and other
tools for scrapping the Web

Feature Generation and Feature
Selection (Extracting

Meaning From Data)

Recommendation Systems: Building a
User-Facing Data

Product

Mining Social-Network Graphs

Data Visualization

Data Science and Ethical Issues

 

Module1 –
Introduction and Importance of Data Science
Module2 – Statistics
Module3 –
Information Visualisation
Module4 – Data
Mining, Data Structures, and Data Manipulation
Module5 –
Algorithms used in Machine Learning
Module6 – Data
Scientist Roles and Responsibilities
Module7 – Data
Acquisition and Data Science Life Cycle
Module8 – Deploying
Recommender Systems on Real-World Data Sets
Module9 –
Experimentation, Evaluation and Project Deployment Tools
Module10 –
Predictive Analytics and Segmentation using Clustering
Module11 – Applied
Mathematics and Informatics
Module12 – Working
on Data Mining, Data Structures, and Data Manipulation
Module13 – Big Data
Fundamentals and Hadoop Integration with R

This advanced training program of Data Science Course In Hyderabad goes with the aim to understand key concepts about:

• To know about the business intelligence and business analysis
• To understand the descriptive statistics of Data analysis
• To excel working with Tableau
• To get introduced to R and data exploration to R
• Understand to create Decision trees.
• To understand Data collection and data mining
• Know the importance of big data technologies
• To get the prior idea about the Loop functions and debugging tools
• To learn running non-parametric tests

Section 1:
Introduction to Data Science

 What is Data Science?

 What is Machine
Learning?

 What is Deep
Learning?

 What is AI?

 Real-time
applications

Section 2:
Introduction to Python

 What is Python?

 Why Python?

 Installing Python

 Python IDEs

 Jupyter Notebook
Overview

Section 3: Python Basics

 Python Basic
Datatypes

 Lists

 IF statements

 Loops

 Tuples

 Functions

 Array

Section 4: Python
Packages

 Pandas

 Numpy

 Sci-kit Learn

 Mat-plot library

Section 5: Data
Pre-processing

 Importing the
libraries

 Importing the dataset

 Missing Data

 Categorical Data

 Feature Scaling

 Splitting the Dataset
into Test and Train

Section 6:
Manipulating Data

 Selecting
rows/observations

 Head and Tail data
display

 Describe Data

 Shape of dataset

Section 7: Statistics
Basics

 Mean

 Median

 Mode

 Skewness

 Normal Distribution

 Probability Basics

 What does probability
mean?

 Types of Probability

 Standard Deviation

 Data deviation &
distribution

 Variance

 Bias-variance
Tradeoff

 Underfitting

 Overfitting

 Distance metrics

Section 8: Linear
Algebra

 Norms

 Euclidean Distance

 Manhattan Distance

 Matrix multiplication

 Vector multiplication

Section 9: Regression
Algorithms – Machine Learning

 Introduction to
Regression

 Algorithm Intuition

 Simple Linear

 Multiple Linear

 Polynomial Regression

Section 10: Classification
Algorithms – Machine Learning

 Introduction to
Classical Algorithms

 Algorithm Intuition

 Logistic Regression

 Support Vector
Classification

 Random Forest
Classification

Section 11: Real-time
project

 Data Science project
environment setup

 Real-time Data
Science project

 Project demonstration

 Expert evaluation and
feedback

Section 12: You made
it!!

 Spark Databox Data
Science certification

 Interview preparation

 Mock interviews

 Resume preparation

 Knowledge sharing
with industry experts

 Counseling to guide
you to the right path in Data Science career

Introduction to Data Science

 Introduction to Big Data

 Roles played by a Data Scientist

 Analyzing Big Data using Hadoop and R

 Methodologies used for analysis

 The Architecture and Methodologies used to solve the Big Data problems

Basic Data Manipulation using R

 Understanding vectors in R

 Reading Data, Combining Data

 Subsetting data

 Sorting data and some basic data generation functions

Machine Learning Techniques Using R

Part-1

 Machine Learning Overview

 ML Common Use Cases

 Understanding Supervised and

Unsupervised Learning

 Techniques, Clustering

 Similarity Metrics

 Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models

Machine Learning Techniques Using R

Part-2

 Understanding K-Means Clustering

 Understanding TF-IDF and Cosine

Similarity and their application to

Vector Space Model

 Implementing Association rule

mining in R

Machine Learning Techniques Using R

Part-3

 Understanding Process flow of

Supervised Learning Techniques

 Decision Tree Classifier

 How to build Decision trees

 Random Forest Classifier

 What is Random Forests

 Features of Random Forest

 Out of Box Error Estimate and Variable Importance

 Naive Bayes Classifier

Introduction to Hadoop Architecture

 Understanding K-Means Clustering

 Understanding TF-IDF and Cosine

Similarity and their application to Vector Space Model

 Implementing Association rule mining in R Integrating R with Hadoop

 Integrating R with Hadoop using RHadoop and RMR package

 Exploring RHIPE (R Hadoop Integrated Programming Environment)

 Writing MapReduce Jobs in R and executing them on Hadoop Mahout Introduction and Algorithm Implementation

 Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout Additional Mahout Algorithms and Parallel Processing using R

 Implementation of different Mahout algorithms

 Random Forest Classifier with parallel processing Library in R

Introduction to Data Science

• Need for Data Scientists
• Foundation of Data Science
• What is Business Intelligence
• What is Data Analysis, Data Mining, and Machine Learning
• Analytics vs Data Science
• Value Chain
• Types of Analytics
• Lifecycle Probability
• Analytics Project Lifecycle

Data

• Basis of Data Categorization
• Types of Data
• Data Collection Types
• Forms of Data and Sources
• Data Quality, Changes and Data Quality Issues, Quality Story
• What is Data Architecture
• Components of Data Architecture
• OLTP vs OLAP
• How is Data Stored?

Big Data

• What is Big Data?
• 5 Vs of Big Data
• Big Data Architecture, Technologies, Challenge and Big Data Requirements
• Big Data Distributed Computing and Complexity
• Hadoop
• Map Reduce Framework
• Hadoop Ecosystem

Data Science Deep Dive

• What is Data Science?
• Why are Data Scientists in demand?
• What is a Data Product
• The growing need for Data Science
• Large-Scale Analysis Cost vs Storage
• Data Science Skills
• Data Science Use Cases and Data Science Project Life Cycle & Stages
• Map-Reduce Framework
• Hadoop Ecosystem
• Data Acquisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats, Quantity and Data Quality
• Resolution Techniques
• Data Transformation
• File Format Conversions
• Anonymization

Intro to R Programming

• Introduction to R
• Business Analytics
• Analytics concepts
• The importance of R in analytics
• R Language community and eco-system
• Usage of R in industry
• Installing R and other packages
• Perform basic R operations using command line
• Usage of IDE R Studio and various GUI

R Programming Concepts

• The datatypes in R and its uses
• Built-in functions in R
• Subsetting methods
• Summarize data using functions
• Use of functions like head(), tail(), for inspecting data
• Use-cases for problem solving using R

Data Manipulation in R

• Various phases of Data Cleaning
• Functions used in Inspection
• Data Cleaning Techniques
• Uses of functions involved
• Use-cases for Data Cleaning using R

Data Import Techniques in R

• Import data from spreadsheets and text files into R
• Importing data from statistical formats
• Packages installation for database import
• Connecting to RDBMS from R using ODBC and basic SQL queries in R
• Web Scraping
• Other concepts on Data Import Techniques

Exploratory Data Analysis (EDA) using R

• What is EDA?
• Why do we need EDA?
• Goals of EDA
• Types of EDA
• Implementing of EDA
• Boxplots, cor() in R
• EDA functions
• Multiple packages in R for data analysis
• Some fancy plots
• Use-cases for EDA using R

Data Visualization in R

• Storytelling with Data
• Principle tenets
• Elements of Data Visualization
• Infographics vs Data Visualization
• Data Visualization & Graphical functions in R
• Plotting Graphs
• Customizing Graphical Parameters to improvise the plots
• Various GUIs
• Spatial Analysis
• Other Visualization concepts

Big Data and Hadoop Introduction

• What is Big Data and Hadoop?
• Challenges of Big Data
• Traditional approach Vs Hadoop
• Hadoop Architecture
• Distributed Model
• Block structure File System
• Technologies supporting Big Data
• Replication
• Fault Tolerance
• Why Hadoop?
• Hadoop Eco-System
• Use cases of Hadoop
• Fundamental Design Principles of Hadoop
• Comparison of Hadoop Vs RDBMS

Understand Hadoop Cluster Architecture

• Hadoop Cluster and Architecture
• 5 Daemons
• Hands-On Exercise
• Typical Workflow
• Hands-On Exercise

Map Reduce Concepts

• Map Reduce Concepts
• What is Map Reduce?
• Why Map Reduce?
• Map Reduce in real world and Map Reduce Flow
• What is Mapper, Reducer, and Shuffling?
• Word Count Problem

Spark Apache Spark

• Introduction to Apache Spark
• Why Spark
• Batch Vs. Real-Time Big Data Analytics
• Batch Analytics – Hadoop Ecosystem Overview
• Real-Time Analytics Options
• Streaming Data – Storm
• In Memory Data – Spark, What is Spark?
• Spark benefits to Professionals
• Limitations of MR in Hadoop
• Components of Spark
• Spark Execution Architecture
• Benefits of Apache Spark
• Hadoop vs Spark

Introduction to Scala

• Features of Scala
• Basic Data Types of Scala
• Val vs Var
• Type Inference
• REPL
• Objects & Classes in Scala
• Functions as Objects in Scala
• Anonymous Functions in Scala
• Higher Order Functions
• Lists in Scala
• Maps
• Pattern Matching
• Traits in Scala
• Collections in Scala

Spark Core Architecture

• Spark & Distributed Systems
• Spark for Scalable Systems
• Spark Execution Context
• What is RDD
• RDD Deep Dive and Dependencies
• RDD Lineage
• Spark Application In Depth and Spark Deployment
• Parallelism in Spark
• Caching in Spark

Spark Internals

• Spark Transformations, Actions, Cluster and SQL Introduction
• Spark Data Frames
• Spark SQL with CSV, JSON, and Database

Spark Streaming

• Features of Spark Streaming
• Micro Batch
• Dstreams
• Transformations on Dstreams
• Spark Streaming Use Case

Statistics + Machine Learning Statistics
What is Statistics?

• Descriptive Statistics
• Central Tendency Measures
• The Story of Average
• Dispersion Measures
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics
• What is Hypothesis testing
• Confidence Level
• Degrees of freedom
• what is pValue
• Chi-Square test
• What is ANOVA
• Correlation vs Regression
• Uses of Correlation and Regression

Machine Learning
Machine Learning Introduction

• ML Fundamentals
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning Techniques
• Clustering
• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models
• Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study
• Implementing Association rule mining
• Case study
• Understanding Process flow of Supervised Learning Techniques
• Decision Tree Classifier
• How to build Decision trees
• Case study
• Random Forest Classifier
• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Case study
• Naive Bayes Classifier
• Case study
• Project Discussion
• Problem Statement and Analysis
• Various approaches to solving a Data Science Problem
• Pros and Cons of different approaches and algorithms
• Linear Regression
• Case study
• Logistic Regression
• Case study
• Text Mining
• Case study
• Sentimental Analysis
• Case study

Python
Getting Started with Python

• Python Overview
• About Interpreted Languages
• Advantages/Disadvantages of Python pydoc
• Starting Python
• Interpreter PATH
• Using the Interpreter
• Running a Python Script
• Python Scripts on UNIX/Windows, Editors and IDEs
• Using Variables
• Keywords
• Built-in Functions
• StringsDifferent Literals
• Math Operators and Expressions
• Writing to the Screen
• String Formatting
• Command Line Parameters and Flow Control

Sequences and File Operations

• Lists
• Tuples
• Indexing and Slicing
• Iterating through a Sequence
• Functions for all Sequences
• Using Enumerate()
• Operators and Keywords for Sequences
• The xrange() function
• List Comprehensions
• Generator Expressions
• Dictionaries and Sets

Deep Dive – Functions Sorting Errors and Exception Handling

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values.
• Sorting
• Alternate Keys
• Lambda Functions
• Sorting Collections of Collections, Dictionaries and Lists in Place
• Errors and Exception Handling
• Handling Multiple Exceptions
• The Standard Exception Hierarchy
• Using Modules
• The Import Statement
• Module Search Path
• Package Installation Ways

Regular Expressionist’s Packages and Object – Oriented Programming in Python

• The Sys Module
• Interpreter Information
• STDIO
• Launching External Programs
• path directories and Filenames
• Walking Directory Trees
• Math Function
• Random Numbers
• Dates and Times
• Zipped Archives
• Introduction to Python Classes
• Defining Classes
• Initializers
• Instance Methods
• Properties
• Class Methods and Data Static Methods
• Private Methods and Inheritance
• Module Aliases and Regular Expressions

Machine Learning Using Python

• Introduction to Machine Learning
• Areas of Implementation of Machine Learning
• Why Python
• Major Classes of Learning Algorithms
• Supervised vs Unsupervised Learning
• Learning NumPy
• Learning Scipy
• Basic plotting using Matplotlib
• Machine Learning application

Supervised and Unsupervised learning

• Classification Problem
• Classifying with k-Nearest Neighbours (kNN)

Algorithm

• General Approach to kNN
• Building the Classifier from Scratch
• Testing the Classifier
• Measuring the Performance of the Classifier
• Clustering Problem
• What is K-Means Clustering
• Clustering with k-Means in Python and an

Application Example

• Introduction to Pandas
• Creating Data Frames
• GroupingSorting
• Plotting Data
• Creating Functions
• Converting Different Formats
• Combining Data from Various Formats
• Slicing/Dicing Operations

Scikit and Introduction to Hadoop

• Introduction to Scikit-Learn
• Inbuilt Algorithms for Use
• What is Hadoop and why it is popular
• Distributed Computation and Functional Programming
• Understanding MapReduce Framework Sample MapReduce Job Run

Introduction to Data Science.

Python for Data Science.

Data Analytics using R
Programming.

Machine Learning in Python.

Hadoop & Spark.

Deploying Models on Azure &
AWS.

Simple Linear Regression and Multiple
Linear Regression.

Generalized Regression Models,
Forecasting.

Imputation Techniques (missing
value analysis).

Data Mining, Text Mining.

Artificial Intelligence.

Tableau & Statistics.

Julia.

Supervised Learning (machine
Learning) & Deep Learning.

Linux_OS_Basics and Shell Scripting

Linux_Commands

File_System

vi editor

Advanced_Linux_Commands

System_Administration

Advanced_System_Administration

Grep

Shell_Scripting with examples(All
types of loops)

Regular_Expressions

SED

AWK

MODULE 1: Introduction To Python – Data Science

Installation of Anaconda setup
(Data Science Development Environment)

Installation of Pycharm

Working with Python List , List
operation , Functions

Python Tuple , working and
functions

Sets and Dictionary -operations
and Working with them

Python More on Strings

Python Dates and Times

More on functions

Advanced Python Lambda

List Comprehensions

MODULE 2: Data Analysis

Data Wandering

All about files Files

importing and exporting data with
CSV files

XLRD module – working with xls
.xlsx formats

Json data

XML data

Relational data Bases

Sql in python

Data quality Analysis

DATA MANIPULATION – Cleaning – Munging – Cleansing Data with Python

strong>Data Manipulation
steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived
variables, sampling, Data type conversions, renaming, formatting etc)

Data manipulation tools(Operators,
Functions, Packages, control structures, Loops, arrays etc)

Python Built-in Functions (Text,
numeric, date, utility functions)

Python User Defined Functions

Stripping out extraneous
information

Normalising data

Formatting data

Important Python modules for data
manipulation (Pandas, Numpy, re, math, string, datetime etc)

DATA VISUALIZATION

Introduction exploratory data
analysis

Descriptive statistics, Frequency
Tables and summarization

Univariate Analysis (Distribution
of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs,
Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line
chart/histogram/ boxplot/ scatter/ density etc)

Important Packages for Exploratory
Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

DATA ANALYSIS WITH PANDAS

The Series Data Structure

Querying a Series

The Data-Frame Data Structure

Data-Frame Indexing and Loading

Querying a Data-Frame

Indexing Data-frame

Understanding business problem

Selecting columns from Pandas Data
Structures

Treating with missing values,
outliers, NaN values

Creating new columns

Aggregate data ( use: groupby,
merge, pivot, lambda)

Identifying unique values in data

Filter Data

Using basic functionality of
Pandas API

MODULE 3: Mathamatics    

STASTISTICS

Basic Statistics – Measures of
Central Tendencies and Variance

Building blocks – Probability
Distributions – Normal distribution – Central Limit Theorem

Inferential Statistics -Sampling –
Concept of Hypothesis Testing

Statistical Methods – Z/t-tests(
One sample, independent, paired), Anova, Correlations and Chi-square

Important modules for statistical
methods: Numpy, Scipy, Pandas

PROBABILITY

Probability , Conditional
Probability

Basic of Probability, Independent
and Dependant events

Conditional Probability and Bayes
Theorem

Continuous Probability
Distributions

Mean, Median, Mode, Range

Determination of statistical
techniques

Standard Deviation, Variance,
Covariance, Correlation

outliners

Distribution of Data – Normal,
Binomial, Gaussian

Different types of Data

Continuous , Categorical, Range

Testing of Hypothesis – which
covers

Level of Significance (LOS), Level
of Confidence, P-Value, T test, Z-test, ANOVA Test, CHI -Square Test

MODULE 4: Machine Learning

SUPERVISED LEARNING AND MODEL BUILDING

Process of Machine Learning

Model Building based on Data sets

Splitting Data: Training and Test
sets

Regression Analysis (Linear,
Multiple, Logistics Regression)

Classification concepts and
Distance Functions

K-nn Algorithm concept and
demonstration with data sets

Bayes Classification concept and
demonstration with data sets

Decision Tree Algorithm concept
and demonstration with data sets

Random Forests – Ensembling
Techniques and Algorithms

UNSUPERVISED LEARNING AND MODEL BUILDING

Unsupervised Learning and Clustering
Techniques

Centroid-based Clustering: K- Mean
Algorithm concept and demonstration

Hierarchical Clustering concepts
and Applications

Density-based Clustering: DBSCAN
Algorithm concept and demonstration

DIMENSION REDUCTION TECHNIQUES

Dimension Reduction Introduction

Why Dimension Reduction Required

LDA (Linear Discriminant Analysis)
concept and applications

PCA (Principle Component Analysis)
concept and applications

TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

Introduction – Applications

Time Series Components( Trend,
Seasonality, Cyclicity and Level) and Decomposition

Classification of
Techniques(Pattern based – Pattern less)

vBasic Techniques – Averages,
Smoothening

Advanced Techniques – AR Models,
ARIMA

DATA SCIENCE PROJECTS WITH DATA SETS

Applying different algorithms to
solve the business problems and bench mark the results

 

Module 1 –
Introduction to Python

What is Python and brief history

Why Python and who use Python

Discussion on Python 2 and 3

Unique features of Python

Discussion on various IDE’s

Demonstration of practical use cases

Python use cases using data analysis

Module 2 – Setting up
and Installations

Installing python

Setting up Python Development Environment

Installation of Jupyter Notebook

How to access our course material using Jupyter

Write your first program in Python

Deployment on local and cloud platforms using Google Colab

Module 3 – Python
Object And Data Structures Operations

Introduction to Python objects

Python built-in functions

Number objects and operations

Variable assignment and keywords, String objects and
operations

Print formatting with strings

List objects and operations

Tuple objects and operations

Dictionary objects and operations

Sets and Boolean

Object and data structures assessment test

Module 4 – Python
Statements

Introduction to Python statements

If, elif and else statements

Comparison operators

Chained comparison operators

What are loops

For loops

While loops

Useful operator

List comprehensions

Statement assessment test

Game challenge

Module 5 – UDF
Functions and Methods

Methods

What are various types of functions

Creating and calling user defined functions

Function practice exercises

Lambda Expressions

Map and filter

Nested statements and scope

Args and kwargs in Python

Functions and methods assignment

Milestone Project using Python

Module 6 – File and
Exception Handling

Process files using python

Read/write and append file object

File functions

File pointer and operations

Introduction to error handling

Try, except and finally

Python standard exceptions

User defined exceptions

Unit testing

File and exceptions assignment

Module 7 – Python
Modules, Packages & Inbuilt Modules

Python inbuilt modules

Creating UDM-User defined modules

Passing command line arguments

Writing packages

Define PYTHONPATH

__name__ and __main__

Module 8 – OOPs
Concepts in Python

Object oriented features

Implement object oriented programming with Python

Creating classes and objects

Creating class attributes

Creating methods in a class

Inheritance

Polymorphism

Special methods for class

Assignment – Creating a python script to replicate deposits
and withdrawals in a bank with appropriate classes and UDFs

Module 9 – Advanced
Python Modules

Collections module

Datetime

Python debugger

Timing your code

Regular expressions

StringIO

Python decorators

Python generators

Module 10 – Package
Installation and Parallel Processing

Install packages on python

Introduction to pip, easy install

Multithreading

Multiprocessing

Module 11 –
Introduction to Machine Learning with Python

Understanding Machine Learning

Scope of ML

Supervised and Unsupervised learning

Milestone Project – 2

Module 12 – Data
Analysis with Python

Introduction to data analysis

Why Data analysis?

Data analysis and Artificial Intelligence Bridge

Introduction to Data Analysis libraries

Data analysis introduction assignment challenge

Module 13 – Data
Analysis Using Numpy

Introduction to Numpy arrays

Creating and applying functions

Numpy Indexing and selection

Numpy Operations

Exercise and assignment challenge

Module 14 – Pandas
and Advanced Analysis

Introduction to Series

Introduction to DataFrames

Data manipulation with pandas

Missing data

Groupby

Merging, joining and Concatenating

Operations

Data Input and Output

Pandas in depth coding exercises

Text data mining and processing

Data mining applications in Data engineering

POC – Analysis of e-commerce dataset using pandas

POC – Getting insights on employee salaries data using data
analysis in python

Module 15 – Data
Visualization with Python

Matplotlib

Plotting using Matplotlib

Plotting Numpy arrays

Plotting using object-oriented approach

Subplots using matplotlib

Matplotlib attributes and functions

Matplotlib exercises

Seaborn Visualization

Categorical Plot using Seaborn

Distributional plots using Seaborn

Matrix plots

Grids

Seaborn exercises

Project- Getting insights using python analysis and
visualizations on finance credit score data.

Assignment- Pandas built-in data visualization Data visualization

Module 15 – Data
Visualization with Python

Comparison Between Tableau & Programming Based Data
Visualization Need Of Tableau Types Of Data Sources Supported By Tableau For
Report Development How To Build Report & Dashboard in Tableau How To Build
Charts In Tableau Data Visualization Using Tableau Features

Module 16 – Mathematics and Statistics for Data Science

Need of Mathematics for Data Science

Exploratory data analysis (EDA)

Numeric Variables

Qualitative and Quantitative Analysis

Types of Data Formats

Measuring the Central Tendency – The Model

Measuring Spread – Variance and Standard Deviation

Euclidean Distance

Confidence Coefficient

Understanding Parametric Tests

Module 17 – Machine
Learning Algorithms

Introduction to Data Science

Introduction to Artificial Intelligence

Introduction to Machine Learning

Need of Machine learning in forecasting

Demand of forecasting analytics in current industrial trends

Introduction to Machine Learning Algorithms Categories

Introduction to Natural Language Processing (NLP)

Introduction to Deep Learning

Linear Regression with Python

Introduction to Regression

Exercise on Linear Regression using Scikit Learn Library

Project on Linear regression using USA_HOUSING data

Evaluation of Linear regression using python visualizations

Practice project for Linear regression using advertisement
data set to predict appropriate advertisements for users.

K- Nearest neighbours using Python

Exercise on K-Nearest neighbors using Sci-kit Learn Library

Project on Logistic regression using Dogs and horses’
dataset

Getting the correct number of clusters

Evaluation of model using confusion matrix and
classification report

Standard scaling problem

Practice project on KNN algorithm.

Decision tree and Random forest with python

Intuition behind Decision trees

Implementation of decision tree using a real time dataset

Ensemble learning

Decision tree and random forest for regression

Decision tree and random forest for classification

Evaluation of the decision tree and random forest using
different methods

Practice project on decision tree and random forest using
social network

Data to predict if someone will purchase an item or not

Support Vector Machines

Linearly separable data

Non-linearly separable data

SVM project with telecom dataset to predict the users
portability

Principal Component Analysis

Introduction to PCA

Need for PCA

Implementation to select a model on breast-cancer dataset

Model evaluation

Bias variance trade-off

Accuracy paradox

CAP curve and analysis

Clustering in unsupervised learning

K-means clustering intuition

Implementation of K-means with Python using mall customers
data to implement clusters on the basis of spending and income

Hierarchical clustering intuition

Implementation of Hierarchical clustering with python

Association Algorithms

A priori theory and explanation

Market basket analysis

Implementation of Apriori

Evaluation of association learning

POC – To make a model to predict the relationship between
frequently bought products together on the given dataset from a supermarket.

Module 18 – Natural
Language Processing with NLTK

Introduction to Natural Language processing

NLTK Python library

Data stemming technique

Data Vectorization

Exercise on NLTK

POC- Apply NLP techniques to understand reviews given by
customers in a dataset and predict if a review is good/bad without human
intervention.

Module 19 – Deep
Learning with TensorFlow and Keras

Neural Network and Deep Learning

What is TensorFlow?

TensorFlow Installation

TensorFlow basics

TensorFlow with Contrib Learn

TensorFlow Exercise

What is Keras?

Keras Basics

Pipeline implementation using Keras

MNIST implementation with Keras

Module 20 – Rest API
with Flask and Python

REST principles

Creating application endpoints

Implementing endpoints

Using Postman for API testing

Module 21 – Rest API
Integration with Databases for Web App Development

CRUD operations on database

REST principles and connectivity to databases

Creating a web development API for login registers and
connecting it to the database

Deploying the API on a local server

Module 22 – Major
Project

Project use cases Introduction

Project Scenarios

Project life cycle

What is version controlling in project management

What is GitHub

Significance of GitHub in project management

Code submission for testing and deployment

Predictive analytics tools and techniques

Project best practices