DIPLOMA IN DATA SCIENCE

COURSE 1 : Python for Data Science

The following topics are covered here

Module 1 - Introduction to Data Science with Python

Installing Python

Programming basics

Native Data types

Module 2 - Python Basics: Basic Syntax, Data Structures

Data objects

Math

Comparison Operators

Condition Statements

Loops

Lists

Tuples

Sets

Dicts

Functions

Module 3 - Numpy Package

Numpy overview

Array

Selecting Data

Slicing

Iterating

Manuplications

Stacking

Splitting Arrays

Functions

Module 4 - Pandas Package

Pandas overview

Series and DataFrame

Manuplication

Module 5 - Python Advanced: Data Mugging with Pandas

Histogramming

Grouping

Aggregation

Treating Missing Values

Removing Duplicates

Transforming data

Module 6 - Python Advanced: Visualization with MatPlotLib

Importing MatPlotLib & Seaborn Libraries

Creating basic chart : Line Chart, Bar Charts and Pie Charts

Ploting from Pandas object

Saving a plot

Object Oriented Plotting : Setting axes limits and ticks

Multiple Plots

Plot Formatting : Custom Lines, Markers, Labels, Annotations, Colors

Satistical Plots with Seaborn

Module 7 - Exploratory Data Analysis:

Data Cleaning

Data Wrangling

Module 8 - Exploratory Data Analysis: Case Study

COURSE 2 : SQL for Data Query

The following topics are covered here

Module 1: SQL and RDBMS introduction

Basics of SQL

Essential commands to create and manage DB

Module 2: SELECT Query in SQL

Retrieve data from SQL data base through complex select queries

Module 3: Connecting Tables in Data Base Query

Left Join

Right Join and Inner Join

Module 4: Python SQL query to retrieve from any SQL database

Module 5: Hands-On Project

Project to retrieve data from live SQL server with queries as per the data requirement, in line with Data Science projects.

COURSE 3 : Hadoop - Big data Foundation

The following topics are covered here

Module 1: Introduction to Big Data

What is Big Data?

Why we need it?

Module 2: Big Data Concepts

Core concepts of Big Data

Module 3: Hadoop Installation and configuration

Hadoop Installation on various platforms.

Module 4: Hadoop – Simple use case deployment

Simple use-case with Hadoop

COURSE 4 : Data Science Foundation

The following topics are covered here

Module 1: Data Science Introduction

What is Data Science?

Evolution of Data Science

Module 2: Data Mining vs Business Analytics vs Data Science

Difference between popular terminologies

Module 3: Classification of Business Analytics

Descriptive

Predictive

Discovery and Prescriptive Analytics

Module 4: Artificial Intelligence vs Machine Learning

Basic differences in AI and ML usage

Module 5: Types of Machine Learning

Various Machine Learning methods

Module 6: Data Science Project Work Flow

6-step Process of Data Science projects

Module 7: Industry application of Data Science solutions

Popular Industry applications of Data Science

COURSE 5 : Statistics for Data Science

Module 1: Introduction to Statistics

Descriptive and Inferential Statistics.

Definitions , terms, types of data

Module 2: Harnessing Data

Types of Sampling Data.

Simple random sampling, Stratified, Cluster sampling. Sampling error.

Module 3: Exploratory Analysis

Mean, Median and Mode, Data variability, Standard deviation, Z-score, Outliers

Module 4: Distributions

Normal Distribution, Central Limit Theorem, Histogram, Normalization, Normality tests, skewness, Kurtosis.

Module 5: Hypothesis & computational Techniques

Hypothesis Testing, Null Hypothesis, P-value, Type I & II errors, parametric testing: t- tests, anova test, non-parametric testing

Module 6: Correlation & Regression

COURSE 6 : Data Engineering With Pandas

Module 1: Introduction to Pandas

Pandas import

Basic structure

Module 2: Series and DataFrame data structures

Core data structure in Pandas Series and DataFrame

Module 3: Essential functions in Pandas for data mugging

Basic Pandas functions

Module 4: Various Data Treatment Techniques

Missing values

Duplicates

outliers etc.,

`

Module 5: Exploratory Data Analysis with Pandas

EDA for open dataset with Pandas

Module 6: Plotting with Pandas

Pandas plot function in detail

Module 7: Transformation data to get it ready for Machine Learning

Data treatment with Pandas introduction

COURSE 7 : Machine Learning Associate

Module 1: Machine Learning Introduction

What is ML?

ML vs AI

ML workflow

Statistical modeling of ML

Application of ML

Module 2: Machine Learning Algorithms

Popular ML algorithms

Clustering

Classification and Regression

Supervised vs Unsupervised

Choice of ML

Module 3: Supervised Learning

Simple and Multiple Linear regression

KNN, and more

Module 4: Linear Regression and Logistic Regression

Theory of Linear regression

Hands on with use cases

`

Module 5: K-Nearest Neighbour (KNN)

Theory of KNN

Hands on with use cases

Module 6: Decision Tree

Theory of Decision Tree

Hands on with use cases

Module 7: Naïve Bayes Classifier

Bayes Theorem

Hands on Naïve Bayes implementation

Module 8: Unsupervised Learning

K-means Clustering

COURSE 8 : Machine Learning Expert

Module 1: Advanced Machine Learning Concepts

uning with Hyper parameters.

Popular ML algorithms, clustering, classification and regression, supervised vs unsupervised.

Choice of ML

Module 2: Random Forest – Ensemble

Ensemble theory, random forest tuning

Module 3: Support Vector Machine (SVM)

Simple and Multiple Linear regression

KNN

Module 4: Natural Language Processing (NLP)

Text Processing with Vectorization

Sentiment analysis with TextBlob

Twitter sentiment analysis.

Module 5: Naïve Bayes Classifier

Naïve Bayes for text classification

New articles tagging

Module 6: Artificial Neural Network (ANN)

Basic ANN network for regression and classification

Module 7: Tensorflow overview and Deep Learning Intro

Tensorflow work flow demo and intro to deep learning.

COURSE 9 : Sentiment Analysis

Module 1: Introduction to Sentiment Analysis

Sentiment Polarity

Module 2: Introduction to NLTK and TextBlob packages

Hands on Sentiment Analysis with NLTK and TextBlob

Module 3: Application of Sentiment Analysis on Twitter live

Connecting to Twitter API and Live hands on sentiment analysis use case

COURSE 10 : Deep Learning Foundation

Module 1: Introduction to Deep Learning

What is deep Learning. Deep Learning models

Module 2: Deep Learning with Python frameworks

Keras

TensorFlow

Module 3: Applications of Deep Learning

Various applications of Deep Learning.

COURSE 11 : Artificial Intelligence Foundation

Module 1: Artificial Intelligence Introduction

Core concepts of Artificial Intelligence

Module 2: Domains of Artificial Intelligence

Computer Vision, NLP, ML & DL, Robotics

Module 3: Applications of Artificial Intelligence

Various industry applications of AI

Module 4: Limitations of Artificial Intelligence

Major limitations of AI Adoptions

COURSE 12 : AI Model Deployment

Module 1: AI model deployment strategies

Various model deployment strategies

Module 2: Simple API deployment

API deployment with FLASK framework

Module 3: Creating website based on API deployed

Creating HTML front-end for API

COURSE 13 : Convolution Neural Network

Module 1: Introduction to CNN

Convolution – feature maps, max pooling, ANN

Module 2: Image Processing fundamentals

Image Basics, Converting image to Numpy Array

Module 3: Convolution Filter Explanation

Various kinds of filters – edge filter

COURSE 14 : CNN Hands on Project

Module 1: Introduction to Image classification coding

Keras with TensorFlow, hands on image classification CNN

Module 2: Keras code for classifying Cats and Dogs

Python Keras coding for image classification

Module 3: Creating predicting model with TensorFlow as backend.

Complete CNN Code

COURSE 15 : Flask – API Model Deployment

Module 1: REST API

API concepts

Web servers

URL parameters

Module 2: FLASK Web framework

FLASK Web framework Installing flask

configuration

Module 3: API in Flask

API coding in Flask

Module 3: End to End Deployment

Exporting trained model, creating end to end API.