Data Science Course

Algorithms

• Searching and Sorting Algorithms
• Complexity of Algorithms
• Advance Algorithms intro

Flow Charts

• Flow charts using UML diagrams

Data Structure

• Link-list, stack, queue, heap, trees and graphs
• Python Dictionaries and lists as data structure
• Building Custom Data Structure using Python Classes, List and Dictionary
• Sets, tuples and frozen sets in Python
• Array, Matrix and Data Frames

Installation

• R Base Software
• Exploring CRAN
• Rstudio IDE of R language
• Connecting R kernel to Jupyter notebook
• Setting up Environment Variables for R

Data Structures

• Scalars
• Vectors
• Matrix
• Array
• Lists
• Data Frames
• Tables

Operators

• Operators
• Arithmetic Operators
• Comparison (Relational) Operators
• Assignment Operator
• Logical Operators

Conditional Statements

• if-else, nested if-else
• switch statement in R

Control Statements

• while loop
• for loop
• repeat loop

Functions in R

• Built-in Functions
• User Defined Functions
• Recursion

Brief History Databases

• Introduction to Database Management System
• sql and no sql databases
• pro and cons of RDBMS
• pro and cons of ODBMS

Installation of DBMS server

• Installation on Windows
• Installation on Linux
• Creating Databases
• User & Permissions

Structure Query Language

• DDL statements
• DML statements
• DCL statements
• Creating, Updating and Altering Tables
• Aggregation Functions
• Where, in , like, limit, order by, as, between clauses
• Multiple table Queries
• Nested Queries

SQL Joins

• Inner Joins
• Outer Joins
• Left Outer Join
• Right Outer Join

Normalization in Databases

• First normal form
• Second normal form
• Third normal form

Views in Database

• Create views
• Update Views
• Drop Views
• Alter Views

UDF and Triggers

• Creating and Using User Define Functions
• Creating Triggers

Advance DBMS

• File Organisation and Database Indexes
• Data Warehousing and Mining
• Database Optimization
• Database Connectivity with Python and R language
• Backup and Restore

All about Web

• Introduction to Web and Http Requests
• MIME types and Headers

Scrap Web Site

• Scraping data using request and urllib module of python
• Advance Scrapping using BeautifulSoup Library of python
• Data Scrapping using APIs and making own APIs
• Creation of Rest APIs using json

Introduction to Web Designing

• Introduction to html, css and bootstrap
• Introduction to apache server and CGI-Scripting
• Backend Development using Flask web Framework of Python
• Introduction To Django Web Framework of Python

Descriptive Statics

Data Collection Techniques

• Primary Data Collection
• Secondary Data Collection

Data Classification Techniques

• Geographical Classification
• Chronological Classification
• Qualitative Classification
• Quantities Classification

Central Tendency

• Discrete and Continues Data
• No of Classes, Class Intervals, Mid-value, Range
• Frequency Table
• Less than and Greater than Cumulative Frequency table

Measures of Central Tendency ( Mathematics of Statistics)

Mathematical Average

• Arithmetic mean
• Geometric mean
• Harmonic mean

Average of Position

• Median
• Quartile
• Decile
• Percentile
• Mode
• Variance
• Standard Deviation

Selection of Averages

Data Wrangling in R

• Stats in R
• Summary commands
• cbind, rbind, merge, subset, sort, order, group
• reshape2 package - melt, dcast
• tidyr package - gather, spread, unite, seprate
• sqldf package - Database management using R, inner join, left join, right join, select, other sql queries
• dplyr - select, filter, mutate, arrange, group_by, summarise, bind, bind_cols, bind_rows, intersect, union, setdiff, setequal, left_join, inner_join
• transform , apply, cut

Data Wrangling in Python

• numpy arrays
• pandas Module for data wrangling
• scipy Module scientific calculations of central tendency
• statistics module for stats
• sympy module for symbolic representation

Data Visualization in R

• Scatter plot
• Bar Plot
• Histograms
• Box Plot
• Stack Plot
• ggplot library for beautiful and meaning full graphs
• saving plots

Data Visualization in Python

• Scatter, Bar, Histogram and Box Plot
• matplotlib module
• seaborn module
• opencv module

Supervised Machine Learning

• Linear Regression
• Polynomial Regression
• Decision Trees
• SVM

Unsupervised Machine Learning

• Clustering
• Anomaly Detection
• Neural Networks

Introduction to Linux

• History, Installation, a Word about OpenSource
• Accessing the Command line
• Managing Files from Command Line
• Basic Commands of Linux

Users and Permissions in Linux

• Creating users and groups in linux
• Sudoers and super privileges
• File Permissions in Linux
• Changing Ownership and Permissions of files

Processes, Daemons and Logs in Linux

Networking in Linux

• Ipv4 and Ipv6, gateway, subnets
• DNS
• DHCP

Client-Server Architecture

• Remote access using ssh, putty
• Deploying ftp server
• Deploying Database Server and accessing in client machine
• Deployment of Apache Server

Scripting in Linux

• Basics of Shell Scripting
• If-else, loops and regular expression
• System Automation using Shell Scripting
• Automation using Python + Shell Scripts using CGI

Introduction to AWS services

• ec2 instance creation
• lambda service
• EMR service
• Beanstalk service

Introduction to GCP and Azure

Introduction to Big Data

• History of Big Data

Installation of Hadoop

• Single node cluster Installation
• Multi-node cluster Installation
• Working on Cloudera Distributed Hadoop (CDH)

Hadoop Distributed File System in Detail (HDFS)

Resource management using Yet Another Resource Negotiator (YARN)

Map-Reduce frame work of Hadoop

• Job Tracker
• Task Tracker
• Input, Input split
• Mapping
• Shuffling and sorting
• Combiner and Reducer

Data injection Tools

• Sqoop
• Flume
• Kafka

Data Warehousing Tools

• Hive
• Cassendra
• Hbase
• Kafka

Data Processing Engines

• MapReduce
• Hive
• Pig
• Tez & Impala

Work Flow and Job Scheduler

• OOZIE
• Zookeeper

Spark Core

Spark SQL

PySpark

Machine Learning on Spark

Spark on Data Bricks (AZURE)

Creation of reports

Navigation and accessibility

Bringing data to the user

Identifying patterns and trends

Guided project- Data analysis and visualization