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Programming for Data Science

Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems

What you'll learn

  • Explore the data science process
  • Probability and statistics in data science
  • Data exploration and visualization
  • Data ingestion, cleansing, and transformation
  • Introduction to machine learning
  • The hands-on elements of this course leverage a combination of R, Python,
  • Data PreProcessing
  • Data Imputation
  • Data Cleaning
  • Data Transformation
  • Data Visualization
  • Data Analysis
  • Data Engineering - Big Data

Of these Data Analysis forms the chunk with a coverage of all Machine Learning algorithms - Regression, Clustering, Market Basket Analysis, Classification and Network Analysis and Recommendation Systems.

A programming language like R or Python and all above data task related packages are taught in such a program.



DATA SCIENCE SYLLABUS - Top Down Methodology

Course Syllabus

Section 1: Creative code - Computational thinking. Learn how to qualify and express how algorithms work.

Section 2: Building blocks - Breaking it down and building it up
Understand how data can be represented and used as variables and learn to manipulate shape attributes and work with weights and shapes using code.

Section 3: Repetition - Creating and recognising patterns
Explain how and why using repetiton can aid in creating code and begin using repetition to manipulate and visualise data.

Section 4: Choice - Which path to follow
How to create simple and complicated choices and how to create and use decision points in code. 

Section 5: Repetition - Going further
Discussing advantages of repetition for data visualisation and applying and reflecting on the power of repetitions in code. Creating curves, shapes and scale data in code.

Section 6: Testing and Debugging
Understanding why and how to comprehensively test your code and debug code examples using line tracing techniques

Section 7: Arranging our data
Exploring how and why arrays are used to represent data and how static and dynamic arrays can be used to represent data.

Section 8: Functions - Reusable code
Understand how functions work in ProcessingJS and demonstate how to deconstruct a problem into useable functions.

Section 9: Data Science in practice
Exploring how data science is used to solve programming problems and how to solve big data problems by applying skills and knowledge learned throughout the course.

Section 10: Where next?
Understand the context of big data in programming and transform a problem description into a complete working solution using the skills and knowledge you've learned throughout the course. 
Exploring how you can expand the skills learned in this course by participating in future courses.

Explore the data science process – An Introduction

  • Understand data science thinking
  • Know the data science process
  • Use AML to create and publish a first machine learning experiment
  • Lab: Creating your first model in Azure Machine Learning Probability and statistics in data science
  • Understand and apply confidence intervals and hypothesis testing
  • Understand the meaning and application of correlation Know how to apply simulation
  • Lab: Working with probability and statistics
  • Lab: Simulation and hypothesis testing Working with data – Ingestion and preparation
  • Know the basics of data ingestion and selection
  • Understand the importance and process for data cleaning, integration and transformation
  • Lab: Data ingestion and selection - new
  • Lab: Data munging with Azure Machine Learning, R, and Python on Azure stack Data Exploration and Visualization
  • Know how to create and interpret basic plot types
  • Understand the process of exploring datasets
  • Lab: Exploring data with visualization with Azure Machine Learning, R and Python Introduction to Supervised Machine Learning
  • Understand the basic concepts of supervised learning
  • Understand the basic concepts of unsupervised learning
  • Create simple machine learning models in AML
  • Lab: Classification of people by income
  • Lab: Auto price prediction with regression
  • Lab: K-means clustering with Azure Machine Learning


Section 1: Python Basics
Take your first steps in the world of Python. Discover the different data types and create your first variable. 

Section 2: Python Lists
Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways. 

Section 3: Functions and Packages
Learn how to get the most out of other people's efforts by importing Python packages and calling functions. 

Section 4: Numpy
Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data. 

Section 5: Matplotlib
Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data. 

Section 6: Control flow and Pandas
Write conditional constructs to tweak the execution of your scripts and get to know the Pandas

DataFrame: the key data structure for Data Science in Python.

  What is Python

  Python Features

  Python History

  Python Version

  Python Applications

  Python Install

  Python Path    

  Python Example

  Execute Python

  Python Variables

  Python Keywords

  Python Identifiers

  Python Literals

  Python Operators

  Python Comments

  Control Statement

  Python If

  Python If else

  Python else if

  Python nested if

  Python for loop

  Python while loop

  Python do while

  Python break

  Python continue

  Python pass

  Python OOPs

  Python OOPs Concepts

  Python Object Class

  Python Constructors

  Python Inheritance

  Multilevel Inheritance

  Multiple Inheritance

  Python Strings

  Python Lists

  Python Tuples

  Python Dictionary

  Python Functions

  Python Files I/O

  Python Modules

  Python Exceptions

  Python Date

  Python Programs

What is Python?

Python is a high level, object oriented programming language. While Python is very powerful, it’s syntax makes Python simple to learn.

Python is very popular among data scientists, but it is not solely used for analytics. Here is a list of popular applications written in Python:

  • BitTorrent
  • Dropbox
  • Morpheus
  • Ubuntu Software Center
  • Battlefield 2

This website is dedicated to Analytics, so the Python tutorials have been shaped with that in mind. I have purposefully eliminated a lot of information that doesn’t lend itself well to data work. If you are looking for a soup to nuts Python tutorial, this is not it. However, if you are looking for a streamlined education in Python for Analytics, you have found it.

  • 8 Fun Facts About Python
  • Python 2.xx VS 3.xx


  • Probability: An Introduction
  • Bayes’ Theorem
  • Probability vs Odds
  • Python: Histograms and Frequency Distribution
  • Benford’s Law: Fraud Detection by the Numbers
  • The Monty Hall Problem


  • R: Intro to Statistics – Central Tendency
  • Statistics: Range, Variance, and Standard Deviation
  • Python: Co-variance and Correlation
  • Python: Central Limit Theorem
  • Python: Hypothesis Testing(T-Test)
  • Factor Analysis: Picking the Right Variables
  • Simpson’s Paradox: How to Lie with Statistics



  • Python: Install Python
  • Python Fundamentals
  • Python: Print Variables and User Input
  • Python: Printing with .format()
  • Python: Lists and Dictionaries
  • Python: Tuples and Sets
  • Python Loops
  • Python Conditional Logic
  • Python Functions
  • Python: Working with Lists
  • Python: Enumerate() and Sort
  • Python: Error handling


  • Python lambda, map(), reduce(), filter()
  • Python zip and unpack
  • Python list comprehensions
  • Python: Intro to Graphs
  • Python: Line Graph
  • Python: Generators
  • Python: Regular Expressions
  • Python: **Kwargs and *Args
  • Python: Create, Import, and Use a Module
  • Python: Working with CSV Files

Object-Oriented Programming

  • Python: Object-Oriented Programming

Numpy and Pandas

  • Python: Numpy
  • Python: Numpy Part II
  • Python: Pandas Intro (Series)
  • Python: Pandas Intro (Dataframes)
  • Python: Pandas, Working with DataFrames
  • Python: An Interesting Problem with Pandas
  • Python: Pivot Tables with Pandas
  • Python: Read CSV and Excel with Pandas
  • Python: Accessing a SQL database


  • Python: Fun with Central Tendency
  • Python: Histograms and Frequency Distribution
  • Python: Central Limit Theorem
  • Python: Co-variance and Correlation
  • Python: Hypothesis Testing(T Test)
  • Python: Create a Box whisker plot


  • Python: Linear Regression
  • Python: Logistic Regression

Supervised Machine Learning

  • Python: K Nearest Neighbor
  • Python: Naive Bayes’

Unsupervised Machine Learning

  • Python: K Means Cluster
  • Python: K Means Clustering Part 2


SQL (Structured Query Language) is one of the most common languages found in the data profession. Don’t let the NoSQL chatter scare you away from learning SQL, as the appearance of applications such as SQL on Hadoop are testimony that no matter what the underlying data structure, SQL is still going to have a strong presence in the data world

Automating Data Preparation with SQL Server

Getting Full Days Worth of Data

MS SQL Server: Database Diagrams

Simple Methods to Recover MDF File Password

Intro to SQL

  • Install MS SQL Server
  • SQL Select Command
  • SQL Select Functions (Top, Count, Distinct, Min and Max)
  • SQL: Where Clause
  • SQL: Aggregates, Group By, and Having
  • SQL: Case Statement
  • SQL: Intro to Joins
  • SQL: 4 Types of Joins
  • SQL: Stored Procedures
  • SQL: Create a View
  • SQL: Working with Date/Time Functions
  • SQL: Learn to use Cursors – List table names
  • SQL: Reindex a Database is the most popular website dedicated to online puzzle programs and tutorials.

R- Programming


  • R: An Introduction
  • R: Data Types Tutorial
  • R: Intro to Statistics – Central Tendency
  • R: Create Functions in R
  • R: Loops – For, While, Repeat
  • R: seq() and rep()
  • R: Vector operations
  • R: Building Matrices
  • R: Matrix Operations
  • R: Working with lists
  • R: Converting Factors to Numbers
  • R: Working with Data frames
  • R: Filter Data frames
  • R: gsub
  • R: Graphing with matplot()
  • R: Installing Packages
  • R: Intro to qplot()
  • R: Intro to ggplot()
  • R: ggplot – Histograms
  • R: ggplot using facets
  • R: Boxplot – comparing data
  • R: ANOVA (Analysis of Variance)
  • R: laply and ldply from plyr package


  • R: Simple Linear Regression

Machine Learning

  • R: Decision Trees (Classification)
  • R: Decision Trees (Regression)
  • R: K-Means Clustering
  • R: K-Means Clustering- Deciding how many clusters

Text Analytics

  • R: Connect to Twitter with R
  • R: Twitter Sentiment Analysis
  • R: Creating a Word Cloud
  • R: Text Mining (Term Document Matrix)
  • R: Text Mining (Pre-processing)


2 Months
Regular batches: Monday – Friday – 2 hrs per day
Weekend Batches: Saturday / Sunday – 5 hrs per day

Job Opportunities

Data science Jobs in Coimbatore

Jobs from Indeed



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Project Done By Students

S.NoStudent NameTechnology UsedProject Title
1. Sidharth PHP
2. Praveena PHP
3. Mohan Balaji PHP
4. Preethi PHP
5. Vimalraj PHP
6. Suresh PHP
7. Anitha PHP
8. Gajendran PHP
9. Santhosh Kumar PHP
10. Mentor-Boopathikumar Android
11. Nisha JAVA
12. Prabhadevi Rajan PHP
14. Nilafur NIsha.M PHP
15. Saranya PHP
16. Lotha PHP
17. B.Sathya Rubavathi PHP
18. Lotha PHP
19. KarthiKeyan Selvam PHP
20. S.Priyanka PHP
21. Divya PHP
22. Mohanraj PHP
23. Lakshminarayanan PHP
24. Swetha PHP
25. Siva Sakthi PHP
26. Siva Sakthi PHP
27. Radha kannan PHP
28. Radha kannan PHP
29. Kameshwaran PHP
30. Uma maheswari PHP