Python + Data Science Combo Course

This course “Python for Data Science”, combo course is designed for candidates with or without programming skills, with basics of Data importing, Data mugging and coding Machine Learning algorithms along with effective programming techniques. This also includes Python Data Science challenges kit, enabling the candidates to not only understand Python core concepts but also gain practical mastery over Python for Data Science, which is very much in demand in Today’s Data Science job opportunities.


Can’t find a batch you were looking for?

This course "Python for Data Science", combo course is designed for candidates with or without programming skills, with basics of Data importing, Data mugging and coding Machine Learning algorithms along with effective programming techniques. This also includes Python Data Science challenges kit, enabling the candidates to not only understand Python core concepts but also gain practical mastery over Python for Data Science, which is very much in demand in Today's Data Science job opportunities.
Python is the most popular programming language for Data Science as on Today. Python is powerful , easy to learn and flexible tool for coding Data Science and Machine Learning algorithms. In recent years, Python has evolved immensely with respect to Data Science sphere, with a huge community around Python creating quite a few power data science and analytics packages such as Pandas, Numpy, Scikit Learn, Scipy and more. As a result, analyzing data, modeling machine learning algorithms with Python has never been easier.

Course content




Core Python



Getting Started
  • History
  • A Python Q&A Session
  • How Python Runs Programs
  • How You Run Programs
Introduction to Python:
  • What is Python?
  • Why Python?
  • Python Applications in real life
  • Brief history of Python
  • Versions of Python
  • Installing Python
  • Using IDLE
  • First Python Program
  • Getting help from Python Docs
Basic Syntax
  • Basic syntax
  • Commenting
  • Indentation
  • Python keywords
  • Strings
  • String values
  • String Operations
  • String slicing
  • Built in string methods
  • Formatted printing
  • Simple Input and Output handling
  • Variables
  • Data type
  • Number
  • string
  • List
  • Tuple
  • Dictionary
Types and Operations
  • Introducing Python Object Types
  • Numeric Types
  • The Dynamic Typing Interlude
  • Strings
  • Lists and Dictionaries
  • Tuples, Files and Everything Else
Variables Data types
  • Intro to dynamic typing
  • Variables in Python
  • Naming conventions
  • Basic Data types (representation of strings, integer, floats)
Decision Making – Loops 
  • While loop, if loop and nested loop
  • Number type conversion – int (), long (). Float ()
  • Mathematical functions, Random function, Trigonometric function
Language Building blocks
  • Control statements, the if, elif, else
  • True and False
  • Arithmetic Operators
  • Relational Operators
  • Logical Operators
  • Bitwise Operators
  • While loop
  • Usage of pass, break and continue
  • For each loop
  • Strings- Escape char, String special Operator, String formatting Operator
  • Build in string methods – center (), count () decode (), encode ()
  • Python List – Accessing values in list, delete list elements, Indexing slicing & Matrices
  • Built in Function – cmp(), len(), min(), max()
  • Dictionary – Accessing values from dictionary, Deleting and updating elements in Dict.
  • Properties of Dist., Built in Dist functions & Methods.
  • Date & time -Time Tuple, calendar module and time module
  • Lists
  • Tuples
  • Sets
  • Dictionaries
  • Sorting collections
  • Operations on collections
  • Discussion on real life application of above collections
  • Introduction to functions
  • Built in functions
  • User defined functions
  • Function parameters
  • Variable arguments, args and kwargs
  • Positional and named arguments
  • Discussion scope of variables with respect to functions and namespace
  • Passing function to another function
  • Files in Python- Reading keyboard input, input function
  • Opening and closing files. Syntax and list of modes
  • Files object attribute- open, close . Reading and writing files, file Position.
  • Renaming and deleting files
  • mkdir methid, chdir () method , getcwd method , rm dir
File Handling
Exception Handling
  • Exception handling – List of exceptions – Try and exception
  • Try- finally clause and user defined exceptions
  • Introduction to modules
  • Introduction to standard modules
  • OS module
  • path module
  • Sys module
  • sub process module
  • Argument parsing using argparse module
  • .csv file parsing using csv module
  • .jason file paring using Jason module
  • Xml file parsing using xml module
  • Introduction to logging module
Object Oriented Programming
  • Introduction to Classes and Objects
  • Principles of OOP
  • Instance methods
  • Special methods
  • Encapsulation
  • Inheritance
  • Polymorphism
Regular Expressions
  • Introduction to regular exceptions
  • Introduction to re module
  • Simple character matches
  • Match function
  • Searching function
  • Regular expression patterns
  • Patterns in Regex
  • Search And Replace
GUI Programming
  • Introduction
  • Tkinter programming
  • Tkinter widgets
  • Data base connectivity
  • Methods- MySQL, Oracle, how to install MYSQL, DB connection
  • Create, insert, update and delete operation, Handling erros
  • Into Multi-Threading
  • Threading module
  • creating thread
  • Synchronizing threads
  • Multithreaded Priority Queue
Optional I (For testers)
  • Introduction to testing using Python
  • Introduction to test automation
  • Introduction to Selenium web deriver
  • Web testing using selenium
Option II (   Developers)
  • Generators
  • Decorators
  • Iterators and iterator protocol
  • Debugging using PDB
Options III (Web programming)
  • Introduction to web programming using Python
  • Introduction to Django/Flask
  • Introduction to Restful API’s using Python
Option IV (Data Science)
  • Introduction to data science using python
  • Introduction to panda’s module
  • Introduction to data visualization using matplotlib
  • Introduction to NumPy
  • Introduction to SciPy



Advanced Python



Functional Programming
  • Lambdas
  • List Comprehensions
  • Set and Dictionary Comprehensions
  • Closures and Decorators
  • Generators and Coroutines
  • Generator Expressions
  • Declarative Programming
Systems Programming
  • File Descriptors
  • Reading and Writing Files
  • Files and Directories
  • File Locking
  • Memory Mapped I/O
  • Creating Processes
  • Process Management
  • Pipes and Signals
Classes and Objects
  • New Style Classes
  • Inheritance and Mixins
  • Properties and Slots
  • Static and Class Methods
  • Abstract Base Classes
  • Method Overriding
  • Attributes and Functors
  • Decorators and Factories
  • Descriptors and MetaClasses
Persistence and Databases
  • Shelve and Pickle
  • SQL Relational Databases
  • Connection, Cursor, Row Objects
  • Create, Read, Update, Delete
  • Error Handing
  • Query Results and Metadata
  • Create and Aggregate Functions
  • Exporting and Importing
  • Transactions and Rollbacks
  • Database Objects
Network Programming
  • Sockets and Addresses
  • Establishing Connections
  • TCP Clients and Servers
  • UDP Clients and Servers
  • UDS Clients and Servers
  • Network Objects
  • SocketServers
  • Secure Sockets Layer
Web Programming
  • JSON and XML
  • Using XML-RPC
  • Rest Interfaces
  • WSGI and HTML
  • Flask Framework
  • Controller Functions
  • Templates and Forms
  • Database ORMs
 Threads and Concurrency
  • Creating and Joining Threads
  • Daemon Threads
  • Thread Objects
  • Timer Threads
  • Locks and Semaphores
  • Events and Conditions
  • Thread Locals
  • Thread Queues
  • Process Queues and Tasks
  • Process Pools
  • The DRY Principle Revisited
  • Single Inheritance
  • Sub-Classing Classes from Python Packages
  • Overriding Methods
  • Calling the Parent Method with super()
  • Multiple Inheritance
  • Method Resolution Order
Extending and Embedding Python
  • Calling C/C++ from Python
  • Using ctypes
  • Extension Modules in C/C++
  • Raising Python Exceptions
  • Calling Python from C/C++
  • Embedding Python Interpreter
  • Importing Python Modules from C/C++
  • Converting Python Objects to C/C++
  • Invoking Python Functions from C/C++
Data analysis using Numpy
  • Introduction to Numpy arrays
  • Creating and applying functions
  • Numpy Indexing and selection Numpy Operations
  • Exercise and assignment challenge
Pandas and advanced analysis
  • Panda’s series
  • Introduction to Data Frames
  • Missing data
  • Group by
  • Merging, joining and concatenating
  • Operations
  • Data Input and Output
Data visualization with Python
  • Plotting using Mat plot lib
  • Sea born visualization
  • Pandas built-in data visualization
Seaborn visualization
  • Categorial Plot using Seaborn
  • Distributional plots using Seaborn
  • Matrix plots
  • Grids
  • Seaborn exercises






 Introduction to Django
  • Introduction
  • Why Django?
  • Batteries Included
  • Django Principles
  • What you Should Already Know
  • Course Overview
Installing Django
  • Intro
  • Choosing your Versions
  • Installing Pip and Python on Windows
  • Demo: Windows Installation
  • Installing Pip and Python on Mac OS X
  • Demo: OS X Installation
  • Installing Pip and Python on Linux
  • Demo: Linux Installation
  • Virtualenv
  • Demo: Virtualenv
  • Installing Django
  • Summary
Starting a Django Project
  • Introduction
  • Creating a Django Project
  • Demo: Creating a Django Project
  • The Model-Template-View Pattern
  • Demo: Hello, World!
  • Mapping URLs
  • Demo: URL Mapping
  • Django Views
  • Demo: Templates
  • Summary
  • Introduction
  • Demo: Adding Models
  • Django Model Classes
  • py Database Commands
  • Demo: The Admin Interface
  • The Django Admin Interface
  • Demo: The Model API
  • Save and Delete
  • The Model API
  • Database Relations
  • Summary
Adding a User Home Page
  • Introduction
  • Demo: Adding Login and Logout Views
  • More about URL Mappings
  • Demo: A Template for the Home Page
  • Authorization with Django
  • More about Django Templates
  • Demo: Adding the Home View
  • URL Mappings for Apps
  • Demo: Template Inheritance
  • Template Inheritance
  • Demo: Login Required
  • Demo: Showing Game Data on the Home Page
  • Demo: A Custom Manager Class
  • The Template Context
  • Templates: For and Include Tags
  • Summary
  • Introduction
  • Demo: Adding a HTML Form
  • Using Django Forms
  • Demo: Adding Stypng to the Form with Crispy-Forms
  • Demo: Field Options
  • Field Options
  • Demo: Showing Invitations in a List
  • Demo: Accepting Invitations
  • Demo: Named Groups
  • Named Groups in URLs
  • Summary
Odds and Ends
  • Introduction
  • Class-based Views
  • Demo: Class-based Views
  • Demo: Adding User Signup
  • Generic Views
  • Debugging Django
  • Demo: The Django Debug Toolbar
  • Resources
  • Summary



Data Science with Python



Introduction to Data Science
  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & it’s types
Python Programming Fundamentals
  • Programming Basics
  • Python Data Types
  • Structures and conditional statements
  • Python core packages
  • Introduction to Jupyter Notebook
Data Science Essentials
  • Data Science Introduction
  • Data Science work flow
  • Machine Learning Overview
Data Mugging with Numpy and Pandas
  • Introduction to Numpy and Pandas
  • Data filtering and selecting
  • Find duplicates and treating missing values
  • Concatenate and transform data
Basic Statistics
Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
Probability Basics
  • What does it mean by probability?
  • Types of Probability
  • ODDS Ratio?
 Standard Deviation
  • Data deviation & distribution
  • Variance
 Bias variance Tradeoff
  • Underfitting
  • Overfitting
 Distance metrics
  • Euclidean Distance
  • Manhattan Distance
 Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • catter plot
  • Cook’s Distance
Missing Value treatments
  • What is an NA?
  • Central Imputation
  • KNN imputation
  • Dummification
  • Pearson correlation
  • Positive & Negative correlation
Error Metrics Duration
  • Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
  • MSE
  • RMSE
  • MAPE
Visualization, web scraping
  • Creating basic charts
  • Statistical Charts
  • Web Scrapping tools
Introduction to Machine Learning
  • Overview of Supervised and Unsupervised Machine Learning
  • Linear Regression
  • Clustering with K-means
  • Naive Bayes Classification
  • Introduction to Neural Networks
Supervised Learning  
  • Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value
  • Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff
Unsupervised Learning
  • K-Means
  • K-Means ++
  • Hierarchical Clustering
Other Machine Learning algorithms
  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest


To see the full course content Download now

Course Prerequisites

  • Basic Programming is recommended
  • Basic Statistics knowledge is recommended

Who can attend

  • Candidates wanted to pursue Data Science career, with basic or no programming skills
  • Seasoned conventional programmer aiming to gain basic machine learning coding skills
  • Job seekers, pursuing a career as Data Science Developer
  • Professionals, whose job involves Data Science and Python.

Number of Hours: 70hrs



Key features

  • One to One Training
  • Online Training
  • Fastrack & Normal Track
  • Resume Modification
  • Mock Interviews
  • Video Tutorials
  • Materials
  • Real Time Projects
  • Virtual Live Experience
  • Preparing for Certification


DASVM Technologies offers 300+ IT training courses with 10+ years of Experienced Expert level Trainers.

  • One to One Training
  • Online Training
  • Fastrack & Normal Track
  • Resume Modification
  • Mock Interviews
  • Video Tutorials
  • Materials
  • Real Time Projects
  • Materials
  • Preparing for Certification

Call now: +91-99003 49889 and know the exciting offers available for you!

We working and coordinating with the companies exclusively to get placed. We have a placement cell focussing on training and placements in Bangalore. Our placement cell help more than 600+ students per year.

Learn from experts active in their field, not out-of-touch trainers. Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule. We have a pool of experts and trainers are composed with highly skilled and experienced in supporting you in specific tasks and provide professional support. 24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts. Our trainers has contributed in the growth of our clients as well as professionals.

All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process which includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating continue to train for us.

No worries. DASVM technologies assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.

DASVM Technologies provides many suitable modes of training to the students like:

  • Classroom training
  • One to One training
  • Fast track training
  • Live Instructor LED Online training
  • Customized training

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

You will receive DASVM Technologies recognized course completion certification & we will help you to crack global certification with our training.

Yes, DASVM Technologies provides corporate trainings with Course Customization, Learning Analytics, Cloud Labs, Certifications, Real time Projects with 24x7 Support.

Yes, DASVM Technologies provides group discounts for its training programs. Depending on the group size, we offer discounts as per the terms and conditions.

We accept all major kinds of payment options. Cash, Card (Master, Visa, and Maestro, etc), Wallets, Net Banking, Cheques and etc.

DASVM Technologies has a no refund policy. Fees once paid will not be refunded. If the candidate is not able to attend a training batch, he/she is to reschedule for a future batch. Due Date for Balance should be cleared as per date given. If in case trainer got cancelled or unavailable to provide training DASVM will arrange training sessions with other backup trainer.

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Please Contact our course advisor +91-99003 49889. Or you can share your queries through

like our courses