Data Science with Python Training in Bangalore

data science with python training in bangalore
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Python is an open source and highly interactive object oriented language that can be effectively integrated with data science and machine learning. The process of examining the data, data analysis and statistical computing can be done using Python. Importing data, data manipulation (aka cleansing), data modeling, data reporting using visualisation can be done efficiently using Python.

I am a M.Sc graduate completed Applied Statistics in 2015. I have been working in a startup as “Python programmer” for last 2 years. I was very much interested in data science. So I joined here to learn statistical and analytical algorithms using Python. global training bangalore approved reviews My trainer taught me from basic data science algorithms and statistics. He taught very slowly and very clearly. His step by step approach was very helpful for my understanding. At the end of my course, I also did a project under his guidance. With his training and support, I cleared aptitude and technical rounds confidently. Now, I’m preparing for the final round of HR interview, and I’m sure of getting this job.

Tell me more about Data Science with Python? IT Training courses in bangalore

  • Python has become more popular nowadays because of its user friendly features, data structures, standard libraries and scripting capability.
  • Global Training Bangalore is the best Data Science with Python training center in Bangalore where you will be exposed to differentiated learning environment as the course syllabus has been prepared by the highly experienced professionals. With this course, you can learn about classes, functions, OOPs, file operations, memory management, garbage collections, standard library modules, generators, iterators, fourier transforms, discrete cosine transforms, signal processing, linear algebra, spatial data structures and algorithms, multi-dimensional image processing and lot more. Please check below for the detailed syllabus.

Are there any prerequisites for Data Science with Python? IT Training Prerequisites in bangalore

  • Strong knowledge on Python.
  • If you are already familiar with the above, this course will be quite easy for you to grasp the concepts. Otherwise, experts are here to help you with the concepts of Python and Data Science from the basics.

Can you help me about Data Science with Python Job Opportunities? global training bangalore placement

  • Python Data Scientist jobs are suitable for experienced people, who have key skills on deep learning, statistics and data analysis. In the current IT market, there are plenty of data scientist opportunities for the experienced professionals who are aware of the above technologies.
  • If you possess analytics and statistics skills, you can get job as Data scientist with this course.
  • If you possess advanced analytics and SQL Server as co-skills, you can get job as Solution Architect.
  • If you possess robotics, Linux, Analytics and image processing as co-skills, you can get job as Imaging Scientist.
  • If you possess Java, NLP, algorithms as co-skills, you can get job as Data Science Engineer.
  • Some of the companies that hire for data science are JP Morgan, Amazon, IBM, Deloitte, Mphasis, Intel, Accenture, Capgemini, KPMG, Philips, NTT Global, Cyient, E&Y.

Compared to other training institutes, Global Training Bangalore (TIB Academy) is one of the best Data Science with Python training institutes in Bangalore where you can acquire the best Data Science with Python training and placement guidance.

What is special about the Data Science with Python training in Global Training Bangalore? tib-academy-bangalore

  • Global Training Bangalore (TIB Academy) is the only institute providing the best Data Science with Python training in Bangalore. They have knowledgeable and experienced industrial professionals as the trainers who are working in fortune 500 MNCs with years of real time experience. So they can give relevant coaching for  you to become the best data scientist.
  • Since the trainers are all currently working during the day, the Data Science with Python training program will be usually scheduled during weekdays early mornings between 7AM to 10AM, weekdays late evenings between 7PM to 9:30PM and flexible timings in weekends. They provide Data Science with Python classroom training, Data Science with Python online training and Data Science with Python weekend training based upon the student’s time convenience. This training will expose you to the best Data Science with Python course and placement support in Bangalore with moderate course fees.
  • The practical sessions throughout the course will help you to enhance your technical skills and confidence. Their links to the corporate job market will surely help you to get closer to your dream job. So start putting your sincere efforts into practice and grab the wonderful opportunities.

What are the Data Science with Python Class timings and course duration?  IT training in bangalore timing

Day Data Science with Python Classroom Training Timing Data Science with Python Online Training Timing
Mon – Fri

7AM to 10AM

7PM to 9.30PM

7AM to 10AM

7PM to 9.30PM

Sat,

Sun

Flexible Timing

Flexible Timing

Please contact us soon to book your preferable time slot.

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Best Data science with Python Training in Bangalore

Best Institute for Data Science with python course. Global Training Bangalore have provided me real time training with placement support. Learned many new things and came to know interesting case studies.

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Data science with python Course Duration

Regular Classes (Morning, Day time & Evening)

Duration: 30 – 35 hrs.

Weekend Training Classes (Saturday, Sunday & Holidays)

Duration: 5 weeks

Fast Track Training Program (5+ hours daily)

Duration: within 20 days.

Call Global Training Bangalore (TIB Academy)

+91 9513332301 / 02 / 03

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Data science with python Training in Bangalore Syllabus

1.  Whetting Your Appetite

2. Using the python Interpreter

  • 2.1 Invoking the Interpreter
    • 2.1.1 Argument Passing
    • 2.1.2 Intractive Mode
  • 2.2 The Interpreter and its Environment
    • 2.2.1 Source Code Encoding

3. An informal Introduction to Python

  • 3.1 Using Python as a calculator
    • 3.1.1 Numbers
    • 3.1.2 Strings
    • 3.1.3 Unicode Strings
    • 3.1.4 Lists
  • 3.2 first step towards programming

4. More control flow tools

  • 4.1 if Statement
  • 4.2 for Statement
  • 4.3 The range() Function
  • 4.4 break and continue statements and else clauses on loops
  • 4.5 pass statements
  • 4.6 defining statements
  • 4.7 more defining statements
    • 4.7.1 Default Argument Values
    • 4.7.2 Keyword Argument
    • 4.7.3 Arbitary Argument Lists
    • 4.7.4 Unpacking Argument Lists
    • 4.7.5 Lambda Expressions
    • 4.7.6 Documentation Settings
  • 4.8 Intermezzo : Coding Style

5. Data Structures

  • 5.1 More on Lists
    • 5.1.1 Using Lists as Stacks
    • 5.1.2 Using USN as Queues
    • 5.1.3. Functional Programming Tools
    • 5.1.4 list Comprehensions
    • 5.1.5Nested List Comprehensions
  • 5.2 The del statement
  • 5.3 Tupfes and Sequences
  • 5.4 Sets
  • 5.5 Dictionaries
  • 5.6 Looping Techniques
  • 5.7 More on Conditions
  • 5.8 Comparing Sequences and Other Types

6. Modules

  • 6.1 More on Modules
    • 6.1.1 Executing modules as scripts
    • 6.1.2 The Module Search Path
    • 6.1.3 ‘Compiled’ Python files
  • 6.2 Standard Modules
  • 6.3 The dir() Function
  • 6.4 Packages
    • 6.4.1 Importing • From a Package
    • 6.4.2 Intra-package References
    • 6.4.3 Packages in Multiple Directories

7. Input and Output

  • 7.1 Fancier Output Formatting
    • 7.1 A . Old string formatting
  • 7.2. Reading and Writing Files
    • 7.2.1. Methods of File Objects
    • 7.2.2. Saving structured data with json

8. Error and Exceptions

  • 8.1 Syntax Errors
  • 8.2 Exceptions
  • 8.3 Handling Exceptions
  • 8.4 Raising Exceptions
  • 8.5 User-defined Exceptions
  • 8.6 Defining Clean-up Actions
  • 8.7 Predefined Clean-up Actions

9. Classes

  • 9.1. A Word About Names and Objects
  • 9.2. Python Scopes and Namespaces
  • 9.3. A First Look at Classes
    • 9.3.1. Class Definition Syntax
    • 9.3.2. Class Objects
    • 9.3.3. Instance Objects
    • 9.3.4. Method Objects
    • 9.3.5. Class and Instance Variables
  • 9.4. Random Remarks
  • 9.5. Inheritance
    • 9.5.1. Multiple Inheritance
  • 9.6. Private Variables and Class-local References
  • 9.7. Odds and Ends
  • 9.8. Exceptions Are Classes Too
  • 9.9. Iterators
  • 9.10. Generators
  • 9.11. Generator Expressions

10. Data Science

  • Numpy
  • 2D Numpy Array
  • Basic Statistics with Numpy
  • Basic plot with matplotlib
  • Histograms
  • Customization
  • Boolean logic and control Flow
  • Pandas

Additional Topics :

  • We will work on 100 + programs in this session
  • Other than the above mentioned topic we will cover GUI Development

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