Data Science using Python Training in Marathahalli

Python is an highly interactive and open source object oriented language introduced by Python Software Foundation PSF. It can be easily embed with data science and machine learning applications using statistical and machine learning algorithms.  Importing data, data manipulation (aka cleansing), data modeling, data reporting using visualization are the major steps of data science. The process of data mining, statistics and mathematical computing can be done easily using Python scripts. It has become famous nowadays due to its user friendly features,  standard libraries, data structures and scripting capability.

Data Science Using Python Job Opportunities

  • Python Data Scientist jobs are especially suitable for the people, who have skills in deep learning, statistics and data analysis.
  • Having strong analytics and statistics skills can be a feather on your cap and get you Data scientist job.
  • Having advanced analytics and SQL Server as co-skills can get you a job as a Solution Architect.
  • Having robotics, Linux, Analytics and image processing as co-skills can get job as Imaging Scientist.
  • Having Java, NLP, algorithms as co-skills can get job as Data Science Engineer.
  • JPMorgan, Amazon, IBM, Deloitte, Mphasis, Intel, Accenture, Capgemini, KPMG, Philips, NTT Global, Cyient, E&Y are some of the companies that hire for Python Data Scientists.
    Expand your Python Data Scientist job opportunities and maximize the chances by acquiring the best support and training from TIB Academy.
Best Data Science with Python training in Marathahalli

I was looking for a Data Science with Python training in Marathahalli and Training Marathahalli was suggested by some of my friends. The Data Science with Python Trainer was very good. His knowledge on Data Science was good. And most importantly he was able to deliver the knowledge among us. I recommend this place with 5 star ratings!

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Prerequisites for Python with Data Science

  • No prerequisites to learn Python with Data Science.
  • If you are already familiar with python programming and basic statistics, this course will be easier for you to learn. Otherwise, our experienced professionals are here to teach you and coach you from the Python and data science fundamentals.

Our Python with Data Science Training and Support

TIB Academy is one of the best Python with Data Science training institutes in Marathahalli. Our trainers are highly experienced professionals. Currently, they are all working in top rated MNCs and Corporates, carrying years of real time industrial experience in their particular technologies. In this Python with Data Science training in Marathahalli, you will be experiencing a unique learning  environment. Our Python with Data Science syllabus includes 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. For the detailed Python with Data Science course syllabus, please check below.
Usually, our Python with Data Science training sessions are scheduled during weekday mornings (7AM – 10AM), weekday evenings (7PM – 9:30PM) and weekends (flexible timings). We do provide Python with Data Science classroom course and Python with Data Science online course, both on weekdays and weekends based upon the student’s preferred time slots.
You will surely enhance your technical skills and confidence via this Python with Data Science training. Our connections and networks in the job market will help you to achieve your dream job easily. Compared to other training institutes, we are offering the best Python with Data Science training course in Marathahalli, Bangalore, where you can get the best Python with Data Science training and placement guidance for reasonable and affordable cost.

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Data science with python Training in Marathahalli Syllabus

1.  Whetting Your Appetite

2. Using the python Interpreter

  • Invoking the Interpreter
    • Argument Passing
    • Intractive Mode
  •  The Interpreter and its Environment
    • 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
  •  first step towards programming

4. More control flow tools

  •  if Statement
  •  for Statement
  •  The range() Function
  •  break and continue statements and else clauses on loops
  •  pass statements
  •  defining statements
  •  more defining statements
    •  Default Argument Values
    •  Keyword Argument
    •  Arbitary Argument Lists
    •  Unpacking Argument Lists
    •  Lambda Expressions
    •  Documentation Settings
  •  Intermezzo : Coding Style

5. Data Structures

  •  More on Lists
    • Using Lists as Stacks
    • Using USN as Queues
    • Functional Programming Tools
    • list Comprehensions
    • Nested List Comprehensions
  • The del statement
  • Tupfes and Sequences
  • Sets
  • Dictionaries
  • Looping Techniques
  • More on Conditions
  • Comparing Sequences and Other Types

6. Modules

  • More on Modules
    • Executing modules as scripts
    • The Module Search Path
    •  ‘Compiled’ Python files
  • Standard Modules
  • The dir() Function
  • Packages
    • Importing • From a Package
    • Intra-package References
    • Packages in Multiple Directories

7. Input and Output

  • Fancier Output Formatting
    • Old string formatting
  • Reading and Writing Files
    • Methods of File Objects
    • Saving structured data with json

8. Error and Exceptions

  • Syntax Errors
  • Exceptions
  • Handling Exceptions
  • Raising Exceptions
  • User-defined Exceptions
  • Defining Clean-up Actions
  • Predefined Clean-up Actions

9. Classes

  • A Word About Names and Objects
  • Python Scopes and Namespaces
  • A First Look at Classes
    • Class Definition Syntax
    • Class Objects
    • Instance Objects
    • Method Objects
    • Class and Instance Variables
  • Random Remarks
  • Inheritance
    • Multiple Inheritance
  • Private Variables and Class-local References
  • Odds and Ends
  • Exceptions Are Classes Too
  • Iterators
  • Generators
  • 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

Data Science Using Python Interview Questions

  1. Name a few libraries in Python used for Data Analysis and Scientific computations.
  2.  What is the main difference between a Pandas series and a single-column DataFrame in Python?
  3. Write code to sort a DataFrame in Python in descending order
  4. Which method in is used to create scatter plot matrix?
  5. Which is the standard data missing marker used in Pandas?
  6. How are NumPy and SciPy related?
  7. Which plot will you use to access the uncertainty of a statistic?
  8. What is pylab?
  9. What is the main difference between a Pandas series and a single-column DataFrame in Python?
  10. Can we create a DataFrame with multiple data types in Python? If yes, how can you do it?
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