Python3.6 for Data Science Training
Enroll the course

Course Details

Course Outline

Key Features


About Python3.6 for Data Science Certification Training Course

Python 3.6 for Data Science Training Course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

What You Will Get From This Course?
  • Basic process of data science
  • Python and Jupyter notebooks
  • An applied understanding of how to manipulate and analyze uncurated datasets
  • Basic statistical analysis and machine learning methods
  • How to effectively visualize results

Who should take this Python3.6 for Data Science Online Training Course?
  1. Big DataHadoop Developers eager to learn other verticals like Testing, Analytics, Administration
  2. Mainframe Professionals, Architects & Testing Professionals
  3. Business Intelligence, Data warehousing and Analytics Professionals
  4. Graduates, undergraduates eager to learn Big Data can take this Python3.6 for Data Science Certification online training

What are the prerequisites for taking HadoopCertification Training?

Python training is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data.

  1. Introduction to Python
      • What is Python?
      • Why Python?
      • Who Uses Python?
      • Characteristics of Python
      • History of Python
      • What is PSF?
      • Python Versions
      • How to Download and Install Python
      • Install Python with Diff IDEs
      • Features and Limitations of Python
      • Python Applications
      • Creating Your First Python Program
  2. Different Modes in PYTHON
      • Interactive and Script Mode
      • Python File Extensions
      • SETTING PATH IN Windows
      • Clear screen inside python
      • Python Comments
      • Quit the Python Shell
      • Shell as a Simple Calculator
      • Multiline Statements
      • Quotations in Python
  3. Basic Python Syntax
    • Structuring with indentation in Python
    • Python Keywords, Identifiers and Literals
    • What is Variable?
    • Variables and Constants in Python
    • Variable names and Value
    • Mnemonic Variable Names
    • Values and Types
    • What Does “Type” Mean?
    • Multiple Assignment
    • Python different numerical types
    • Standard Data Types
    • Operators and Operands
    • Order of Operations
    • Swap variables
    • Python Mathematics
    • Type Conversion
  4. Python Operators and Operands
    • Arithmetic, Relational Operators and Comparison Operators
    • Python Assignment Operators
    • Short hand Assignment Operators
    • Logical Operators or Bitwise Operators
    • Membership Operators
    • Identity Operators
    • Operator precedence
    • Evaluating Expressions
  5. String Handling
    • What is string?
    • String operations and indices
    • Basic String Operations
    • String Functions, Methods
    • String Multiplication and concatenation
    • String Formatting Operator
    • Built-in String Methods
  6. Python Conditional Statements
    • How to use “if condition” in conditional structures
    • if statement (One-Way Decisions)
    • if .. else statement (Two-way Decisions)
    • How to use “else condition”
    • if ..elif .. else statement (Multi-way)
    • When “else condition” does not work
    • How to use “elif” condition
    • How to execute conditional statement with minimal code
    • Nested IF Statement
  7. Python LOOPS
    • How to use “While Loop” and “For Loop”
    • Break statements
    • Continue statement
    • Enumerate function for For Loop
    • Practical Example
  8. Python Data Structures: Python Lists
    • Lists are mutable
    • Getting to Lists
    • List indices
    • Traversing a list
    • List operations, slices and methods
    • Map, filter and reduce
    • Deleting elements
    • Lists and strings
  9. Python TUPLE
    • Advantages of Tuple over List
    • Packing and Unpacking
    • Comparing tuples
    • Creating nested tuple
    • Using tuples as keys in dictionaries
    • Deleting Tuples
    • Slicing of Tuple
    • Tuple Membership Test
    • Built-in functions with Tuple
  10. Python Dictionary
    • How to create a dictionary?
    • Python Dictionary Methods
    • Copying dictionary
    • Updating Dictionary
    • Delete Keys from the dictionary
    • Dictionary items() Method
    • Sorting the Dictionary
    • Python Dictionary in-built Functions
    • Dictionary len() Method
    • Variable Types
    • Python List cmp() Method
    • Dictionary Str(dict)
  11. Python Sets
    • How to create a set?
    • Iteration Over Sets
    • Python Set Methods
    • Python Set Operations
    • Union of sets
    • Built-in Functions with Set
  12. Python Functions
    • What is a function?
    • How to define and call a function in Python
    • Types of Functions
    • Significance of Indentation (Space) in Python
    • How Function Return Value?
    • Types of Arguments in Functions
    • Default Arguments and Non-Default Arguments
    • Keyword Argument and Non-keyword Arguments
    • Arbitrary Arguments
    • Rules to define a function in Python
    • Various Forms of Function Arguments
    • Scope and Lifetime of variables
    • Nested Functions
    • Anonymous Functions/Lambda functions
    • Passing functions to function
    • map(), filter(), reduce() functions
    • What is a Docstring?
  13. File Handling
    • File Objects
    • File Different Modes and Object Attributes
    • How to create a Text File and Append Data to a File and Read a File
    • Closing a file
    • Renaming and Deleting Files
    • Directories in Python
    • Working with CSV files and CSV Module
    • Handling IO Exceptions
  14. ADVANCED PYTHON:Python Exception Handling
    • Python Errors
    • Common RunTime Errors in PYTHON
    • Abnormal termination
    • Chain of importance of Exception
    • Exception Handling
    • Try … Except
    • Try ..Except .. else
    • Try … finally
    • Argument of an Exception
    • Python Custom Exceptions
  15. Python Date and Time
    • How to Use Date &DateTimeClass
    • How to Format Time Output
    • How to use Timedelta Objects
    • Calendar in Python
    • datetime classes in Python
    • How to Format Time Output?
    • The Time Module
    • Python Calendar Module
  16. Python Class and Objects
    • Introduction to OOPs Programming
    • Object Oriented Programming System
    • OOPS Principles
    • Define Classes
    • Creating Objects
    • Basic concept of Object and Classes
    • How to define Python classes
    • Python Namespace
    • Self-variable in python
    • Inheritance in Python
    • Python Multiple Inheritance
    • Overloading and Over Riding
  17. Python Regular Expressions
    • What is Regular Expression?
    • Regular Expression Syntax
    • Understanding Regular Expressions
    • Regular Expression Patterns
    • Literal characters
    • Repetition Cases
    • Example of w+ and ^ Expression
    • Example of \s expression in re.split function
    • Using regular expression methods
    • Using re.match()
    • Finding Pattern in Text (
    • Using re.findall for text
    • Python Flags
    • Methods of Regular Expressions
  18. Python Modules
    • os module
    • sys module
    • argparse module
    • csv module
    • cx_Oracle module
    • json module
    • requests module
  19. Data Analysis Use Case and Data Science Stepping Stone
    • Introduction to Numpy
    • Introduction to pandas
    • Introduction to matplotlib
What are the key features of this course ?

  • Online training by industry professional
  • Course Material
  • Collaboration tools for the seamless communication with trainer
  • Practise Envirnoment set-up
  • Multiple assignments and pracise on use cases
  1. What is the mode of accessing the training?
    • Training is provided only online mode.

  2. What if I miss to attend a class?
    • Every training session will be recorded and would be readily available at dispense.

  3. How do I interact with trainer for queries on subject?
    • We provide a collaboration and communication platform for interacting with the trainer real time.

  4. Will you be providing any course material for this training?
    • Yes, we do provide the course material.

  5. How do I practice, will there be any environment provided?
    • Yes, we help you set-up a cloud environment for you to practice

  6. What part of course is practical training?
    • Training would start with the concepts, then practice and theory go hand in hand and finally after course completion, participants will work on live projects.

Not Yet Satisfied with our Trend?