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Python for Data Science Online Training

coursack
Coursack Tutor
Last Update March 12, 2024
0 already enrolled

About This Course

Welcome to our Coursack’s Python for Data Science Online Training! In this course, we will be diving into two key libraries for data manipulation and analysis: NumPy and Pandas. NumPy is a fundamental package for scientific computing with Python, providing efficient operations on arrays and matrices. Pandas is a powerful library that offers data structures and data analysis tools for handling and manipulating large datasets. With these tools, you’ll be equipped to work with a wide range of data and perform advanced operations with ease. Whether you’re a data scientist, machine learning engineer, or just someone looking to expand your Python skills, this training is for you. So let’s get started on our journey into Python for Data Science Online Training with NumPy and Pandas! with Coursack

Skills covered in the training

Python-Symbol
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Course Features

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Live Classes

60 Hours of Interactive Instructor led Live sessions.

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Coding Assignments

Coding Assignments after every topic for practice.

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Daily Class Recordings

Daily class recordings are shared after every class.

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FREE Demo Class

Register for FREE Demo class before enrollment.

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1:1 Classes

1:1 Classes are charged on hourly basis.

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Placement Assistance

Resume guidance & Interview preparation.

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Contact for Next batch on Python for Data Science Online Training.

Learning Objectives

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Course Syllabus

  • Introduction to basic Programming
  • Python evolution
  • Python 2 vs Python 3
  • Why Python?
  • Setting up Python on – Windows, Mac and other Platforms
  • Installing and setting up Jupiter notebook
  • Python IDLE
  • Programming basics
  • Compiling and Interpreting
  • Integer
  • Float
  • Complex
  • Boolean
  • String
  • ‘type’ Method
  • Variable Name Guidelines
  • Variable assignment
  • Print Statements
  • ‘input’ Method
  • Parsing Command Line Arguments
  • Command Line Arguments with argv
  • Formatting with Placeholders
  • Formatting with Multiple Placeholders
  • Formatting with .FORMAT() Method
  • Alignment, Padding and Precision with .FORMAT()
  • F-STRINGS
  • Arithmetic operators
  • Assignment operators
  • Conditional Operators – Logical and Comparison
  • Membership operators
  • Identity operators
  • Bitwise operators
  • Implicit Type Conversion
  • Explicit Type Conversion
  • Operators on Lists
  • Indexing and Slicing of Lists
  • List Methods
  • Append vs Extend
  • Nesting of Lists
  • List Comprehension
  • Lists using Random module
  • Indexing and Slicing of Tuples
  • Tuple Methods
  • Creating Dictionaries
  • Accessing Dictionary Data
  • Nesting of Dictionaries
  • Dictionary Methods
  • Dictionary Comprehension
  • Creation of Sets
  • Set Methods
  • If…else
  • Multiple Branches
  • While Loops
  • FOR LoopsBreak, Continue and Pass Keywords
  • Infinite Loops
  • For loops using Range function
  • For loops using enumerate function
  • String basics
  • Escape Sequences
  • String Manipulations – Slicing, Indexing and ‘replace’ Method
  • String Methods
  • Regular Expression Syntax
  • Searching for Patterns
  • Split with Regular Expressions
  • Finding All using Patterns
  • Repetition Syntax
  • Character sets
  • Exclusion
  • Character Range
  • Escape Code
  • Function Definition
  • Programs using Functions
  • Restricting input argument
  • Function Variable
  • Function Scope
  • Nested functions
  • Local and Non Local Variables
  • Global Keyword
  • Call by Value
  • Call by Reference
  • Different Function types
  • Recursive functions
  • Lambda expressions
  • Map Function
  • Filter
  • Reduce
  • Zip
  • Enumerate
  • All() and any()
  • Complex
  • Args and Kwargs
  • Try & Except
  • Finally
  • Error types
  • Module Basics
  • Import
  • Import vs from..import
  • Writing Modules
  • passing command line arguments
  • Reloading modules
  • Packages
  • Installing External Modules
  • Text File handling
  • Text File operations
  • Open, Read, Write, Append, Delete and Close methods
  • With keyword using Files
  • Redirecting stdout
  • StringIO objects
  • OOPs basics
  • Object
  • Classes
  • Self keyword
  • Attributes
  • Methods
  • Attributes types
  • Class method and Static method
  • property attribute
  • Inheritance
  • Polymorphism
  • Abstract class & Inheritance
  • Multiple Inheritance
  • Special methods
  • Method resolution order {MRO}
  • Super()
  • Decorators with python
  • Generators with python
  • Inner Functions
  • Introduction to Database
  • Introduction to SQLite
  • Creating a SQLite Database
  • Fetching Values from DB
  • DB functions
  • Cursor Functions
  • DB Exception handling
  • Date and Time
  • OS Module
  • JSON Module
  • SYS Module
  • Logging Module
  • Math Module
  • Random Module
  • Counter
  • OrderedDict
  • Defaultdict
  • Deque
  • Namedtuple
  • Array
  • Del command
  • Garbage collection
  • Logging
  • Introduction to NumPy
  • Installation
  • NumPy arrays vs Lists
  • Creating 1-D, 2-D and 3-D NumPy arrays
  • Indexing NumPy Arrays
  • Iterating over NumPy Arrays
  • Zeros and Ones of NumPy Array
  • Arithmetic Operations on NumPy Arrays
  • Matrix Multiplication
  • Statistics using NumPy Array
  • NumPy Methods
  • Slicing of NumPy Array
  • Conditions and Boolean Array
  • Joining Arrays
  • Splitting Arrays
  • Structured Arrays
  • Reading and Writing Array Data on Files

Pandas Data Structure

  • Declaring Series
  • Fetching Values and Indexes of Series
  • Selecting Elements from Series
  • Assigning Values to the Elements
  • Defining Series from NumPy Arrays and Other Series
  • Filtering Values
  • Evaluating Data in Series
  • NAN Values
  • Series as Dictionaries
  • Adding Two Series
  • Creating Dataframe using Lists
  • Column Labels
  • Creating Dataframe using NumPy Array
  • Creating Dataframe using Dictionary
  • Loading CSV File as a Dataframe
  • Changing the Index Column
  • Examining the Data
  • Statistical Summary
  • Row and Column Slicing
  • Boolean List
  • Filtering Rows
  • Adding and Deleting Rows and Columns
  • Adding Columns
  • Sorting values by Columns
  • Exporting and Saving Pandas Dataframe
  • Concatenating Data Frames
  • Introduction To Matplotlib
  • Plotting Line Plots
  • Plotting Multiple Line Plots
  • Plotting Histograms
  • Plotting Bar Charts
  • Plotting Pie Charts
  • Plotting Scatter Plots
  • Plotting Log Plots
  • Plotting Polar Plots
  • Handling Dates
  • Multiple subplots

Frequently Asked Questions

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All of our Training programs are Live Instructor led sessions conducted using the video conferencing tools like Zoom or Google Meet or any other meeting tool. 

All our sessions are recorded and shared with the students after the session. If you misses the session we encourage to go through the video recording and we will be able to help you to solve your questions.

Yes. We provide course completion certificate at the end of the program. Based on the performance in the assessment we will the graded.

100% Training fee is returned if you opt out of the training with in first 3 classes. No money will be returned post 3 classes.

We provide materials for Interview preparations, help with resume building and mock interview sessions.

We provide job marketing through partnered consultants for USA candidates who are on EAD, H1B, OPT Visa and GC.

We accept payments from all currencies. We support RazorPay and PayPal for International Payments and UPI/Bank Transfer for Domestic payment (INR).

Target Audience

  • Both IT and Non-IT people can enroll in this training.
python for data science

14,90022,500

34% off
Level
All Levels
Duration 60 hours
Language
English

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