Python & Data Science

Become a Python Developer

Python & Data Science Course in Lahore, Pakistan

  • Course Duration: 2 Months
  • Days: 3 days a week
  • Hours: 1.5
  • Status: Online & On-Campus
  • WhatsApp: 0302 895 7000
  • Website: www.kakti.pk
  • Address: 116-P, MM Alam Road, Mini Market, Gulberg II, Lahore
  • Google Map: Khurshid Akhtar Khan Training Institute

Course Outline

WHAT YOU’LL LEARN

Python & Data Science Course Content

Python, Data Science & Data Visualization

  • Introduction to Python Programming Language
  • Different Popular Python Usages
  • Data Analysis & Visualization
  • WHAT YOU’LL LEARNWeb & API Development with

Django and Flask

  • Software & Desktop Apps Development
  • Task Automation
  • Artificial Intelligence, Machine Learning AI/ML
  • Internet of Things IoT
  • Python 3x Setup & Installation
  • Python Programming in Windows Command Prompt
  • Basic DOS Commands
  • Python Console using python & ipython commands
  • Writing Code in Python Console
  • Functions print() & quit()
  • Writing Code in Python .py File using VS Code Editor
  • Run .py Code File
  • Python Package Manager PIP Install and Import Packages
  • Introduction to Anaconda 3x Data Science Platform
  • What Anaconda Offers
  • Anaconda Package Manager Conda Install and Import

Packages

  • PIP vs Conda
  • Install Packages by Reading a File pip/conda install -req.txt
  • Anaconda Prompt Console
  • Anaconda Offers In-Memory Environments
  • Benefits of Isolated OS Independent Environments
  • Create, List, Activate, Deactivate, Remove Environments
  • Jupyter Notebook Web Interface & Its Benefits
  • Create and Use .ipynb File
  • Variables & Data Types in Python
  • Global vs Local Variables
  • Global Variables Outside a Function Use Anywhere
  • Local Variables Inside a Function Use Within Function
  • Implicit vs Explicit
  • Text : str
  • Numeric : int, float, complex
  • Sequence : list, tuple, range
  • Mapping Dictionary : dict
  • Set : set, frozenset
  • Boolean : bool
  • Binary : bytes, bytearray, memoryview
  • Setting Data Type Implicitly assign value to a variable
  • Setting Data Type Explicitly use constructor functions str(),
  • int(), float(), complex(), list(), tuple(), range(), dict()
  • set(), frozenset(), bool(), bytes(), bytearray(),

memoryview()

  • Getting Data Type print(type(x))
  • Basic Data Storage Structures
  • Variable for Single Value, Arrays/Collections for Multiple

Values

  • Lists vs Tuples
  • Tuples Values must be Unique and Constant cannot be

modified

  • Python uses Indentation to Indicate a Block of Code
  • Spaces as You Want but atleast one, Once defined have to

follow

  • Tab is Best for Indentation
  • Comments in Python using Hash #
  • String Literal as Multiline Comment “”” Comment Inside“””
  • Boolean and Comparison Operators
  • Logical Operators
  • Arithmetic Operators
  • Assignment Operators
  • Identity Operators
  • Membership Operators
  • Bitwise Operators
  • Conditional Statements If Else and Elif
  • For and While Loops in Python
  • Python Normal Functions and Anonymous/Lambda

Functions

  • Python JSON (JavaScript Object Notation)
  • Exception Handling, RegEx
  • Python Classes and Objects
  • Inheritance in Python
  • Python User Input, Dates and String Formatting
  • Python Modules and

Scope

  • Python Iterators
  • Python File Handling Read, Write/Create, Delete Files
  • A Introduction to NumPy, Pandas, Plotly & Dash Packages
  • Dictionary Standard Advanced Multiple Values Storage

Structure

  • DataFrames Pandas Advanced Multiple Values Storage
  • Concept of Objects and Composed Objects
  • Arrays & DataFrames as Objects
  • Arrays & DataFrames Helper Functions
  • Attached as Composed Objects to Main Object
  • NumPy Numerical Python Crash Course
  • NumPy used for Working with Arrays
  • Play with Numbers Fast as Compared to Raw Python

Functions

  • NaN (not a number), INF (infinity)
  • Statistical Operations
  • Shape, Reshape, Ravel, Flatten
  • array, arrange, zeros, ones, linspace
  • Sequence, Repetitions and Random Numbers
  • Random Arrays randint, seed NumPy Random Package
  • randint().min() .max() .mean() .argmax() .argmin()

.reshape()

  • Indexing & Masking
  • Find Value by Indexing & Conditional Filters to Grab

Elements

  • Where File Read and Write
  • Concatenate and Sorting
  • Working with Dates
  • Pandas Advanced Data Manipulation Package Crash

Course

  • DataFrame and Series
  • File Reading and Writing
  • Import Pandas, Fetch DataSets in CSV File
  • Functions read_csv(), head() First 5 Rows, tail() Last 5

Rows

  • Info, Shape, Duplicated and Drop
  • Columns
  • NaN and Null Values
  • Imputation, Lambda Functions
  • Pivoting and Unpivoting Columns of DataSets
  • Reshape and Clean Data in Jupyter Notebook Better

Project 1 – COVID-19 Analytical Dashboard
Project 2 – Stock Exchange Analysis & Visualize
Project 3 – Sales Dashboard
Project 4 – Real-Time Dashboard & KPI
Project 5 – Deploy Dash App on Heroku Cloud

Our Flagship Offers

  • Fundamentals of Python Programming Language
  • Data Visualization and Analysis in Python
  • Anaconda, NumPy, Pandas, Plotly and Dash
  • Total 4 Data Analysis and Visualization Projects
  • 3 Projects are Using Stored DataSets
  • 1 Project is Real-Time
  • Covid-19, Sales and Stocks Analytical Dashboards
  • Deploy Dash App on Heroku Cloud

Who should attend?

  • Entrepreneurs
  • Housewives
  • Govt. Officials
  • Teachers
  • Students
  • All Professionals and Non-Professionals

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