Python with Artificial Intelligence and Data Science

Development with Python (Artificial Intelligence and Data Science)


Domains of Implementation and practice problems will be student driven and will be majorly based on Image / video classification, sentiment analysis, data mining and predictive models. During course students are encouraged to work on individual/ group projects for development of research and development skills.python with Ds

Mindscapes technologies offers the best Python for Data Science Course in Karachi. We provide this course with the advanced course module. We design this course in such a way which fit the people who are willing to learn. Even they don’t have any prerequisite knowledge on Python for Data Science.




Why Learn Python For Data Science?

Python is no-doubt the best-suited language for a data scientist. Few points which will help you understand why people go with Python for Data Science:

  • Python is a free, flexible and powerful open source language
  • Python cuts development time in half with its simple and easy to read syntax
  • With Python, you can perform data manipulation, analysis, and visualization
  • Python provides powerful libraries for Machine learning applications and other scientific computations

Introduction to Python for Data Science

Python was created by Guido Van Rossum in 1989. It is an interpreted language with dynamic semantics. It is free to access and run on all platforms.

Data scientists are a new breed of analytical data expert. They should have the technical skills to solve complex problems. They should have curiosity to explore what problems they need to solve.

The biggest benefits of getting this course is getting the fast employment. It will demonstrate that you can learn your job more quickly.

We should analyze the data and studied in any sector or field. First, we should understand the importance of data. It is the key to growth in any organization. When it comes to larger companies, we should analyze large amount of data. Data science involves automated methods. This is to analyze & understand the data. The demand for data science & analytics has a rise in the market. The data from mobile sensors and storage devices analysis is very popular. A data scientist should coordinate the varied levels of data.


Why you should learn Data Science with Python?

Python is a multi-paradigm or versatile programming language. It will consider as a sort of swiss knife for the coding world. This is because of the following supports. They are as follows.

  • Structured programming.
  • Object Oriented Programming
  • Functional programming patterns.

The versatility of Python will make it as the best suited for data scientists. Following are some of the advantages of python for data science. This will help you understand why you should learn data science with Python.

  • Python is a powerful open source programming language. It means that it is free to use. It will have all the properties that a programming language should have.
  • It is a versatile programming language. It supports Object-Oriented, Structured and functional programming patterns.
  • Python has some 72,000 libraries in the Python Package Index. It will aid in scientific calculations and machine learning applications.
  • Python sports an easy to understand and readable syntax. It will ensure the development time will cut into half. This is when compared with other programming languages.
  • Python enables you to perform data analysis, manipulation & visualization. These are very important in data science.

All these advantages of Python will make it ideal. So, data scientists use python as programming language.

Python for Data Science Course Content

  • What is AI
  • Basic Concepts of AI
  • Define Problem
  • Prepare Data.
  • Evaluate Algorithms.
  • Improve Results.
  • Present Results.
  • What is Docker
  • Development with Docker
  • Getting Started with Jupyter Notebook
  • Creating Your First Jupyter Notebook
  • Jupyter Notebook Modes
  • Useful Shortcut Keys
  • how to install libraries in jupyter
  • Opening Files
  • The os and os.path Modules
  • File Paths
  • Opening and Closing a File in Python
  • Text File Types
  • Raw File Types
  • Reading and Writing Opened Files
  • Iterating Over Each Line in the File
  • Appending to a File
  • Working With Two Files at the Same Time
  • One-dimensional Arrays
  • Multi-dimensional Arrays
  • Getting Basic Information about an Array
  • NumPy Arrays Compared to Python Lists
  • Universal Functions
  • Modifying Parts of an Array
  • Adding a Row Vector to All Rows
  • Series and DataFrames
  • Accessing Elements from a Series
  • Series Alignment
  • Comparing One Series with Another
  • Element-wise Operations
  • Creating a DataFrame from NumPy Array
  • Creating a DataFrame from Series
  • Creating a DataFrame from a CSVl
  • Getting Columns and Rows
  • Cleaning Data
  • Combining Row and Column Selection
  • Scalar Data: at[] and iat[]
  • Boolean Selection
  • Different types of basic Matplotlib charts
  • Labels, titles and window buttons
  • Legends
  • Bar Charts
  • Histograms
  • Scatter Plots
  • Stack Plots
  • Pie Chart
  • Loading data from a CSV
  • Loading data with NumPy
  • Section Outro
  • Subplot2grid *
  • Plotting Coordinates*
  • Basic 3D graph example using wire_frame
  • 3D scatter plots
  • 3D Bar Charts
  • Missing values
  • Polynomial features
  • Categorical features
  • Numerical features
  • Custom transformations
  • Feature scaling
  • Normalization
  • Environment Setup
  • Basic Functionality
  • Cluster
  • Constants
  • FFTpack
  • Integrate
  • Interpolate
  • Input and Output
  • Stats
  • Primer Concepts
  • Getting Started
  • Machine Learning
  • Data Preparation
  • Classification
  • Regression
  • Logic Programming
  • Clustering
  • Introduction to tensorflow
  • Introduction to kaggle
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Design and devlopment of a problem (Machine Learning)
  • Design and devlopment of a problem

Participant Profile

This course is valuable for programmers and software engineers who are interested in learning to develop Object oriented applications.

Difficulty Level


Applicable Job Roles

A lot of professionals including Software Engineers, Data Scientists, Research Analysts, Software Developers and data analysts have benefited from python courses.


No prior knowledge about python is required, but people are expected to have some basic knowledge about computers, some knowledge about one or two other programming languages such as Perl, PHP, Python or Java etc is preferred.


2 Months


2 Days a Week


The trainer is an IT professional working in IT- based Organization in Karachi, Pakistan.