Modules for Emerging Technical Trends

College will be having two certification Programs catering to Emerging Technical Trends. Enroll Today. Limited seats on First Come First Serve Basis

Artificial Intelligence & Data Science

Course curriculum

(1)Lesson 1 – Data Science Overview
(2)Lesson 2: Data Analytics Overview
(3)Lesson 3: Statistical Analysis and Business Applications
(4)Lesson 4: Python Environment Setup and Essentials
(5)Lesson 5: Mathematical Computing with Python (NumPy)
(6)Lesson 6 – Scientific computing with Python (Scipy)
(7)Lesson 7 – Data Manipulation with Pandas
(8)Lesson 8 – Machine Learning with Scikit–Learn
(9)Lesson 9 – Natural Language Processing with Scikit Learn
(10)Lesson 10 – Data Visualization in Python using matplotlib This lesson teaches you to visualize data in python using matplotlib and plot them.
(11)Lesson 11 – Web Scraping with BeautifulSoup
Lesson 12 – Python integration

Data analytics with Python & Advanced Python Module 2019-20 Even Sem

Data Analytics with Python    

With the advent of big data over the last decade and storage becoming cheaper, organizations are collecting a lot more data than before, making it imperative to derive insights from data and unlock the business value hidden in the data. Python is one of the most popular programming languages for data analysis. This course is designed for students who are familiar with a high level programming language like C, C++ or Java and would like to use Python for data analysis. The course provides a fast introduction to Python and then delves into Python data analysis

libraries – numpy, pandas, matplotlib and scikit-learn – and shows how to apply these libraries to practical dataanalytics problems

Chapter 1 – Introduction to Python

                     ● Python interpreter

                     ● Variables/Data Type

                     ● Loops/Conditionals

                     ● Functions

                     ● Data Structures – Lists and Maps

Chapter 2- Advanced Python

           ● Decorators

            ● Object Oriented Programming

            ● Functional Programming

           ● HTTP Protocol/Requests

Chapter  3 – numpy/pandas

● ndarray

● Vectorization

● Linear Algebra – Matrix operations

● Random number generation and sampling

● Series, DataFrame

● Summary Statistics

Chapter  4: Pandas/matplotlib

              ● Loading data – csv, sql

              ● Cleansing and Shaping data

              ● Grouping, filtering and joining

              ● Matplotlib – Figures and Subplots

              ● Matplotlib – Colors, Markers, Legends, Ticks, Lables

              ● Matplotlib – Area, Pie, Bar, Line, Density, Scatter plots

Chapter  5: Scikit-learn

                ● Supervised vs Unsupervised Learning

                ● Model fitting – cost function

                ● Classification

                ● Linear and Logistic Regression

For Further details meet Md Rehan (Dean Academics & Program Coordinator)

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