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Why Data and Engineering with Data so important now

Data is a precious asset of any organization. It helps firms understand and enhance their processes, thereby saving time and money. Wastage of time and money, such as a terrible advertising decision, can deplete resources and severely impact a business. The efficient use of data enables businesses to reduce such wastage by analyzing different marketing channels’ performance and focusing on those offering the highest ROI. Thus, a company can generate more leads without increasing its advertising spend.

Data is meaningless until its conversion into valuable information. Data Science involves mining large datasets containing structured and unstructured data and identifying hidden patterns to extract actionable insights. The importance of Data Science lies in its innumerable uses that range from daily activities like asking Siri or Alexa for recommendations to more complex applications like operating a self-driving car.

The interdisciplinary field of Data Science encompasses Computer Science, Statistics, Inference, Machine Learning algorithms, Predictive Analysis, and new technologies.

SYLLABUS OUTLINE: Python

Module 1:

Imperative languages: Introduction to python programming language; syntax and constructs of a specific language. Variable Names, Data Type and Sizes, Constants, Declarations, Arithmetic Operators, Relational Operators, Logical Operators, Type Conversion, Increment Decrement Operators, Bitwise Operators, Assignment Operators and Expressions, Precedence and Order of Evaluation.

Module 2:

Control Flow: Statements and Blocks, If-elif-else statement, Loops: while, for. Concept of break, continue and pass statement.

Module 3:

Functions and Program Structure with discussion on standard library: Basics of functions, parameter passing and returning type, block structure, Initialization, Recursion and return types. Concept of module and packages.

Module 4:

Array and String: Basic concepts of tuple, list, dictionary and string. Linear and Binary Search, Selection and Bubble Sort.

Module 5:

File and Libraries: File handling, different modes, data extraction from url, data cleaning and preprocessing. Numpy, Pandas and Matplotlib packages and their basic applications on several types of data.

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