Data Science Using Python: Basic to Advance

Why Learn Data Science Using Python?

• Average salary base for a Data Scientist is ₹11,12500/- year in India
• Demand for Data Scientists Will Soar 47.1% by 2023
• Data Science Job Openings are expected to increase to 11.5 million jobs by 2026
• In India, the initial salary ranges between ₹ 4.5 Lakhs to ₹ 25.9 Lakhs

Salient Features

• 2 Hrs. / Week Live Instructor-Led online Sessions/Office Hours
• 3 Weeks of Project Work
• Active Q/A Forum
• Class Labs/Home Assignment (05 hrs./Week Learning Time)
• Industry and Academia Faculty
• Multiple Tools Covered
• Internal Competitions with Prizes
• Top Rated Advisors
• Industry Relevant Curriculum
• Doubt Clearing Sessions
• Hands-on Approach

Who Should do this Course?

• Fresher’s aspiring an exemplary career in Data Science
• College Students who wants to make their career in Data Science
• Non-IT Professionals desirous of making a career shift to the Data Science Industry

Course Description

The demand for skilled data scientists with a sound knowledge of programming languages is increasing exponentially. Python is one of the best data science programming languages. Apart from being easy to learn and implement, it has a wide range of applications in web and game development. This course teaches you programming concepts in Python and how they can be applied to manipulate and analyze data. It begins by introducing you to the Jupyter Notebook environment where you will be writing your code. Moving on, you will be taught how to use markdown cells to add images, text and links to your code. You will learn about variables, indentation and how to comment on your code for other programmers to understand. Furthermore, the course shows you how to work with different data types in Python such as lists, dictionaries, sets, and tuples as well  as how to use operators.

Next, the material explores the various decision-making statements in Python such as the ‘if statements’, ‘else statements’, ‘else-if statements’ as well as the ‘for loops’ and the ‘while loops’. Functions are a great way to save time and effort when writing computer programs because they are a set of instructions that can be used repeatedly to perform a specific task when called upon. In this course, you will create a function that converts the temperature scale from Celsius to Fahrenheit and then call it out to execution. Learners will gain an understanding of the difference between the print function and the return statements as well as the AWS Lambda keyword and its syntax. This Course also teaches you how to iterate and use the concept of nesting to access other functions and variables. You will learn about double indexing and how to select a specific item form a list or dictionary.

Python libraries play an important role in data science as they help eliminate the need for writing programs form scratch. The final part focuses on two key Python libraries –NumPy and Pandas –which ease sorting, manipulating and the analysis of a data set. The material shows you how to create and shape an array using NumPy and how to select items from a data set using the NumPy indexing and slicing techniques. The various functions of NumPy that serve different purposes will be reviewed. Then, the focus shifts to Pandas, where you will gain a thorough understanding of how to engineer and examine raw data. This course is your stepping stone to a career in data science, as it walks you through the basics of the application of Python to data science.

Learning Outcomes:

• Identify the basic data types in Python
• Use the arithmetic, logic, assignment and comparison operators
• Discuss the ‘for loop; and ‘while loop’ structures
• Create and apply functions to perform basic arithmetic operations
• Summarize the nested data and iteration concept in Python
• Create a NumPy array
• Describe NumPy indexing and slicing
• Select data from a data set using both the index-based and label-based method in Pandas