Data Analysis with Python
What is Data Analysis?
Data analysis or Data Analytics is a process of inspecting, cleansing, transforming, and modeling data. The goal is to discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Python Programming is essential for Data Analytics.
How Python helped for data analysis?
Python is a valuable part of the data analyst’s toolbox, as it’s tailor-made for carrying out repetitive tasks and data manipulation, and anyone who has worked with large amounts of data knows just how often repetition enters into it. By having a tool that handles the grunt work, the data analysts are free to handle the more interesting and rewarding parts of the job. Join Python Classes in Surat to learn data analysis course. Python libraries like NumPy, Pandas, and Matplotlib, help the data analyst carry out his or her functions
What you will learn in Data Analysis?
- What is Data Analytics?
- What is Data Analytics Course Syllabus?
- Data and Analysis in the Real World
- Analytical Tools
- Data Extraction Using SQL
- Real World Analytical Organizations
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Course Features
- Lectures 53
- Quizzes 0
- Duration 8 Weeks
- Skill level Beginner
- Language English and Hindi
- Students 152
- Assessments Yes
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Basic Python
- Introduction to Python
- Python – The Universal Language
- Installing Python
- Python – *Hello World*
- Using the Interpreter
- iPython – a better Python interpreter
- Types – Dynamic v/s Static Typing – tstrong v/s Weak Typing
- Numbers
- Strings
- Unicode
- Complex Types
- Operators – Operator Overloading
- Variables
- Scopping And Expressions
- Use of tabs and whitespaces as indent
- Conditionals – for…else
- The general syntax
- Default values for arguments
- Returning and receiving multiple values
- Variable number of arguments – args, kwargs
- Scope revisited
- Lists
- Tuples
- Maps (or Dictionaries)
- Sets
- Enums
- Looping Techniques
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Data Analysis with Python
- Installing Anaconda and Jupyter Notebook
- Jupyter Notebook overview
- Importing data from a csv, text file or another database
- Overview of various methods and attributes of Numpy
- Array
- Creating 1 dimensional arrays
- Creating 2 dimensional arrays
- Array indexing
- Accessing array elements
- Concatenating Numpy arrays
- Arithmetic operations with arrays
- Covariance
- Correlation
- Linear Regression
- Overview of various methods and attributes of Pandas
- Introduction
- Working with various Series attributes
- Introduction
- Pivoting
- Sorting
- Aggregation
- Descriptive statistical analysis with Pandas
- Introduction to Matplotlib
- Various Matplotlib methods
- Creating Line, Scatter, Bar, etc. charts
- Customising charts using: X and Y labels, Limits, Ticks, Legends
