Great Start for Coding – Python Crash Course for Beginners

This Python Crash Course is designed to foundations in order to write simple programs in Python . No previous knowledge to programming is needed. After Finishing this Course, you’ll understand the benefits of programming in IT roles; be able to write simple programs using Python.

What you will learn

Everything you need know about Python

• Python – a tool, not a reptile
• There is more than one Python
• Let’s start our Python adventure
• Your first program
• Python literals
• Operators – data manipulation tools
• Variables – data-shaped boxes
• How to talk to computer?
• Making decisions in Python
• Python’s loops
• Logic and bit operations in Python
• Lists – collections of data
• Sorting simple lists – the bubble sort algorithm
• Lists – some more details
• Lists in advanced applications
• Writing functions in Python
• How functions communicate with their environment?
• Returning a result from a function
• Scopes in Python
• Let’s make some fun… sorry, functions
• Tuples and dictionaries
• Using modules
• Some useful modules
• What is package?
• Errors – the programmer’s daily bread
• The anatomy of exception
• Some of the most useful exceptions
• Characters and strings vs. computers
• Python’s nature of strings
• String methods
• Strings in action

and many more things

This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts,

This is best course for any one who wants to start career in data science.

The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered.

Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

Data Visualization and Analysis with ML using Python, Numpy Pandas, Matplotlib, Seaborn, Plotly & Scikit Learn library

This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.