
Explore Python programming basics, download and install Python, and create starter projects with a straightforward setup.
Begin your Python programming basics journey with getting started ii, exploring integration concepts and identifying IDs mentioned in the caption.
Explore the Python program within an integrated development environment, using the console and color output to build helpful helpers.
Explore Python basics by writing and running a hello world program, building a simple application to understand scripts and basic software concepts.
Learn fundamental Python data types and variables, including integers, floats, and strings, how to assign values, and perform simple operations on these variables.
Explore data types conversion in Python programming basics, converting between integers, decimals, and strings, and learn how to cast values to appropriate types for arithmetic and string operations.
Learn how to use arithmetic operators in Python to perform basic calculations, with examples of subtraction and multiplication.
Explore comparison operators in Python, learning how equal, not equal, greater than, less than, and their inclusive forms yield true or false results during programming.
Learn python programming basics by exploring assignment operators, using the equals sign to assign values to variables, and running simple examples to see results.
Learn how to use boolean operators in Python to form true or false conditions. Evaluate expressions and understand logical outcomes in basic programming.
Explore Python programming basics by practicing decision making with operators like less than and greater than, and printing outcomes based on evaluated conditions.
Explore decision making by analyzing statements and cases presented in the lecture, referencing identity, verbal and radical notions, and the sequence of scenarios described.
Explore how to use loops in Python to automate counting tasks, printing numbers from 0 to 99 (or 1 to 100) without tedious repetition.
Explore loops in Python, cover conditions and increments, and avoid infinite loops as you learn practical basics for building a calculator.
Explore list basics in Python. Create variables to store values, access elements by zero-based indexing (first and second values), and work with ranges to view multiple items.
Develop a Python basics calculator by learning core programming concepts, printing results, and managing simple arithmetic tasks for fast, practical coding practice.
Practice basic Python programming concepts by adding values, using variables, and arranging simple steps to create a calculator.
Explore how to define and call functions in Python, print outputs, and perform subtraction to build a simple calculator.
Create and save Python code in a new folder, write small functions, and run simple programs to practice basic programming steps.
Define classes and create objects in Python, and apply these concepts to a basic calculator project.
Create your own calculator by building simple operations, getting user input, and running code to perform addition and subtraction.
Build a calculator in Python by taking user input, selecting operations such as addition, subtraction, multiplication, and division, and printing the result via a function.
Put everything together to build your own calculator using Python basics, combining variables, calculations, and practical coding steps for a functional tool.
Put together your own calculator in Python by creating an object and implementing subtraction and multiplication. Run the code to test how the calculator handles these operations.
Put together the concepts to create your own calculator using Python, following a guided walkthrough of a calculator program from the book and running the course modules.
Why learn Data Analysis and Data Science?
According to SAS, the five reasons are
1. Gain problem solving skills
The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.
2. High demand
Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.
3. Analytics is everywhere
Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.
4. It's only becoming more important
With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.
5. A range of related skills
The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths. Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.
The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.
This is the bite-size course to learn Python Programming. You will learn Python Programming very fast and You will create your own calculator very soon after learning the course.
You can look into the following courses to get SVBook Certified Data Miner using Python
SVBook Certified Data Miner using Python are given to people who have completed the following courses:
- Create Your Calculator: Learn Python Programming Basics Fast (Python Basics)
- Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)
- Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation)
- Machine Learning with Python (Modeling and Evaluation)
and passed a 50 questions Exam. The four courses are created to help learners understand about Python programming basics, then applied statistics (descriptive, inferential, regression analysis) and data visualizations (bar chart, pie chart, boxplot, scatterplot matrix, advanced visualizations with seaborn, and Plotly interactive charts ) with data processing basics to understand more about the data understanding and data preparation stage of IBM CRISP-DM model. The learner will then learn about machine learning and confusion matrix, which is the modeling and evaluation stages of the IBM CRISP-DM model. The learner will be able to do data mining projects after learning the courses.
Content
Getting Started
Hello World Software
Variables and Data Types
Data types Conversion
Arithmetic Operators
Comparison Operators
Assignment Operators
Boolean Operators
Decision Making I (IF statements)
Loop (while loop, for loop)
Lists
Functions
Modules
Object and Classes
Create Your own Calculator