Fundamentals of Python for Data Mining

Description

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 course aims to cover the fundamentals of Python programming through real-world examples, followed by a touch on Data Science. Python programming basics such as variables, data types, if statements, loops, functions, modules, object,s and classes are very important and this course will try to teach these with a Console Calculator project. 

The course will then run through the popular data mining libraries like pandas, matplotlib, scipy, sklearn briefly on iris dataset to do data manipulation, data visualizations, data exploration with statistics (inferential and descriptives), model, and evaluation. 

You do not need to know to program for this course.  

This course is based on my ebooks at SVBook.

You can look at the following courses if you want to get SVBOOK Certified Data Miner using Python.

SVBook Certified Data Miner using Python is 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 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 are the modeling and evaluation stages of the IBM CRISP-DM model. Learners will be able to do data mining projects after learning the courses.

What You Will Learn!

• Python fundamentals, using Python libraries for data mining (pandas, scipy, matplotlib, ...)

Who Should Attend!

• Beginners

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