Applied Data Science with Python
Data science is now playing more and more important role in the era of big data. The posted jobs are more than the applicants for data scientists' job in the current job market. One of important reason for that is most of data analysts can only use SAS to do data analysis in non-Hadoop environment and don't know how to use any open source tools (such as R, Python, Scala, etc.) to do analysis.
However, in Canada, open source tools get more and more popular across all industries, and will be over SAS quickly for data analysis in industry. For instance, 5 big Banks, Telecom and consulting companies are using Python, R or Scala to do big data analysis and modeling instead of using SAS in Hadoop. Skills in SAS are no longer attractive to employers as before, instead the open source tools become required and more attractive to employers. For the popularity of programming language, you can get details at http://www.tiobe.com/tiobe_index. In terms of TIOBE index report 2016, you can see Python moved up three spots within the last year to claim the number 5 spot. Meanwhile, R was ranked to 16, while SAS just dropped to number 21. Data Scientist is a brand new role vs. previous data analyst, and more opportunity and more promising and more paid. Capture this opportunity with good preparation, and don't miss it! Please go to any job search website, and try to search for Data Scientist to feel how the job is so hot and so demanding.
In order to fit the needs of data scientist job market, this course intends to provide required knowledge and skills to help data analyst optimize Data Science learning path to successfully transit into data scientist. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. The course will not only introduce you step-by-step to the process of installing the Python interpreter and data ingestion/wrangling, but also guide you from end-to-end to develop models with machine learning in Python.
The course is created around three themes designed to get you started and using Python for applied machine learning effectively and quickly. These three parts are as follows:
Lessons: Learn how data can be processed in Python (Python fundamental), and how machine learning project map onto Python and the best practice way of working through each task (Python advance – machine learning) through two sessions
Projects: Tie together all of the knowledge from the lessons by working through case study data processing and predictive modeling problems
Recipes: Apply machine learning with a catalog of standalone recipes in Python are provided as bonus, which you can copy-and-paste as a starting point for your new projects
Who is this course designed for:
You can seek and do data scientists job after mastering all you learnt from this course with confidence
Part 1: Introduction to Applied Data Science with Python Course
Part 2: Python Ecosystem for Machine Learning
Part 3: Python Programming Fundamentals
Part 4: IPython and Raw Python, NumPy, Pandas
Part 5: Matplotlib
Introducing the basics of matplotlib
Part 6: Python vs. SAS vs. SQL