Breaking into Data Science and Machine Learning with Python
Breaking into Data Science and Machine Learning with Python
If you have any quantitative, STEM or business background this course is for you to break into data science using Python
What you'll learn
- You will learn how to use Python for data science along with other libraries e.g. NumPy, Pandas, Matplotlib, Scikit-learn
- You will also learn: Exploratory data Analysis (EDA), Descriptive Analysis, Predictive Modeling using Machine Learning/Deep Learning
- Data Science Best Practices: How techniques and tools are being used by real Data Scientist in industries.
- Machine Learning Model: Linear and Logistics Regression, KNN, Naive Bayes, Multinomial Models
Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist.
If I can break into data science without a CS or Stat degree I think you can do it too!
In this class allow me sharing my journey towards data science and let me help you breaking into data science. Of course it is not fair to say that after taking one course you will be a data scientist. However we need to start some where. A good start and a good companion can take us further.
We will definitely discuss Python, Pandas, NumPy, Sk-learn and all other most popular libraries out there. In this course we will also try to de-mystify important complex concepts of machine learning. Most of the lectures will be accompanied by code and practical examples. I will also use “white board” to explain the concepts which cannot be explained otherwise. A good data scientist should use white board for ideation, problem solving. I also want to mention that this course is not designed towards explaining all the math needed to “practice” machine learning. Also, I will be continuously upgrading the contents of this course to make sure that all the latest tools and libraries are taught here. Stay tuned!
