| Published | 2022-05-02 |
| Platform | Udemy |
| Rating | 4.69 |
| Number of Reviews | 7 |
| Number of Students | 66 |
| Price | $24.99 |
| Subjects |
Learn about Data Science and Machine Learning with Python by Creating Super Fun Projects!
FAQ about Data Science:
What is Data Science?
Data science encapsulates the interdisciplinary activities required to create data-centric artifacts and applications that address specific scientific, socio-political, business, or other questions.
Let’s look at the constituent parts of this statement:
1. Data: Measurable units of information gathered or captured from activity of people, places and things.
2. Specific Questions: Seeking to understand a phenomenon, natural, social or other, can we formulate specific questions for which an answer posed in terms of patterns observed, tested and or modeled in data is appropriate.
3. Interdisciplinary Activities: Formulating a question, assessing the appropriateness of the data and findings used to find an answer require understanding of the specific subject area. Deciding on the appropriateness of models and inferences made from models based on the data at hand requires understanding of statistical and computational methods
Why Data Science?
The granularity, size and accessibility data, comprising both physical, social, commercial and political spheres has exploded in the last decade or more.
According to Hal Varian, Chief Economist at Google and I quote:
“I keep saying that the sexy job in the next 10 years will be statisticians and Data Scientist”
“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids.”
************ ************Course Organization **************************
Section 1: Setting up Anaconda and Editor/Libraries
Section 2: Learning about Data Science Lifecycle and Methodologies
Section 3: Learning about Data preprocessing: Cleaning, normalization, transformation of data
Section 4: Some machine learning models: Linear/Logistic Regression
Section 5: Project 1: Hotel Booking Prediction System
Section 6: Project 2: Natural Language Processing
Section 7: Project 3: Artificial Intelligence
Section 8: Farewell