Data Science: Create Real World Projects
What you'll learn
- Learn to create real world Data science and Machine learning projects
- Learn about different Machine learning models and algorithms
- Learn about Data Science life cycle and apply methodologies for creating projects
- Learn about different domains of Data Science: Feature engineering, Feature transformation, and model Melection
- Learn about Natural Language Processing
- Learn about Artificial Intelligence and how to use it to solve the Data Science problems
Requirements
- Basic knowledge of Python programming is essential
- You should know topics of programming like functions, data structures and object oriented programming
Description
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
Who this course is for:
- This course is dedicated to those people who has some knowledge of programming and wants to learn about how to solve data science and machine learning problems
- This course is for them who wants to built career in the field of Data science and Machine Learning
- This course is for them who wants to learn data science in perfect way: by learning about feature engineering: data cleaning, transforming and using it to algorithms
- This course is for them who wants to learn Machine Learning and Artificial Intelligence by creating fun projects
Instructor
- 4.0 Instructor Rating
- 3,171 Reviews
- 182,965 Students
- 23 Courses
Sachin Kafle is a Python and Java developer, ethical hacker and social activist. His interest's lies in software development and integration practices in the areas of computation, quantitative fields of trade. His technological interests include Python, C, Java, C# programming. He has been involved in teaching since 2013.
Sachin is a engineer of Computer Science (B.E. Computer Science). He is also an instructor on his previously made some geek Youtube channel. He has been giving free classes mostly for students who have not been able to pay for expensive classes in his country.