Data Science 101: Methodology, Python, and Essential Math
What you'll learn
- Explain data science methodology, starting with business understanding and ending at deployment
- Identify the various elements of machine learning and natural language processing involved in building a simple Chatbot
- Indicate how to create and work with variables, data structures, looping structures, decision structures, and functions.
- Recall the various functionality of the two main data science libraries: Numpy and Pandas
- Solve a system of linear equations
- Define the idea of a vector space
- Recognize the proper probability model for your use case
- Compute a least squares solution via pseudoinverse
Requirements
- None. This course is ideal for a beginner to Data Science.
Description
Welcome! Nice to have you. I'm certain that by the end you will have learned a lot and earned a valuable skill. You can think of the course as compromising 3 parts, and I present the material in each part differently. For example, in the last section, the essential math for data science is presented almost entirely via whiteboard presentation.
The opening section of Data Science 101 examines common questions asked by passionate learners like you (i.e., what do data scientists actually do, what's the best language for data science, and addressing different terms (big data, data mining, and comparing terms like machine learning vs. deep learning).
Following that, you will explore data science methodology via a Healthcare Insurance case study. You will see the typical data science steps and techniques utilized by data professionals. You might be surprised to hear that other roles than data scientists do actually exist. Next, if machine learning and natural language processing are of interest, we will build a simple chatbot so you can get a clear sense of what is involved. One day you might be building such systems.
The following section is an introduction to Data Science in Python. You will have an opportunity to master python for data science as each section is followed by an assignment that allows you to practice your skills. By the end of the section, you will understand Python fundamentals, decision and looping structures, Python functions, how to work with nested data, and list comprehension. The final part will show you how to use the two most popular libraries for data science, Numpy, and Pandas.
The final section delves into essential math for data science. You will get the hang of linear algebra for data science, along with probability, and statistics. My goal for the linear algebra part was to introduce all necessary concepts and intuition so that you can gain an understanding of an often utilized technique for data fitting called least squares. I also wanted to spend a lot of time on probability, both classical and bayesian, as reasoning about problems is a much more difficult aspect of data science than simply running statistics.
So, don't wait, start Data Science 101 and develop modern-day skills. If you should not enjoy the course for any reason, Udemy offers a 30-day money-back guarantee.
Who this course is for:
- Beginners to Data Science or those interested in a data science career.
- Individuals considering switching fields.
- Individuals who want to get a big picture overview before focusing on specific Data Science topics.
- You are interested in an Introduction to data science in Python.
- You are interested in learning the essential math for data science.
Instructor
![Ermin Dedic](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.4 Instructor Rating
- 8,610 Reviews
- 56,017 Students
- 16 Courses
I have a passion for anything data, whether it is applying statistical methods to data more generally, or utilizing a data-driven approach in the Healthcare or Finance/Banking industries.
I studied Psychology for 6-years, including 2 years of Graduate school, where I was training to be a Child/School Psychologist. I was fortunate enough to have the opportunity to experience a blend of course work and clinical work but also recognize some of the problems facing the mental health system and graduate school system. While I am very interested in finding a solution for the latter, this is a long-term goal.
I did ultimately decide to voluntarily leave the Grad program, it was via academics that I fell in love with statistics and statistical software like SPSS/SAS.
Furthermore, it was my Graduate school experience that not only solidified my interest in teaching, it's where I received a lot of positive feedback on my ability to break down complex topics.
I enjoy receiving messages from students who have passed exams, obtained interviews, or gained employment, from taking one of my courses.
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Para meus alunos de língua portuguesa ...
Sou apaixonado por estatística, ciência de dados, programação orientada a objetos e psicologia / saúde mental. Eu desenvolvi um conhecimento em Programação e Estatística SAS através da minha escolaridade e auto-estudo. Também sou autodidata em programação orientada a objetos.
Eu sou um ex-aluno de graduação em psicologia educacional. Dois anos depois, decidi me retirar voluntariamente. Aprendi que o ambiente acadêmico tradicional e o ambiente clínico não eram o caminho adequado para promover mudanças em larga escala.
Ensinar é uma paixão há muito tempo. Criei meu primeiro curso de vídeo online em 2016 (um curso de Estatística). Foi um projeto de pura paixão. Como resultado de obter ótimos comentários, continuei! Atualmente, ensino os cursos de SAS, estatísticas e psicologia, mas também estou sempre aprendendo. Gosto de receber mensagens de alunos que passaram nos exames, obtiveram entrevistas ou obtiveram emprego ao fazer um de meus cursos.
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