Path to Full Stack Data Science

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Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.

Full Stack Data Science has become one of the hottest industries in the field of computer science. Starting from traditional mathematics to advance concepts like data engineering, this industry demands a breadth of knowledge and expertise. Its demand has seen an exponential rise in online resources, books, and tutorials. For beginners, it's overwhelming, to say the least. Most of the time, beginners start with either a python course, a machine learning course, or some basic mathematics course. But many times, a large number of them do not know where to start. And with so many resources to go to, many of them keep scraping through resources. Moving between Udemy, edX, Coursera, and YouTube, many hours are lost.

Subject Matters involved with Data Science.

The goal of this article is not to list out the required syllabus but rather list out some of the prominent online resources for each subject area in the end-to-end Data Science domain. It will help beginners start their data science journey without wasting their precious time. I have tried to put down the resources in as much order as possible. But it might vary to a great extent depending upon the individual’s expertise and requirements. The focus of this article is solely the listing out of some of the thorough and in-depth online courses and tutorials available out there for domains comprising full-stack data science. I have tried to keep the list as short as possible so that it helps the starters get started with their learning without much selection.

Download PDF Data Science Books 

 

Resources for the following areas

 

• Mathematics — Linear Algebra, Calculus, Probability, Statistics, and Convex Optimization
• Python Programming — Fundamentals, OOP Concepts, Algorithms, Data Structures, and Data Science Applications
• R Programming — Fundamentals, Data Science, and Web Applications
• Core DS Concepts — Database Programming, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Data Visualization, Model Deployment, and Big Data
• C/C++ Programming — Fundamentals, Problem Solving, OOP Concepts, Algorithms, and Data Structures
• Computer Science Fundamentals — Introduction, Algorithms, Data Structures, Discrete Mathematics, Operating System, Computer Architecture, Database Concepts, Git, and GitHub

Mathematics

 

Linear Algebra

 

1. Instructor: Grant Sanderson / Channel: 3Blue1Brown
   Course: Essence of Linear Algebra
2. Instructor: Prof. Gilbert Strang / MIT OpenCourseWare
   Course: Linear Algebra / Youtube
3. Instructor: Kaare Brandt Petersen & Michael Syskind Pedersen
   Book: Matrix Algebra

 

Calculus

 

1. Instructor: Grant Sanderson / Channel: 3Blue1Brown
   Course: Essence of Calculus
2. Instructor: Prof. David Jerison / MIT OpenCourseWare
   Course: Single Variable Calculus / YouTube
3. Instructor: Prof. Denis Auroux / MIT OpenCourseWare
   Course: Multi-Variable Calculus / YouTube

 

Probability & Statistics

 

1. Instructor: Khan Academy
   Course: Probability
2. Instructor: Khan Academy
   Course: Statistics
3. Instructor: Joshua Starmer
   Course: Statistics Fundamentals
4. Instructor: Prof. John Tsitsiklis / MIT OpenCourseWare
   Course: Probabilistic Methods
5. Instructor: Allen B. Downey
   Book: Think Stats

Note: Use this book after completing the fundamentals of python and statistics

 

Convex Optimization (Advanced Concept)

 

1. Instructor: Prof. Stephen Boyd / Stanford
   Course: Introduction to Convex Optimization

Python Programming

 

Python Fundamentals

 

1. Python For Everybody: Course/ Book / Web
2. Learn Python The Hard Way: Book
3. Think Python: Book
4. Python Programming by Krish Naik: Course
5. Complete Python Bootcamp: Course

 

Algorithms & OOP with Python

 

1. Problem Solving & OOP with Python: Course
2. Grokking Algorithms: Book
3. Automate the Boring Stuff with Python: Course
4. (Advanced) Social Network Analysis for Startups: Book

 

Data Science with Python

 

1. Python Data Science Handbook: Book
2. Python for Data Science: freecodecamp course
3. Introduction to Computational Thinking & Data Science: Course
4. Applied Data Science with Python: Course

R Programming

1. R for Data Science: Book
2. Hands-on Machine Learning with R: Book
3. Interactive Web Apps using R Shiny: Tutorial

Database Programming

1. Fundamentals of Database Systems: Book
2. SQL vs NoSQL| MySQL vs MongoDB: Tutorial/ Tutorial
3. Full Database Design Course: Tutorial
4. SQL using MySQL: Course
5. PostgreSQL: Course
6. PostgreSQL for Everybody: Course
7. SQLite with Python: Course
8. Popular Database: Tutorial

Data Visualization

1. Power BI Full Course by Edureka: Course
2. Power BI Full Course by Simplilearn: Course
3. Tableau Full Course by Edureka: Course
4. Tableau Full Course by Simplilearn: Course
5. Tableau Crash Course by freecodecamp.org: Course

Machine Learning

 

Beginner Courses

 

1. Instructor:  Andrew Ng
2. Instructor:  Abu Yaser Mustafa
3. Instructor: Krish Naik
4. AI Introduction: ai/ Edureka
5. Artificial Intelligence by MIT: Course

 

Applied Machine Learning Course with Python

 

1. Machine Learning A-Z: Course
2. Practical Machine Learning with Python: Course

 

Books for Hands-on Machine Learning

 

1. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow: Book
2. The 100 Page ML Book: Book
3. Learning from Data: Book

Deep Learning

 

Specialization Courses

 

1. Instructor:  Andrew Ng/ YouTube
2. Instructor: Krish Naik
3. Instructor: Yann Le’Cun
4. Instructor: MIT

 

Applied Deep Learning with Python & TensorFlow

 

1. Deep Learning A-Z: Hands-On Artificial Neural Networks: Course
2. TensorFlow Complete Course by freecodecamp.org: Course
3. AI TensorFlow Developer Professional Certificate: Course
4. TensorFlow Data & Deployment: Course

 

Books for Hands-on Deep Learning

 

1. Deep Learning Book: Book
2. Fundamentals of Deep Learning: Book

 

Natural Language Processing

 

1. NLP Specialization by deeplearning.ai: Course
2. NLP with Deep Learning by Stanford: Course/ YouTube
3. Complete NLP by Krish Naik: Course

 

Computer Vision

 

1. Convolutional Neural Networks for Visual Recognition: Course
2. Complete CV by Krish Naik: Course
3. Full OpenCV by freecodecamp.org: Course

 

Reinforcement Learning

 

1. Reinforcement Learning by DeepMind: Course
2. Reinforcement Learning by Stanford: Course
3. Reinforcement Learning by University of Alberta: Course

Web Development

1. Django Tutorial by Corey Schafer: Course
2. Django for Everybody: Course
3. Flask Tutorial by Corey Schafer: Course
4. Web Development by Traversy Media: Web Link/ YouTube
5. Full Stack Web Development Guide: Tutorial
6. Web Design for Everybody: Course
7. Web Applications for Everybody: Course

Git & Github

1. Crash course by freecodecamp.org: Course
2. Crash course by Traversy Media: Course
3. Full Course by Edureka: Course
4. Git Tutorial for Beginners by Mosh: Course
5. Git and Github tutorial by Amigoscode: Course

AWS

1. AWS Certifications: Tutorial
2. AWS Tutorial for Beginners: Course
3. AWS Basics for Beginners: Course
4. AWS Certified Cloud Practitioner Training: Course
5. AWS Certified Solutions Architect — Associate Training: Course
6. AWS Certified Developer — Associate Training: Course
7. AWS SysOps Administrator-Associate Training: Course

Model Deployment

1. Instructor: Krish Naik
2. Instructor: Daniel Bourke
3. Live End-to-End Model Deployment: Tutorial
4. Model Deployment using Amazon Sagemaker: Tutorial
5. Model Deployment using Azure: Tutorial

Big Data

1. Introduction to Big Data by CrashCourse: Tutorial
2. Introduction to Big Data by Edureka: Tutorial
3. Big Data Intro by Simplilearn: Tutorial
4. Big Data & Hadoop by Edureka: Course
5. Apache Spark by Edureka: Course

C/C++ Programming for Problem Solving

 

Tutorials & Courses

 

1. Full C Tutorial by Mike: Course
2. Full C++ Tutorial by Caleb Curry: Course
3. Full C++ Tutorial by Suldina Nurak: Course
4. C++ OOPS Concepts: Course
5. Problem Solving & OOP using C++: Course
6. Pointers in C++: Course
7. STL using C++: Course
8. Data Structure using C/C++: Course

 

Books

 

1. The C++ Programming Language by Bjarne Stroustrup: Book
2. The C Programming Language by Dennis Ritchie: Book

Algorithms & Data Structure

1. Introduction to Algorithms by MIT: Course
2. Design & Analysis of Algorithms by MIT: Course
3. Advanced Algorithms by MIT: Course
4. Competitive Programming Guide by GeeksforGeeks: Web Link
5. Introduction to Algorithms by Thomas H. Cormen: Book

Fundamentals of Computer Science

1. Missing Semester of Computer Science: Course
2. Computer System Architecture by CMU: Course
3. Computer System Architecture by MIT: Course
4. Operating System by Neso Academy: Course
5. Operating System by UC Berkely: Course
6. Basics of Software Engineering: Course

I tried to provide specific resources (courses/tutorials/books) that are in-depth, prominent on the web, and have proved to be quite beneficial to a large number of learners in the data science arena. I tried to be as specific as possible and listed those with which I have familiarity. It goes without saying that many great resources have also been left out. As such, this list should not be considered an expert guide by any means. Rather, it picks out some of the highlighted courses to make the learning journey easier for beginners. I will finish off by providing some of the topmost YouTube channels that have tons of learning materials and some pretty good guidance in regards to the subject matter.

Download Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Arthur Zhang - 2017

Link - https://fr.b-ok.africa/dl/3384776/ceb09c

#Python, #100DaysOfCode, #CodeNewbies, #WomenWhoCode, #DevOps, #code, #Coding, #LearnToCode, #DataAnalytics, #DataScience, #MachineLearning, #AI, #programming, 

Détails sur le produit

• Éditeur ‏ : ‎ CreateSpace Independent Publishing Platform (10 mars 2017)
• Langue ‏ : ‎ Anglais
• Broché ‏ : ‎ 280 pages
• ISBN-10 ‏ : ‎ 1544603975
• ISBN-13 ‏ : ‎ 978-1544603971
• Poids de l'article ‏ : ‎ 490 g
• Dimensions ‏ : ‎ 15.24 x 1.63 x 22.86 cm

Download Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 2nd Edition

 Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming

Link - https://fr.b-ok.africa/dl/5416472/bee132

#Python, #100DaysOfCode, #CodeNewbies, #WomenWhoCode, #DevOps, #code, #Coding, #LearnToCode, #DataAnalytics, #DataScience, #MachineLearning, #AI, #programming, 

The Golden Path to become A Full-Stack Data Scientist — Who is needed by in Real Industry.

All about Agile, Ansible, DevOps, Docker, EXIN, Git, ICT, Jenkins, Kubernetes, Puppet, Selenium, Python, etc

Why do you have to read this?

from glassdoor.com on 25th of March, 2019

The shortage of data scientists is becoming a serious constraint in some sectors. — Data Scientist: The Sexiest Job of the 21st Century from Havard Business Review

Nowadays Big Data is one of the most important key resources to get a competitive advantage in business, especially for IT companies. All of GAFA, accelerate their business by applying Data Science Technique. Let me explain a little bit more.

For instance, Google, Youtube uses Recommend Engine which will never let you go, because they suggest contents by completely following your taste. Amazon increases its Gross Merchandise Volume(GMS) by matching user and product very efficiently. Facebook shows Advertisements with higher CVR from extracting critical insights from your demographical and behavioral data. (I used their Ads in the previous job and was surprised at how effective it performed.)

Till now I’ve only mentioned about Internet Giants. However, according to Masayoshi Son who is Japanese CEO of SoftBank Vision Fund’s which is one of the biggest founds all over the world, and investments hover near $45 Billion dollars in 2018, left an insightful comment.

image from SoftBank’s unicorn hunter

The impact of Internet is somehow limited in particular domains or industries, like Advertisement, Retail (E-commerce), but AI is different. — Masoyoshi Son

“Next Big Waves” in other industries, which is applying data science methods into Unstructured Data like Image, Natural Language, Sounds, are coming up. I mean, for example, Mobility Industry you can see what Elon Mask does, Robotics you see what’s happening in Amazon’s warehouse, Finance, Healthcare area and so on.

If you are young and wanna be the biggest winner in your career, this Data Science or Machine Learning field could be the most possible choice.

Because from my marketing perspective, a quite simple demand-supply balance analysis, I think some special paid job title like professionals in the Finance field or classic Software Engineer, Business Development position with MBA grad is already taken by our elders. And those skills and knowledge are became commoditized. I mean these are very competitive.

Comparing to them, Data Science field is way messier and still in the mist. Then why not invest your career and passion, intelligence into this challenging field? Welcome to this fantastic ML technology with a full of hope!

In this article, I will focus on how to get a Data Scientist jobs or Machine Learning Engineer posts who will be needed by real industries, or in other words, who will pass the job hunting process with lesser pain. Let’s get started!

— — —

After you read this, you’ll get:

• The Shortest Path towards A Real Data Scientist
• The Best Learning Resource everyone trust in this area
• The Realistic Possibility of your career with ML

— — —

Menu

1. What is Data Science for Business?
2. Data scientist vs Machine Learning Engineer
3. Required Skill for A Full-Stuck Data Scientist
4. A Golden DS Learning path for a newbie
5. The Secret Bibles towards A Full-Stack Data Scientist

— — —

1. What is Data Science for Business?

from edureka!

Data Science itself is just a Method, Not a Goal in our business. Then definition of it should be explained in much simpler way. — an unknown data scientists

Data Science itself can never be a purpose, if it becomes, it means you already fail. Rather than that, I would say “We just became able to make some additional or novel values in the real business which we couldn’t 10 years before”.

I highly recommend listening to this podcast, SDS 131: The One Purpose to Data Science and The Truth about Analytics from SuperDataScience.

To define Data Science, I wanted to start from their goal and purpose. The fundamental goals of Data Science in business is pretty clear. I think it can be simply described as followings:

• To make more profit in your business by using data (as a result)
• To understand and satisfy your customers by efficiency, better matching
• To create a new business or startup by using machine learning

The background behind this Game Change

In addition, I think it’s very important to understand the reasons why the significance of Data Science is gradually recognized in recent business. It could be the following three:

• An explosion the Amount of Data by Internet and Smartphone
• Improvement of Computational power by GPU and TPU
• Deep Learning enables to Process Unstructured Data

OK, so now we can take the next step to understand how Data Science jobs are separated in real industries. Let’s figure out where to start your career depends on what you have right now and what you should have to get an ideal position for 5–10 years long-span career plan.

— — —

2. Data Scientist vs Machine Learning Engineer

from www.stoodnt.com

In2018 summer, I decided to build a Machine Learning related career from general Data Analyst job because I was pretty sure this technological innovation will exactly reproduce what we’ve seen a drastic change which was caused by internet and smartphone.

Suppose you already heard about the average salary of data scientist (117,000USD/year!) or understood enough the potential of ML innovation. Here, I only focus on talking to people who want to switch their expertise domain into ML(data science) by taking several years.

After I built my Data Science portfolio and started my job hunting I realized there are mainly two different job title which can work closely with machine learning technology:

One is Data Scientist. Another is Machine Learning Engineer.

As you can see above image, the difference between these two and classic Data Analyst job and Data Engineer(Big Data Engineer) job is relatively easy to describe because they’ve been already existing for more than 10 years and required skills are very clear.

However, I found the required skills and knowledge of Data Scientists and Machine Learning Engineer is quite duplicated.

The reason is simple. Since Machine Learning is the most impactful innovation in recent Data Science field, which even able to create new business and companies like Chatbot startup or Drone startup, the specialist of Machine Learning itself became an undoubtedly respectable job title.

On the other hand, nowadays we also cannot talk about Data Science without taking Machine Learning into account, Data Scientist also must know ML Theory as well. Because most of the companies are currently interested in to accelerate their business by using ML technology.

In fact, Data Scientists might not only have theoretical knowledge of Machine Learning but also, at least, be able to implement several Machine Learning Algorithm like SVM or Random Forest for classification task by using scikit-learn or build Neural Network by using Keras.

Because ironically or interestingly, unless you understand math and statistics deeply, if we want to understand ML theory and Deep learning, it’s an efficient or required way to write codes and implement it by using Tensorflow and scikit-learn or those high-level API.

2–1. The different Area of Expertise between DS and ML engineer

Related skills in DS field from edureka!

I know this highest rated answer for this question from Quora is not enough for you, to clarify your career in this field.

Finally, I found the wall which is unable to climb over between these two jobs in real industries. It was like this:

Professional Machine Learning Engineer can build an “end-to-end software product” which has machine learning algorithm as a part of them.

Professional Data Scientists can define “the problem which should be solved(or not)” with scalability by using by machine learning and “How” as well.

I hope you get some pictures of what I wanna say. Please don’t forget that the final goal and responsible mission of ML engineer are, I think, finalizing to build a moving software. More clearly said, unless you don’t have experience of backend software engineering, it seems hard to get a comprehensive ML engineering position which we can often find on JDs.

I wanted to tell the newbies from a non-engineering background, like data analyst, the reality is that some serious tech companies write neural network from scratch, I mean they even don’t rely on Keras or Tensorflow.

On the other hand, Data Scientist requires outstanding business understanding which is more vague and difficult to prove though (life is hard). But this is so important because in some case, a classical statistics method like Multiple regression analysis can be applied, ML is even not required. And also the application of ML in software requires an enormous amount of time and human resources. It’s necessary to calculate cost-performance balance before a huge investment decision making.

2–2. So is it impossible to get ML engineering job?

image from www.newtium.com

Well, for the enthusiastic ML fresher, I found a suitable position for us in ML projects. That is Data Preprocessing and Feature Engineering role.

In the real machine learning application, we repeat the following main process again and again until we acquire significant enough accuracy:

1. Data Preprocessing and Feature Engineering
2. Modeling ML/DL architecture and Training
3. Model Validation and Hyperparameter Tuning

Then finally ML engineers put this architecture into existing software or define whole architecture at the same time if you build a new product. In this process, it requires more development experiences and knowledge.

— — —

3. Required Skill for A Full-Stuck Data Scientist

What is the difference between Data Scientist and Data Engineer?

Conclusion: In my definition, A Full-Stack Data Scientist is a perfect mix of Data Scientist and Machine Learning Engineer, who can design and build “End-to-End Machine Learning Project and Software”. — by me

After I analyzed over 200 job description of the Machine Learning related position in Japan and India, Singapore, including companies like Google, Facebook, IBM. I found must-have skill towards A Full-Stack Data Scientist Career.

I will divide them into two different categories, one is a visible and more practical skill(more important in terms of getting a job!), another is theoretical and relatively difficult to prove.

You can use the following checklist before you start making a learning plan. It’s very flexible as well depends on JDs which you want to apply.

3–1. Practical Skill (Easy to prove and visualize)

• Basic Statistical Language: Python, R, Julia
• Data Science Library: Numpy, Pandas, Scipy, Seaborn
• ML/DL Library Experience: Tensorflow, Torch, scikit-learn
• Unstructured Data Processing: Image, Text, Sounds
• Relational Database: MySQL, PostgreSQL, SQLite
• Distributed File System: Hadoop, Spark, AWS, MongoDB
• Container-type virtual environment: Docker
• Version control system: GitHub
• Web Framework: Django, Flask, Ruby on Rails