The Best Applied Data Science online courses and tutorials for beginner to learn Applied Data Science in 2020.

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data.

Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.[3] It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.

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Top Applied Data Science Courses, Tutorials, Certifications list

  1. Applied Data Science

  2. Statistics & Applied Data Science - Business Data Analysis

  3. Applied Data Science with Python

  4. Applied Data Science with Python Specialization

  5. Machine Learning Entrepreneurship - Applied Data Science

1. Applied Data Science

Learn Applied Data Science from IBM. This is an action-packed specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. It appeals to anyone interested in pursuing a career in Data Science, ...

Course rating: 4.6 out of 5.0 ( 11,178 Ratings total)

In this course, you will :

  • You will learn Python - no prior programming knowledge necessary. You will then learn data visualization and data analysis.
  • Importing data-sets and cleaning the data. Data frame manipulation and summarizing the data.
  • Through the guided lectures, labs, and projects you will get hands-on experience tackling interesting data problems.
  • Solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.
  • Building machine learning Regression models and Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments.
  • You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

Upon completing all courses in the specialization and receiving the Specialization certificate, you will also receive an IBM Badge recognizing you as a Specialist in Applied Data Science. Finally, you will create a project to test your skills.

You can take Applied Data Science Certificate Course on Coursera .

2. Statistics & Applied Data Science - Business Data Analysis

Data Science Statistics : Data Science from Scratch for Beginners : Data Analysis Techniques, Method Course : Analytics

Course rating: 4.3 out of 5.0 ( 238 Ratings total)

In this course, you will :

  • Data analysis FAQ related to interview questions in your career ..
  • Four main things you should know them in data analysis and business analysis.

Course Contents :

  • Starting with FAQ related to interview questions in your career .
  • What is after data analysis ?
  • Descriptive statistics with collection of important quizzes and examples .
  • Normal distribution and standard normal  in details using Z table .
  • Sampling distribution with practical simulation apps and answering of important technical questions .
  • Confidence level and Confidence interval .
  • what is t distribution ? ( with examples )
  • What is DEGREE OF FREEDOM ? ( with examples )
  • One tail  and two tail in Confidence level .

You can take Statistics & Applied Data Science - Business Data Analysis Certificate Course on Udemy .

3. Applied Data Science with Python

Learn Applied Data Science with Python from University of Michigan. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language.

Course rating: 4.5 out of 5.0 ( 13,677 Ratings total)

In this course, you will :

  • Analyze the connectivity of a social network.
  • Conduct an inferential statistical analysis.
  • Discern whether a data visualization is good or bad.
  • Enhance a data analysis with applied machine learning.
  • Look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.
  • Students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively.

  • By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
  • This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library.
  • The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations.
  • The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework.
  • The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem.
  • This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.
  • The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial.
  • The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting).
  • The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text.
  • You will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
  • This course will introduce the learner to text mining and text manipulation basics.
  • The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text.

This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

You can take Applied Data Science with Python Certificate Course on Coursera .

4. Applied Data Science with Python Specialization

Master Class of Data Science with Case Studies using Python

Course rating: 4.7 out of 5.0 ( 74 Ratings total)

In this course you will learn:

  • path to become a data scientist
  • Problem Solving Approach
  • Make powerful analysis
  • Python Basic to Advance Concept
  • Python Libraries for Data Analysis such Numpy, Scipy, Pandas
  • Python Libraries for Data Visualization such Matplotlib, Seaborn, Plotlypy
  • Case Studies of Data Science with Coding

This Course Cover Topics such as Python basic and advance concepts,  data science libraries: Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, and introduction to data science and steps to start project in data science with the case studies of data science.

5. Machine Learning Entrepreneurship - Applied Data Science

Learn how to transform your ML ideas into interactive web applications and paywall subscription sites - Master Class

Course rating: 4.6 out of 5.0 ( 27 Ratings total)

In this course, you will :

  • Learn how to transform your ML ideas into fully interactive web applications and paywall subscription sites.
  • You will extend multiple Python machine learning ideas into fully interactive web applications, into a format that anybody anywhere can access as long as they have access to a web browser.
  • Your last project will be built around a professional paywall infrastructure so you can control and monetize how and whom can access it.

Whether you want to test out business ideas or share advanced and predictive analytics ideas with the world, the tools taught in this course will allow you to do that quickly and easily.

You can take Machine Learning Entrepreneurship - Applied Data Science Certificate Course on Udemy.

Wrapping Up

Thus, being aware of the fact that Data Science is such a demanding skill and people possessing expertise in this hot tech-domain are paid hefty amounts, Applied data science is a must have skill if you are an aspiring Data Scientist, Machine Learning Engineer or AI-Enthusiast!

The popularity is ever-increasing and thus, the above compiled list can take you long way in your career in the field of data science.


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