Course Highlights

University of Michigan via Coursera
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★★★★★ (24,903 ratings)

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Data science is all about gathering data, analyzing it, and making decisions. Through data science, companies can take better decisions and can-do predictive analysis more efficiently. Today, data science is used in various industries such as banking, healthcare, consultancy, and manufacturing. This specialization is about Applied Data Science using Python. You can gain valuable insights into data sciences with python by enrolling in this specialization.

TTC Course Analysis:

Following are the results of comprehensive analysis of “Applied Data Science with Python” online specialization by our team of experts.

TTC Rating
9,379 Reviews

4.1

TakeThisCourse Sentiment Analysis Results:

In order to facilitate our learners with real user experience, we performed sentiment analysis and text mining techniques that generates following results:

TTC analyzed a total of 9,379 reviews for this online specialization.
The analysis indicates that around 81% reviews were positive while around 19% of reviews had negative sentiment.
University of Michigan online specialization received a total score of 4.1 out of 5 collectively across all courses, based on user opinions related to 4 effectiveness factors including content, engagement, quality practice and career benefit.

Online Course Effectiveness Score
Content	Engagement	Practice	Career Benefit
Excellent ★★★★★	Good ★★★★☆	Good ★★★★☆	Good ★★★★☆

Based on the learner’s review, we deduce the following points about this specialization:

Has an overall good rating of 4.5
very good content quality.
Covers all basic technical aspects.

What will you learn through this specialization?

There are mainly 5 courses in this specialization, and you will be getting the following concepts:

In the first course, you will be introduced to the basics of python programming environment and techniques.
The 2^nd course will introduce the learners to the basics of information visualization with a focus on charting and reporting with the help of matplotlib library.
3^rd course is about applied machine learning with a focus on techniques and methods
4^th course is about text mining and manipulation basics
Last but not the least, in the 5^th course you will learn network analysis with the help of tutorials using the NetworkX library.

Courses in Applied Data Science with Python Specialization

Introduction to Data Science in Python

★★★★★ 4.5
26,075 ratings
5,757 reviews

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. 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 should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

1. Introduction to Data Science in Python

TTC Sentiment Analysis for Introduction to Data Science in Python

Applied Plotting, Charting & Data Representation in Python (★★★★★) 4.5 | 6,055 ratings | 1,007 reviews)

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. The course will end with a discussion of other forms of structuring and visualizing data.

This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

2. Applied Plotting, Charting _ Data Representation in Python

TTC Sentiment Analysis for Data Representation in Python

Applied Machine Learning in Python (★★★★★ 4.6 | 8,099 ratings | 1,478 reviews)

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 will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, 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.

TTC Sentiment Analysis for Applied Machine Learning in Python

Applied Text Mining in Python (★★★★☆ 4.2 | 3,681 ratings | 706 reviews)

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. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

Applied Social Network Analysis in Python (★★★★★ 4.7 | 2,593 ratings | 431 reviews)

This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

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.

Pros and Cons:

Pros:

Acquire inferential statistical and analytical skills.
Can discern the quality of data visualization.
Professors have a profound and sound knowledge.

Cons:

Quality of the text mining concept was below average level.
Some learners found it tedious and frustrating.

What People Are Saying About this Professional Certificate:

Now in this section, we are going to talk about the feedback which users have given about the courses in this specialization. By reading the comments given by the users, it can become easy for any person to decide which course to enroll in.

Positive Feedback:

I took course 1 to get the basic knowledge to initiate my career in data science and I found it to be an excellent one. The professor had a firm grip over the concepts. (BK, ★★★★★)
This is the best technical course; I have taken so far. The assignments also help you a lot with your learning. I found it to be a practical course. (AV, ★★★★★)
I must say taking course 2 was the best thing I did, as it inspires you to create many attractive visualizations with a balanced representation. It also encourages you to explore API to get better results. (PV, ★★★★★)
Course number 3 was really interesting to study. All the quizzes and assignments or the hands-on gives you the chance to test your knowledge. High five to the mentor for teaching in such an eloquent way. (RS, ★★★★★)
This course was indeed a good one and all the concepts taught were really useful. It was beyond my expectations as I didn’t expect to learn this much from an online course. Now, I am really excited for the rest of the specialization. (DB, ★★★★★)
It was an overall very good specialization, especially the fourth course. It gave the basic idea about text and natural language processing kits. There was much in this course for self- exploration and to learn wider. (FA, ★★★★★)
The courses in this specialization were excellent. Video lectures were also of high quality, containing some realistic applications and problems. Exercises were reasonably challenging and fun to do at the same time. Highly recommend it. (CB, ★★★★★)
I really liked the fifth course in this specialization. It was an easy introductory course. Well-explained and nicely structured. I thoroughly enjoyed this course. (JL, ★★★★★).

Negative Feedback:

I took this specialization and passed the first four courses. I really liked the first three courses. I am not satisfied with the last two courses. The quality of fourth course on text mining was below the average standard. I would not recommend this course to anyone who wish to learn text mining with Python. There are many flaws in this course. (Y Dongquan S, ★☆☆☆☆)

Is this Specialization worth taking?

As we always say, the worth of any course or specialization highly depends on the requirements of learners. We have tried to cover all the positive and negative aspects about it along with a brief explanation about what this course has to offer to you. Now it’s up to you to decide to enroll in it now or consider it in near future.

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Alternatives:

IBM Data Science Professional Certificate

This specialization is more or less the same as of our main specialization. You can kickstart your career in data science by enrolling in this program if you don’t find the main specialization meeting all your requirements. In This certification, you will learn Python and SQL, analyze, and visualize data and will learn to build machine learning models. No degree or experience required for enrolling in this course. For an in-depth look into leveraging Python for data insights, don’t miss our Data Analysis with Python Course Review.

Professional Certificate in Data Science

Demand for skilled data science professionals is rising in industry and academics. If you wish to learn the basic skills to tackle real word data challenges, then you can enroll in this course. The idea of this Specialization is pretty much close to our main specialization program as the focus of both is on data sciences, but this course has main emphasis on data science. So, if you find this certification more relevant to your interest, you can enroll in this one.

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