About this Specialization
The University of Michigan is introducing you top 5 courses of Data Science with Python Programming Language in this Applied Data Science with Python Specialization. This skills-based specialization is intended for you who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and network x to gain insight into their data.
Before going to the course details and syllabus, let’s see how data science is important and why Python is an important and most preferred language for Data Science.
Importance of Data Science
Data is one of the important features of every organization because it helps business leaders to make decisions based on facts, statistical numbers, and trends. Due to this growing scope of data, data science came into the picture which is a multidisciplinary field. It uses scientific approaches, procedures, algorithms, and frameworks to extract knowledge and insight from a huge amount of data.
The extracted data can be either structured or unstructured. Data science is a concept to bring together ideas, data examination, machine learning, and their related strategies. It comprehends and dissects genuine phenomena with data. Data science is an extension of various data analysis fields such as data mining, statistics, predictive analysis, and many more.
Moreover, it is a huge field that uses a lot of methods and concepts which belong to other fields like information science, statistics, mathematics, and computer science. Some of the techniques utilized in Data Science encompasses machine learning, visualization, pattern recognition, probability model, data engineering, and signal processing.
“Aim for simplicity in Data Science. Real creativity won’t make things more complex. Instead, it will simplify them.”
― Damian Duffy Mingle
“As usual in programming, if something is difficult for you to understand, it’s probably not a good idea.”
― Mark Lutz, Learning Python.
What you will learn from Applied Data Science with Python course?
In this course on applied data science with python, you will learn:
- Conduct an inferential statistical analysis.
- Discern whether a data visualization is good or bad.
- Enhance a data analysis with applied machine learning in python.
- Analyze the connectivity of a social network.
What skills you will gain from this course?
- Text Mining, Python Programming, Pandas, Matplotlib, and Numpy.
- Data Cleansing, Data Virtualization, and Data Visualization (DataViz).
- Applied machine learning in Python: Machine Learning (ML) and Machine Learning Algorithms.
- Scikit-Learn and Natural Language Toolkit (NLTK).
There are 5 courses in the Applied Data Science with Python Specialization program:
1. Introduction to Applied Data Science with Python (Content ratings 92%)
This course will introduce you 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.
- Fundamentals of Data Manipulation with Python- In this module, you will get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures.
- Basic Data Processing with Pandas- In this week of the course, you’ll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing pandas.
- More Data Processing with Pandas- In this module, you’ll deepen your understanding of the python pandas library by learning how to merge Data Frames, generate summary tables, group data into logical pieces, and manipulate dates.
- Answering Questions with Messy Data- This module of the course you’ll be introduced to a variety of statistical techniques such as distributions, sampling, and t-tests.
2. Applied Plotting, Charting, and Data Representation in Python. (94%)
In the following four modules, this course will introduce you to information visualization basics, with a focus on reporting and charting using the matplotlib library.
- Principles of Information Visualization- In this module, you will get an introduction to the principles of information visualization. You will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations.
- Basic Charting- In this module, you will delve into basic charting. For this week’s assignment, you will work with real-world CSV weather data.
- Charting Fundamentals. – In this module, you will explore charting fundamentals. For this week’s assignment, you will work to implement a new visualization technique based on academic research.
- Applied Visualizations- In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.
3. Applied Data Science with Python: Machine Learning in Python. (92%)
In this course on applied data science with python, there are four modules. The course will introduce you to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.
- Fundamentals of Applied Machine Learning in Python: Intro to SciKit Learn. This module introduces basic machine learning concepts, tasks, and workflow. We will see examples of classification problems based on the K-nearest neighbor method, and implemented using the scikit-learn library.
- Supervised Machine Learning – Part 1. This module delves into a wider variety of supervised learning methods for both classification and regression. We will be learning about the connection between model complexity and generalization performance, the importance of proper feature scaling,
- Evaluation- This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.
- Supervised Machine Learning – Part 2. This module covers more advanced supervised learning methods that include ensembles of trees, and neural networks with an optional summary on deep learning.
4. Applied Data Science with Python: Text Mining in Python. (92%)
This course will introduce you to the four modules of text mining and text manipulation basics. The course begins with an understanding of how text is handled by python. Followed by the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text.
- Working with Text in Python.
- Basic Natural Language Processing.
- Classification of Text.
- Topic Modeling.
5. Applied Social Network Analysis in python. (94%)
This course will introduce you 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.
- Why Study Networks and Basics on NetworkX- This Module introduces you to different types of networks in the real world and why you study them. You will learn about the basic elements of networks, as well as different types of networks.
- Network Connectivity- You will learn how to analyze the connectivity of a network based on measures of distance, reachability, and redundancy of paths between nodes
- Influence Measures and Network Centralization- This Module introduce you will explore ways of measuring the importance or centrality of a node in a network, using measures such as Degree, Closeness, and Betweenness centrality, Page Rank, and Hubs and Authorities.
- Network Evolution- In this Module you will explore the evolution of networks over time, including the different models that generate networks with realistic features. It consists of the Preferential Attachment Model and Small World Networks.
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- University of Michigan
- 3+ Months
- Paid Course (Paid certificate)
- Basic Scripting in Python
- Data Analysis Data Cleaning Data Manipulation Data Mining Data Science Data Science with 'Python' Data Visualization Machine learning Natural language processing